10.1.1.194.5576_

ACM SIGGRAPHCurriculum for Visualization

On-Line DocumentEditor: G. Domik ([email protected])

prepared by theACM SIGGRAPH Education Subcommittee

on"Education for Visualization"

Contributors

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Prof. Dr. Gitta DomikACM SIGGRAPH Curriculum for Visualization

Preface - Executive Summary

In this on-line document we make recommendations for the preparation of courses on"computer-generated visualizations meant to be viewed by a human" (such courses or curricula maybe entitled "scientific visualization", "information visualization" or similar). This document isaddressed to the teaching communities at universities, colleges or similar institutions for thepreparation of undergraduate, graduate, or post-graduate courses and/or curricula. Pllease send anycritizisms and comments to [email protected]!

This on-line document classifies and describes the topics essential to gain necessary skills to becomean expert in visualization ("computer-generated visualizations meant to be viewed by a human" willbe shortened to "visualization" in this document). The reader will also find a classification of skilllevels for visualization experts, and a matrix relating topics and skills. A number of educationalinstitutions in the US and in Europe (specifically Germany) have made their course outlines availableto the public. These course outlines contain information on the offering institution, educator and thetitle of the course, objectives and topics of course, lab setup, references and (if available) extendedinformation on student profiles, assignmens and more. If you want to make information on yourcourseavailable to others please send the necessary facts to [email protected].

Recommended use of this document: We encourage educators to expand individual themes toencompass particular objectives of their students and we encourage educators to collapse proposedthemes to fit visualization education into a curriculum that can not spare a full course on visualization.One or two weeks of well-prepared visualization topics as part of a course on high-performancecomputing or computer graphics will already expand the horizon of a student.

The newest update will always appear on http://www.uni-paderborn.de/cs/vis (http://www.uni-paderborn.de/cs/vis)

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ACM SIGGRAPH Curriculum for VisualizationProf. Dr. Gitta Domik

http://www.uni-paderborn.de/cs/vis

Core Topics

Core topics of visualization are organized into eight themes. These eight themes contain facts aboutthe most important aspects of visualization. We recommend to teach each of these themes. The levelof detail in which to present material for an individual theme is for the educator to decide.

Theme 1: Introduction to Visualization Theme 2: Data Theme 3: User and Tasks Theme 4: Mapping Theme 5: Representations Theme 6: Interaction Issues Theme 7: Concepts of the Visualization Process Theme 8: Systems and Tools

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Theme 1: Introduction to Visualization

This theme introduces students to "computer generated visualization meant to be viewed by a human",defines the term and explains its history. Some significant examples of visualization are used tomotivate students for the importance of knowledge on this topic.

(1.1) History:

Visualization has its ancestry in pictorial representations dating back to the origins of man.Pictographs, for whatever reasons, are human generated images. Through the centuries, wehave had human generated imagery of the sections of the world for travel and warfare;imagery of plans for architectural and novel devices (church designs, Da VinciŸs airplanes,the printing machine); images to enhance stories; images of crop rotations; and more. In thiscentury we have been able to use the computer to generate images supporting many of ourmodern endeavors. Computer generated data visualizations appeared in the late 40Ÿs whentables became much too large for a human to comprehend and manage. These visualizations,then called plots, were followed by the growth of computer graphics and systems thatpermitted the rapid, often interactive, generation of scientific data sets. Through a stronggovernment financial support scientific visualization prospered specifically after the mid´80s. A key event for the growth of scientific visualization was the appearance of a reportbased on an NSF sponsored workshop. [McCormick, B.H., DeFanti, T.A., and Brown M.D.(eds), 1987, Visualization in Scientific Computing. Computer Graphics21(6)]The focus in scientific visualization was on scientific data and modeling. The statisticalcommunity in the 60s and later also began using visualization to support its data exploration.In 1993, the appearance of a paper on the "information visualizer" (CACM, April 1993,Robertson et al.) was ground braking for a series of new developments in informationvisualization. Today we are presented with a broader context within which data visualizationfits. It encompasses scientific visualization, information visualization, databasevisualization, software visualization and all the domain related visualizations includingbiomedical and geospatial visualizations.

(1.2) Definitions:

The term "to visualize" in a general context [see The Oxford English Dictionary, 1989]means "to form a mental vision, image, or picture of (something not visible or present tosight, or of an abstraction); to make visible to the mind or imagination". In our contextvisualization means "a computer generated image or collection of images, possibly ordered,using a computer representation of data as its primary source and a human as its primarytarget." In [MCC87] scientific visualization has been defined as "Visualization is a methodof computing. It transforms the symbolic into the geometric, enabling researchers to observetheir simulations and computations. Visualization offers a method for seeing the unseen. Itenriches the process of scientific discovery and fosters profound and unexpected insights. Inmany fields it is already revolutionizing the way scientists do science." [FOL94] describethe process of visualization as "the binding (or mapping) of data to a representation that canbe perceived. The types of binding could be visual, auditory, tactile, etc. or a combination ofthese." "Topic" visualizations are in general subtopics of visualization, such as softwarevisualization. Some of the topic visualizations are also seen as certain view points at thewhole field of visualization by their experts, e.g. scientific visualization has been explainedas the scientific approach to creating useful visualizations; because each visualization

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process uses data of some sort as its primary source, data visualization has been used as themost general term for visualizations; finally information visualization is the preferred termfor many in the field to express the visual representation of any kind of information. Each ofthese "topic" visualizations can also be defined in a narrower sense, e.g. scientificvisualization as "visualization of scientific computing". Visualization reaches out to otherrelated fields, such as computer graphics, human-computer-interaction (HCI), perception. Itis important to keep clear similarities as well as borders between such related fields. Severalterms need to be well defined in order to be clear on the issues concerning visualization,foremost the terms visualization, individual "topic" visualizations, and data. Additionally,the expectations of related disciplines or subdisciplines and their relationship tovisualization should be clarified here.

(1.2.1) Definitions of visualization: Visualization means "a computer generated image orcollection of images, possibly ordered, using a computer representation of data as itsprimary source and a human as its primary target." Similarly this has been expressed by[FOL94] "A useful definition of visualization might be the binding (or mapping) of data to arepresentation that can be perceived. The types of binding could be visual, auditory, tactile,etc. or a combination of these." The term "to visualize" in a general context [see The OxfordEnglish Dictionary, 1989] means "to form a mental vision, image, or picture of (somethingnot visible or present to sight, or of an abstraction); to make visible to the mind orimagination". In the context of scientific visualization this term has been defined as"Visualization is a method of computing. It transforms the symbolic into the geometric,enabling researchers to observe their simulations and computations. Visualization offers amethod for seeing the unseen. It enriches the process of scientific discovery and fostersprofound and unexpected insights. In many fields it is already revolutionizing the wayscientists do science." [MCC87] Seven years later, Gershon expanded this definition to"Visualization is more than a method of computing. Visualization is the process oftransforming information into a visual form, enabling users to observe the information. Theresulting visual display enables the scientist or engineer to perceive visually features whichare hidden in the data but nevertheless are needed for data exploration and analysis." [GER94]

(1.2.2) Data means "data generated from mathematical models or computations and fromhuman and machine collection (e.g., sensors or point of sale systems)"

(1.2.3) We distinguish between computer representation of data (one or more internalrepresentations of data) and the (computer generated) visual representation of data.

(1.2.4) "Topic" visualizations are in general subtopics of visualization. Some of the topicvisualizations are also seen as certain view points at the whole field of visualization by theirexperts. E.g. scientific visualization has been explained as the scientific approach to creatinguseful visualizations; because each visualization process uses data of some sort as itsprimary source, data visualization has been used to encompass all topic visualizations;finally information visualization is the preferred term for many in the field to meanvisualization as ..The following topic visualizations have reached a stage of maturity:

(1.2.5) show the difference to other , related, disciplines.

(1.3) Sample Applications:

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Examples of the power of visualization to gain new insights into scientific data, tounderstand complex concepts, or to aid in the quest for information are plentiful. In thissection students "see" what visualization is about. Examples to recommend vary fromapplication fields such as Geophysics, e.g. "The Visualization of a Storm", to Sociology andPolitics, e.g. the visualization of census data; from Biochemistry. e.g. the visualization ofDNA, to Information Technology, e.g. the visualization of the web.

(1.3.1) Geophysics, e.g. "The Visualization of a Storm"

(1.3.2) Biochemistry, e.g. the visualization of DNA, molecules, or crystals

(1.3.3) Engineering and Physics, e.g. the visualization of a helicopter turbine, of a windtunnel, of the Big Bang, of Finite Elements Analysis computations

(1.3.4) Sociology and Politics, e.g. the visualization of census data, of vote distributions orthe spread of aids

(1.3.5) Mathematics, e.g. the visualization of klein knots or of splines

(1.3.6) Information Technology, e.g. the visualization of the web, the visualization ofretrieved documents from a query

(1.4) Impact of Future Technology Future technology,

such as future storage systems, display technology, or communication systems, will have astrong impact on visualization by making it an integral part of even more application areas.Knowledge about limitations of such technology and specifically of the human capacity willbe important to convey to the students.

(1.4.1) Next generation PCs

(1.4.2) Next generation storage systems

(1.4.3) Next generation display technologies

(1.4.4) Distributed computing

(1.4.5) Next generation communication systems

(1.4.6) Limitation of human capacity

(1.4.7) Next generation analytic tools

(1.4.8) Improved understanding of psychological and perceptual issues

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Theme 2: Data

Data encompasses "data generated from mathematical models or computations and from human andmachine collection (e.g., sensors or point of sale systems)." This theme relates data to the world beingmodeled, describes processing stages and explores useful models to characterize data. Examples ofcomplex data sets and their visual counterparts will make students sense the importance of knowledgegained from this chapter.

(2.1) Examples:

Examples of complex visualizations, their corresponding data sets, and the world beingmodeled by these data sets explain best the need to understand data together with theirhistory of origin, their intent and their characteristics.

(2.1.1) Proteins

(2.1.2) Satellite Data

(2.1.3) Software

(2.1.4) Web pages

(2.2) Relationship between data and the World being modeled:

First it is important to establish a valid and reliable relationship between the data on onehand and the world being modeled by these data on the other hand.

(2.2.1) establishing reliability

(2.2.2) establishing validity

(2.3) Processing the data:

Manipulating (part of) the data to be displayed, e.g. by normalizing or filtering, mayenhance the information content after visual display.

(2.3.1) normalizing

(2.3.2) data cleansing

(2.3.3) filtering

(2.4) Data Models:

More information than the computer representation of data is necessary to map them intouseful perceptible representations. Data are therefore characterized in often comprehensivedata models that describe geometry, topology, type, dependency or value of data and data elements.

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(2.4.1) value

(2.4.2) geometry

(2.4.3) topology

(2.4.4) metadata (schema)

(2.4.5) statistics

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Theme 3: User and Task

This theme explores limitations and abilities of the user as related to interpreting images. Theinfluence of the task(s) to be accomplished by the user during the interpretation of images is givenconsiderable emphasis.

(3.1) Human Performance Issues:

The following discussion is a broad overview of some of the basic characteristics of thehuman visual system and of perception and how it is thought to work. For amore in depthdiscussion, the reader is referred to such sources as a good Introductory Psychology textbook (e.g., Sternberg, 1995), a good text book on Perception (e.g.,Maitlin, 1997), a goodbook on Cognition (e.g., Reisberg, 1997), and Solso’s(1994) work exploring the intersectionof cognitive psychology and art.

(3.1.1) Perception: Perception is both a Top Down and a Bottom Up process involvingcontributions by the sensory systems, and the ways in which the neurons in the humannervous system interact with each other, as well as contributions from prior experience andlearned expectations.

(3.1.1.1) The human visual system. The primary subsystem of the nervous systemwhich will be of interest in dealing with visualizations is that of the human visual system.

(3.1.1.1.1) Biological. At the basic biological level there are neurons and the typeof stimuli which affect them to be considered.

(3.1.1.1.1.1) Effective sensory stimuli. When we are looking at the visualsystem it is clear that the physical stimulus of interest will be light and someaspect that physical phenomenon. It is important, however, to recognize thatthe effective stimulus which activate various components of the visual systemwill depend in part on the types of neurons being considered and the responseof those neurons to some physical characteristic of light. In general, sinceneurons adapt to continuous stimulation what is critical in their interactionwith the world is a change in stimulation, i.e., the effective sensory stimulusis a changing one.

(3.1.1.1.1.2) Types of neurons: Within the visual system there are severaltypes of neurons. In the eye alone there are rods, three different types ofcones, bipolar cells, horizontal cells, amacrine cells, and ganglion cells. Forall practical purposes, however, it is possible to treat all of these cells as a"black box system" and concern ourselves with the fact that there are twotypes of functions performed by these cells, some of them are receptor cellsand others are transmitter cells.

(3.1.1.1.1.2.1) Receptor cells. The receptors cells, which constitute thefirst layer of cells in the visual system, generally fall into two categories,the rods and the cones. Both of these types of cells convert light energyinto patterns of neural firings.

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(3.1.1.1.1.2.1.1) Rods. The rods are longer and narrower than conesand respond selectively only to changes in the intensity of light.These cells are so sensitive that they have been shown to respond tothe presence of only a single photon. Generally the are largenumbers of rods collecting light and providing stimulationeventually to a single ganglion cell, the type of cell which has afiber leaving the eye and becoming part of the optic nerve. Thisgenerally means that there are assemblies of cells in the eye whichare quite sensitive to the presence of light but which lack the abilityto discriminate finely where the boundaries of that light may belocated in the world.

(3.1.1.1.1.2.1.2) Cones. There are three populations of cones in theeye, those that are maximally sensitive to red light, those that aremaximally sensitive to green light, and those that are maximallysensitive to blue light. In fact wavelength characteristics of thesepopulations is known quite well from their absorption spectra, i.e.,the wavelengths of light which are not reflected back out of the eyeto the degree which other wavelengths are reflected. Generallythere are relatively few cones collecting light and providingstimulation eventually to a single ganglion cell. This generallymeans that there are assemblies of cells in the eye which are quitesensitive to colored edges, boundaries, etc.

(3.1.1.1.1.2.2) Transmitter cells. There are a variety of transmitter cellsin the eye. If the receptor cells are thought of as the first layer of cells,the bipolar and the ganglion cells are the second and third layers,respectively. The horizontal cells make connections among the receptorcells and modify the activity which takes place between the receptorsand the bipolar cells. The amacrine cells provide a similar horizontalorganization at the layer of the connections between the bipolar andganglion cells. It is the ganglion cells which contribute the fibers whichjoin together to form the optic nerve.

(3.1.1.1.2) Psychophysical. In looking at the relationship between the physicalcharacteristics of light and the psychological response to them it is clear that thereare three fundamental psychological dimensions of light which show acorrespondence with physical characteristics of light, hue saturation, andbrightness. The relationship between the physical and psychologicalcharacteristics of light is non-linear (e.g., it takes considerably less light energymake a light source "twice as bright" when that light source is in a darkened roomthat when that light source is in a brightly lit room).

(3.1.1.1.2.1) Hue. The psychological dimension of "hue" corresponds to whatis popularly referred to as the color of the light reflected by an object orproduced by alight source. The effective physical characteristic of the lightthat produces variations in hue is its wavelength. It is primarily on the basisof differences in wavelength that we assign color names across the visiblespectrum from Red (through Orange, Green, Blue, Indigo) to Violet.

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(3.1.1.1.2.2) Saturation. The saturation of a color corresponds to what therelative proportion of that particular wavelength reaching the eye from aparticular light source. For example, when most of light reflected from apatch of cloth is red the object is seen as having a very intense red color, thatis saturated. If the full range of wavelengths is present with red having only aslight predominance the color will appear quite pale and be less saturated.

(3.1.1.1.2.3) Brightness. The brightness of a light is the psychologicaldimension of light which corresponds to the intensity of the light, i.e., thenumber of photons falling on a particular region of the retina at a particularmoment in time.

(3.1.1.1.2.4) Preattentive visual.

(3.1.1.1.3) Visual Phenomena. When examining the visual system and this level ofanalysis there is an important process operating which is responsible for a numberof particularly critical visual phenomena.

(3.1.1.1.3.1) Lateral Inhibition. Atone level of analysis there are two basicprocess by which neurons influence each other: excitation and inhibition. Theprocess of excitation has occurred when an increase in the firing rate of oneneuron leads to an increase in the firing rate of another neuron upon which itis having itŸs effect. The process of inhibition has occurred when an increasein the firing rate of one neuron leads to a decrease in the firing rate of anotherneuron upon which it is having its effect. For example, when measured by theeffect produced upon a ganglion cell leaving the eye, stimulation of aparticular set of receptor cells can result in an increased firing rate in oneganglion cell and a reduced firing rate in some other ganglion cell since.Similarly, the output of a ganglion cell leaving the eye may be the net resultof the effects of several inputs, some of which are inhibitory, some of whichare excitatory, but all of which are the result of stimulation of multiplereceptor cells.

(3.1.1.1.3.2) Color Afterimages. It is this process of lateral inhibition whichis responsible for several visual effects, for example, color afterimages.Imagine a very simple network consisting of three cells, two of themreceptors and the third a transmitter. Let one of the receptors be maximallysensitive to red light and let the other be maximally sensitive to green.Furthermore, let the transmitter cell have anon-zero base firing rate evenwhen none of the receptors are being stimulated. So that this neural circuitbehaves in a way that is consistent with the eye, let both receptors beconnected to the transmitter cell, but let one of the receptors have anexcitatory effect and the other an inhibitory effect. For purposes ofdiscussion, assume that the red receptor has an excitatory effect whenstimulated and that the green receptor has an inhibitory effect whenstimulated. Furthermore, assuming equal amounts of red and green light, letthe about of excitation produced by the excitatory receptor be equal to theamount of inhibition produced by the inhibitory. Under these circumstancesstimulating the neural circuit with white light, which contains equal amountsof red and green light results in no change in the firing rate of the transmittercell. Now expose the receptors to a couple of mines of lots or red light. The

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red receptor gets a considerable work out, the green is receiving little or nostimulation, and the net result is that the ganglion cell firing rate goes upmarkedly as a result of the excitation effect produced by the red receptor.Meanwhile the red receptor has used up any reserve of stored chemicals andis firing only as rapidly as it can regenerate the chemicals it needs to producean electrical impulse. Next the receptors are exposed once again to whitelight, containing equal amounts of red and green light. The red receptorcontinues firing and producing its excitatory effect on the ganglion cell.However, the green receptor, being fresh and having a stored reserve of thechemicals it needs to produce electrical impulses and have an inhibitoryeffect on the ganglion cell, is fresher and fires more frequently than does thered receptor. Even though the light hitting the receptors is white, the redreceptor is not able to keep pace with the green receptor and there is moreinhibition being produced on the ganglion cell than there is excitation. Thenet effect is that the ganglion cell fires below its normal white light base rateand so sends a message to the brain that the incoming light is green.

(3.1.1.1.3.3) Opponent process color coding. The color afterimage effectdescribed above illustrates the opponent process nature of the coding of coloras it leaves the retina of the eye. The interconnections of the various cells andthe patterns of excitation and inhibition have created a neural circuits suchthat some ganglion cells behave in one way to red light and in the oppositeway to green light. That is the cells respond selectively and differentially tocomplementary colors. Similarly, other ganglion cells display an opponentprocess response to blue and yellow light, thereby illustrating some of thecomplexity of the connections and effects since there are no receptors in theeye that are maximally sensitive to yellow light, but yellow and blue arecomplementary colors and each produce the other as an afterimage underappropriate conditions.

(3.1.1.1.3.4) Edge Enhancement. This same process of lateral inhibitionoperates to sharpen edges and to increase the contrast between areas oflightness and darkness. When ganglion cells right along either edge of apatch of light are receiving inhibition and excitation from only from a portionof the field of receptors which normally influence them. Those on the darkside of the edge are receiving some inhibitory input without any excitatoryinput and those along the light side of the edge are receiving excitatory inputwithout some of the inhibitory input which would normally be present if thefull receptive field were being stimulated. Thus the ganglion cell on the darkside of the edge fires more slowly than do the rest of the ganglion cells whosereceptive fields are in darkness. The ganglion cell on the light side of theedge fires more rapidly than do the rest of the ganglion cells whose receptivefields are fully impacted by the light.

(3.1.1.1.3.5) Black, white and shades of gray. Many of these basic stimuliinvolve the shades of brightness of different objects in the world and thecontribution of the neural processes which extract information from the worldand process it.

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(3.1.1.2) The human memory system. While a more extensive discussion of memoryappears in section 6, it is helpful here to provide a brief review of the characteristics ofhuman memory since that memory is where the complex knowledge structures whichguide perception are stored. The major types of memory to be concerned with here areworking memory and long term memory.

(3.1.1.2.1) Working memory. Working memory is a limited capacity systemwhich can keep about 7 plus or minus two "chunks’ active for approximately 12seconds. After about 12 seconds these chunks have decayed and are no longeravailable for use in working memory. Decay in working memory can be preventedby rehearsal of the chunk, i.e., by simple repetition. Although new chunks can beadded to working memory, either by introduction from the perceptual world or byactivation of a node in long term memory, the capacity limitation will result insome chunks being lost if the number of chunks being juggled exceeds thecapacity of working memory. As a unit of storage the chunk is somewhatproblematic in that when one considers some basic physical units it turns out thatsometimes chunks contain only one of something and sometimes chunks containmuch more. For example, a chunk may be only a single one digit number from arandomly presented string. Or alternatively a chunk might well contain severaldigits, as in the case of moving 555-PUCK into working memory so that one cancall for hockey tickets. There are reasons to believe that any principle or basis forthe organization or structuring of long term memory can serve as the basis for chunking.

(3.1.1.2.2) Long term memory. In general long term memory refers to thosememories which may persist of many years but are not active in working memory.It is useful to think of the organization of long term memory as being somethinglike a complex semantic network of concepts and relationships among concepts.Activation of one node in the network can result in an increased likelihood ofactivation of nearby nodes. The range of things stored in long term memory isquite broad and includes such things as representations of events that have beenexperienced and a variety of knowledge structures that have been acquired byreading or thinking.

(3.1.1.3) Theories of Perception.

(3.1.1.3.1) Bottom up processing. The most widely accepted view of howperception works is that there are a number of basic physical stimuli whichprovide input to the sensory system. These include the various characteristics ofthe world which are responsible for color and for black, white, shades of gray.These physical characteristics of the world induce activity in the nervous systemand that activity is influenced and patterned by the interconnections of neuronsand processes (excitatory and inhibitory) which take place at those interconnections.

(3.1.1.3.1.1) Depth, motion, and others. In addition to the raw elements ofsensory stimulation created by the physical characteristics of patterns of lightenergy, there are consistent relationships between some of these patternswhich are either extracted directly or which are interpreted quite directly andimmediately on the basis of prior learning and experience with the world. Forexample, there are a variety of depth perception cues which come into play in

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making judgments of distance. Some of these, such as the relative size of twoobjects or whether or not one object appears to be interposed between theobserver and a second object do not depend upon having two eyes. Othercues such as those associated with binocular disparity result from the brainhaving to integrate two different sets of information from the optic nerve.

(3.1.1.3.1.2) Pattern recognition. Perceived forms fall into a variety ofpatterns. Letters, faces, and visual relationships all constitute examples ofpatterns are easily recognized on the basis of experience. Some of theinformation used to recognize patterns is directly contained in the patterns ofsensory input. However, there are a number of convincing demonstrationswhich show that the recognition of patterns is also guided by one’s priorexperiences and expectations.

(3.1.1.3.2) Top down processing. Patterns, people, and other

(3.1.1.4) Perceptual Phenomena. There are a wide variety of perceptual phenomenawhich operate to determine an observer’s interpretation of a visual scene. These includesuch things as the factors described by the Gestalt Psychologists, the Gestalt Laws ofperception, the movement of objects, either real or apparent, and various types ofperceptual constancies.

(3.1.1.4.1) Gestalt laws of perception. The Gestalt Psychologists identified severallaws of perception which could be determined from the behavior of observers.These laws are: figure ground perception, proximity, similarity, closure,continuity, and symmetry. In the case of figure ground perception, as in the classicexample of the vase and faces, one tends to see either the vase or the faces as thefigure with the other as background. The law of proximity describes theobservation that humans tend to see objects as a group when they are physicallyclose to each other. The law of similarity describes the observation that similarobjects tend to be grouped with each other. The laws of continuity and closuredescribe the observation that humans tend to perceive as a single object thingswhich have smoothly flowing forms rather than disrupted ones and that almostcomplete objects tend to be seen as being complete. The law of symmetrydescribes the observation that human tend to perceive some mirror image forms asbeing part of a whole object.

(3.1.1.4.2) Integration of images across time. The perception of motion, whetheractual or induced, illustrates that the visual nervous system tends to integrateinformation across time. Movies or cartoons, which make use of the principle ofstroboscopic motion, are actually composed of a series of still pictures (frames)which are shown at a rate of about 24 frames per second. The eye, however, seesthe appearance of movement rather than a series of still pictures. There are somewho have argued that all motion perception is actually the result of a similarphenomenon that results from the way in which the eyes interact with thecontinuously varying world.

(3.1.1.4.3) Perceptual constancies. A perceptual constancy exists when there is anunnoticed discrepancy of a particular kind between the stimulus provided by theworld and the observers interpretation. In the case of size constancy, an observerwho knows the individual in question will attribute the same height to another

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person regardless of whether or not that person is standing 5 feet away or 200 feetaway. Similarly know objects tend to be thought of as having the same shaperegardless of the angle from which they are viewed. In the case of color constancy,despite changes in the hue of light shining on known objects the observercontinues to perceive the color as being the same as when the objects areilluminated with white light.

(3.1.1.5) Avoiding Traps

(3.1.1.5.1) Color blindness. Certain types of color blindness result from adeficiency in one or more populations of cones or the chemical processes whichmake them work. Any visualization which codes information which requires theability to discriminate various colors from one another may very well contribute tocreating a new category of disadvantaged users. Statistically, males are morelikely to be represented in this group than females.

(3.1.1.5.2) Adhere to the conventional meanings of colors. While this may seemlike a trivially obvious recommendation, there are many instances where it hasbeen ignored. For example, in most western societies red lights are associated withdanger, green with safety, and yellow indicates a situation in which caution shouldbe exercised. None the less, it is not unheard of for a blue light to be used to signalthat a particular subsystem is over heating and needs cooled off. While this mayseem reasonable in that blue is normally a color associated with coolness, flashingyellow is the color typically associated with a warning or cautionary situation.

(3.1.1.5.3) Some other guidelines for the use of colors (from a presentation at theDelaware Valley Chapter of the Human Factors and Ergonomics Society,Principles of Color and Colorimetry by William A. Breitmaier, 30 Apr.,1997).

(3.1.1.5.3.1) Use white or green letters on a black background.

(3.1.1.5.3.2) Use black letters on a white or desaturated cyan background.

(3.1.1.5.3.3) Do not use more than six colors for information coding and nomore than four colors for text.

(3.1.1.5.3.4) Do not use pure blue for fine detail or small objects.

(3.1.1.5.3.5) Unless you want to show depth, avoid using saturated red andblue together. These colors cause chromosteropsis.

(3.1.1.5.3.6) Use complementary colors for maximum color contrast.

(3.1.1.5.3.7) Some color "meanings" change with context. Most people arefamiliar with the fact that the colors used for letters and background caninfluence the difficulty with which text is read. There are also cases wherethe luminance of colors in a display can make the interpretation of what isseen in the display more or less difficult. (de Weert, 1988, has provided some examples).

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(3.1.1.6) Contributions of senses other than vision

(3.1.1.6.1) Sound

(3.1.2) Design Issues

(3.1.2.1) Visual context (scale bars, annotations, ...)

(3.2) Visualization and task goals.

In developing a visualization and the mechanisms for interaction with that visualization, it isimportant to remember that the user is interested in solving some particular problem in aparticular problem domain. The focus of a user on a particular domain of interest whilederiving meaning from a visual representation is called his/her visualization or task goal.Both the visualization and the tools available to work with it must be suited to the user’stask. To convince students of the importance to understand goals when mapping from datato visual representations, examples make visible the vast difference of effective versusineffective representations. While visualization and task goals are application dependent, afeeling for possible categories of tasks, such as comparing, identifying, associating, orcorrelating, need to be conveyed to students. Finally, procedures to evaluate progress on theinterpretation of an image by a human need to be introduced.

(3.2.1) An example. Imagine a high-end visualization program. Within the visualizationwindow a 3D object is represented, e.g., a complex surface in grid mode with portions of thesurface wrapping around, intersecting with, and concealing other portions of the surface. Inthe lower region of the scroll bar on the right hand side of the window there is a rotatorwheel. The user can "grasp" the wheel with the pointer and move either upwards ordownwards to signal the computer to rotate the visual object around its horizontal axis. Inthe scroll bar across the bottom of the window is another wheel controlling rotation aboutthe vertical axis. Each wheel is carefully designed to recreate the appearance of physicalrotator wheels, even to creating the visual effect of having the top and bottom (or right andleft) edges of the wheel appear to recede behind the plane of the scroll bar. The objects areclearly rotator wheels and their intended function is obvious even to an inexperienced user.Now consider the following scenario. Assume the 3D representation is computationallyintensive and that the user wants to be able to discriminate an intersection or some internalaspect of the structure which is partially concealed by outer portions of the surface. There isa time lag between moving the rotator wheel and when there drawn figure appears on thescreen. Further, there is no visible cue by which the computer signals the user, "IŸmprocessing." Uncertain of the degree of rotation effected by his first attempt to rotate the 3Dobject, and seeing no visible result of having moved the rotator wheel, the user "grasps" thewheel and tries again. Naturally this action aborts the earlier processing and tells themachine to compute the new position of the object. Still having had no feedback after asecond and third attempt to rotate the figure, the user next tests the horizontal rotator wheel.Again there appears to be no direct result. (Meanwhile the computer has been busily andpatiently re-computing a new position of the 3D object each time it has been instructed to doso.) Finally, the user pauses in his manipulation of rotator wheels to try and determine whathe is doing wrong. Suddenly, with no apparent transition, the 3Dobject appears in its mostrecently computed position. While it is true that a really nice interface on an under poweredmachine wonŸt serve the userŸs needs, a bigger, faster machine wonŸt solve the realproblem here. The userŸs frustration is not determined solely by the fact that the machinetakes time to compute each new view of the 3D object. Within limits, time is a minor irritant

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compared to the fact that the user is unable to see what he wants to see in the 3Drepresentation. The control afforded by the rotator wheel is not fine enough to suit his needs.There is no simple and direct way for him to calibrate his movements to results. Too manycognitive operations intervene between views.

(3.2.2) Effect of problem representation upon ease of solution. Just as the flow ofinteraction with a visual representation can be disrupted by failing to provide the interactiontools needed to support the user’s goals, so to the representation itself can make a differencein the ease with which the user can make use of the data or information being visualized.Even though this is ultimately the reason for the existence of visualizations, it is sometimepossible to forget that not all representations are created equal and that some representationsare better than others. "Better," however, needs to be at least partially defined in terms of theuser’s goals and task-oriented needs.

(3.2.3) Task Analysis and problem need (i.e., what are the demands of the task).

(3.2.3.1) Focus on Cognitive Aspects of user needs and goals. The importance offocusing on the cognitive aspects of the user’s task oriented needs and goals is arecurring theme in the literature of human-computer interaction (See, for exampleNorman, 1993; and Preece, Rogers, Sharp, Benyon, Holland & Carey, 1994),humanfactors and ergonomics (See for example, Kirwan & Ainsworth,1992; Rasmussen,1986; and Reason, 1990), and a variety of design disciplines (See for example, Cross,1984; Dreyfuss, 1955; Heskett, 1980). Indeed, this concern for the user’s task can beseen as the basic idea behind many of the recommendations made by Tufte (1983,1990, 1997).

(3.2.3.2) Different types of user tasks. User tasks may be understood as a generalcriteria to fulfill (e.g. "granting exploration of data") or as a very specific assignment(e.g." is the flow of water symmetrical to any of the axes"), and my be statedindependent or dependent of problem domains. Examples of general and domainindependent tasks are:

(3.2.3.2.1) Exploration: What is out there? What if a certain parameter changed?

(3.2.3.2.2) Confirmation: Is there evidence? How much evidence is there?

(3.2.3.2.3) Presentation: Is the point to be made clear? Is the point to be made simple?

(3.2.3.2.4) Time Critical Decision Making. Can a decision be reached within theallowed time? (e.g. cockpit displays, nuclear plan emergency, real time reactive systems).

(3.3) Evaluation.

There is need to assess the quality of the visualization at least in dependency of user andtask. There are two types of evaluation-formative and summative. Summative evaluation isthe competitive evaluation conducted when two or more alternatives are compared in anattempt to determine which is the better alternative. For example, a summative evaluation ofa graph might involve having some students look at a non-visual presentation of data andsome at the visual presentation in order to assess which is most effective. Formativeevaluation is the type of evaluation conducted when one is attempting to determine whether

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or not the graph contains all information and whether or not it is ready for the summativeevaluation. For a further discussion of formative and summative evaluation and an extendedexample of how a variety of evaluation procedures were put top practical use in designingand interactive computing system, see Hewett (1986). Evaluation involves at least thefollowing steps:

(3.3.1) Establishing a goal statement which can be assessed. Effectively, the most criticalpart of formative evaluation is that one must have one or more clear cut goals which havebeen set as a basis for guiding the design and construction of the program or artifact.

(3.3.2) Determining progress towards that goal. The evaluation then involves determiningthe degree to which one is moving towards either the original goal or a towards a new goalwhich has been established at the result of the type of clarification that results from learningmore about what one is doing in dealing with unstructured problems. One of the benefits offrequent periodic formative evaluation is that it helps to conserve resources by preventingone from drifting too far off course.

(3.3.3) Some procedures for formative evaluation. There are a variety of techniques fordoing a formative evaluation and some of these are discussed below in section 6. In thecontext of working with users from one’s target population these basic techniques aretypically the more open ended ones where one leaves room for the unexpected to happen.That is one observes users in some way and listens to them describe or talk about thingswhich they are doing and/or thinking. Often these open ended procedures will reveal thenature of user’s underlying confusion about the meaning or interpretation of a visualization.furthermore, they will often result in potential useful suggestions about how to do thingsmore clearly or simply.

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Theme 4: Mapping Process

This theme discusses a central part of the visualization process, namely the mapping of data to arepresentation that can be (visually) perceived. Different strategies of the mapping process (such as byRenaissance teams, top-down or bottom-up methods) are to be explained. Possible difficulties, such asartifacts, are emphasized. Design factors, such as annotations or scale bars, are necessary to providevisual context.

(4.1) Models of mapping strategies:

This chapter explains various approaches used to bind images to data.

(4.1.1) Blind matching procedure: The "blind matching procedure" demands apreprocessing stage of data and a set of visual attributes and graphics primitives. First thedata is separated into individual data elements that are subsequently matched to visualelements (graphics primitives and visual attributes) to build up one or more images.Matching between data elements and visual elements are continued until the user is satisfiedwith the results.

(4.1.2) Renaissance teams: Renaissance teams bring together experts from variousdisciplines, such as from computer graphics, art and design, and the sciences. Matchingbetween data elements and visual elements is done methodically utilizing the expertise ofteam members.

(4.1.3) Top down versus bottom up strategies: Bottom up strategies match data elementsto visual elements in order to create one or more images. Such generated images need to betested for their value to the user (e.g. coherency, expressiveness, effectiveness). Top downstrategies start out with coherent, easy to interpret images and match their visual elements todata elements.

(4.1.4) Generate-and-test: Generate-and-test is a procedure to test images (after thebottom-up approach) for their value to the user. This test can be done automatically.

(4.1.5) Case based reasoning: Case based reasoning is a procedure mostly used withtop-down matching strategies. Case based reasoning adapts and combines successful visualrepresentations by keeping a data base of such representations for use and manipulation by auser.

(4.2) Difficulties in the mapping process:

Computer-generated representations can be difficult to interpret due to e.g. approximationsor artifacts

(4.2.1) approximations introduced by the representation technique, as when a smoothsurface is approximated by a collection of polygons

(4.2.2) artifacts introduced by computer graphics techniques, as when a surface color variesbecause of the lighting model

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(4.3) Visual context:

Design Issues, such as scale bars or annotations provide additional visual context outside themain mapping stage.

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Theme 5: Representations

This theme introduces students to a wealth of (visual) representations. As there are a variety ofwell-known techniques for representing data, and because it is impossible to present an exhaustive list,effective categorizations of presentations are important.

(5.1) General discussion:

Many visualization problems can be addressed by choosing from among traditionalrepresentations. In other situations, new representations must be designed. In either case,effectiveness and expressiveness of the resulting image are crucial. Expressiveness refers tothe ability of a visual display to represent (i.e., to encode) the data. Effectiveness refers tothe ease with which a user can interpret (i.e., to decode) the representation. Use the dictumfirst, make sure the picture is accurate.

(5.2) Computer Graphics:

Many issues of Computer Graphics are pertinent to understand (power and pitfalls of)visualization techniques. For the design of new visualization techniques often an in-depthcomprehension of computer graphics algorithms is necessary.

(5.3) Selection Criteria:

To make the right choice among the offered visualization techniques, selection criteria mustbe known and used correctly. These include characteristics of the data (such as type,dimensionality, structure or topology), the purpose of the visualization (which might beexploration, confirmation, or presentation), the visualization goal and output medium.

(5.3.1) Several factors must be considered when choosing representations, including:

(5.3.1.1) Characteristics of the data, such as

(5.3.1.1.1) Type

(5.3.1.1.1.1) Is the data nominal, ordinal, or quantitative? For quantitativedata, is it a scalar, vector, or tensor quantity?);

(5.3.1.1.2) Spatial characteristics

(5.3.1.1.2.1) Is the data geographical?

(5.3.1.1.3) Dimensionality of the data

(5.3.1.1.4) Temporal characteristics

(5.3.1.1.5) Topology of the data, e.g. is the data scattered, unstructured, structured?

(5.3.1.2) The purpose of the visualization, which might be exploration, confirmation, orpresentation. Presentation could be to a small group of colleagues or to a larger audience.

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(5.3.1.3) The [user / goal / interpretation] aim of the visualization.

(5.3.1.4) The output medium [paper, video, interactive?]

(5.4) Techniques:

This chapter describes a number of well-known techniques for representation in order tosuggest the variety of representations that have been used in visual data representationwithout attempting to offer an exhaustive list. Describe first single techniques, such as linegraphs, scatterplots, glyphs, contour plots, streamlines, isosurfaces, or cone trees, thenseveral organizational structures of techniques, such as animation, fish eye view, or linkedmaps.

(5.4.1) Describe number of well-known techniques for representation. This chapter suggeststhe variety of representations that have been used in visual data representation rather thanattempting to offer an exhaustive list:.

(5.4.1.1) line graph, histogram, bar chart, strip chart, pie chart

(5.4.1.2) scatterplots

(5.4.1.3) glyph and icons, e.g. Chernoff faces, Andrews Plots, icons

(5.4.1.4) parallel coordinates

(5.4.1.5) map, contour plot, surfaces (height fields), raster images

(5.4.1.6) particles, streamline, streakline

(5.4.1.7) isosurface, direct-volume rendering

(5.4.1.8) cone trees

(5.4.1.9) ball-and-stick model

(5.4.2) Organizational Structure of representations: Representations are presented withinan overall context. For example, two isosurfaces might be presented side-by-side to facilitatecomparison between two data sets. Or, a series of raster images might be presented in a row,each one showing a different frequency band from satellite imagery of some area. Otherstructures include:

(5.4.3) animation

(5.4.4) worlds within worlds

(5.5.5) perspective wall

(5.5.6) table lens

(5.5.7) fish eye views

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(5.5.8) linked maps

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Theme 6: Interaction Issues

This theme is concerned with the flow of events taking place in an interactive visualization session -concern is to preserve human cycles at the expense of machine cycles.

(6.1) Interaction flow.

It is important to recognize that in designing a visualization one is both creating an interfaceto information and shaping or structuring the interaction which the user of the visualizationhas with the information or data being visualized. In other words, it is important to "designwith the mind in mind (Hewett, 1997)."

(6.1.1) The characteristics of this interaction will facilitate and limit what the user is able todo and the types of information which can be extracted from the visualization. The criticalissue is that in many cases there will be a flow of events taking place overtime. The concernis to preserve human cycles at the expense of machine cycles as generally human cycles costmore than machine cycles.

(6.1.2) Relationship between interface and interaction. In thinking about visualizationdesign, it is important to recognize that there are two different levels of design criteria thatare important. One level is the interface or"tactical" level of design. The second level is theinteraction or "strategic" level. At the interface level, design criteria address issues such ashow interface components should be built and how they should be shaped. For example,aircraft cockpit instruments need to show clearly certain necessary information. Similarly, ina wide range of circumstances, knobs, switches and dials need to be easily discriminable bysight and/or by touch. Interaction level design criteria address the overall goals of the userand take into account the way in which the user thinks about the task. Concern withinterface features is important since badly designed components can make the userŸs taskmore difficult, lead to unnecessary errors, and even prevent successful task accomplishment.However, optimizing interface features without paying close attention to the overall flow ofinteraction can lead to unworkable or unusable systems.

(6.1.3) Example.

(6.2) Interaction design.

As illustrated above, even with locally well designed interface components a tool can fail tosupport the userŸs interaction needs unless care is also taken to understand and support theuserŸs task goals. Focusing on the problem of interaction design, Norman (1988) hasoffered a series of ideas about how to design artifacts that are both usable and useful.

(6.2.1) Seven stages of action. The model Norman has developed involves what he calls the"seven stages of action." To complete an action successfully, the user must first formulateone or more goals. Once a goal has been established there are then six additional stepswhich must be taken for the user to determine whether or not the goal has beenaccomplished. The user must: 1) formulate an intention to act; 2) specify the action(s) tomake; 3) execute the actions successfully; 4) perceive the state of the world;5) interpret thestate of the world, and 6) evaluate the outcome.

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(6.2.2) The gulfs of execution and evaluation. The first three of these steps (1, 2, &3)Norman calls, "The Gulf of Execution," and the second set of three (4, 5, &6) he calls, "TheGulf of Evaluation." If any step doesn’t work properly either the user won’t make progresstowards the goal, or else the user won’t be able to determine the outcome of actions taken.The visualization designer should facilitate the user’s tasks by helping to make it clear howto bridge the Gulf of Execution and by providing appropriate feedback to make it possible tobridge the Gulf of Evaluation. As noted above, one prerequisite to designing a visualizationwhich facilitates tasks is understanding what those tasks are and how the user thinks about them.

(6.2.3) Some design principles. At a general level, some guidance in how a visualizationdesigner might help the user bridge the Gulfs of Execution and Evaluation can be found in aset of human-computer interaction design principles proposed by Norman.

(6.2.3.1) Principle 1. The first design principle is that the designer should enable theuser to make use of both knowledge in the head and knowledge in the world.Knowledge in the head is that knowledge which is stored in memory. However, weoften can and do store only partial knowledge in memory and make use of the presenceof knowledge in the world to assist in guiding our actions. For example, most peoplehave little trouble dialing a phone number such as555-PUCK to get hockey tickets.However, with no telephone dial or set of pushbuttons visible, many of those samepeople have to reconstruct rather than remember the letters associated with eachnumber. (They also have difficulty identifying the two letters of the alphabet which donot appear.) Similarly, the typical graphical user interface provides memory cues whichreduce the userŸs memory burden since it stores many commands on the screen wherethey can be easily located or found with minimal search time.

(6.2.3.2) Principle 2. A second design principle identified by Norman is that thedesigner should seek to simplify the structure of the tasks which the user needs toperform. This simplification can involve several different options. One is to keep thetask the same and provide one or more mental aids (e.g., the calendar for people whohave to do scheduling; e.g., a symbolic computing engine which can send numericoutput to other tools). Another option is to improve feedback and the userŸs ability tomaintain control by using technology to make things visible that would otherwise notbe visible (e.g., instruments in the automobile; e.g., processing flow in a parallelprogram). A third option is to automate portions of the task without radically alteringthe actual flow of the task (e.g., provide turn signals for automobiles rather than requirearm and hand signals; e.g., provide a consistency checker which determines if there aremissing parentheses in a complex equation; e.g., develop a notation translator whichfacilitates conversion from one notation to another). The fourth option described byNorman for simplifying the structure of the userŸs tasks involves actually changing thenature of the task to make it more easily performed (e.g., learning to tell time is mucheasier with digital watches than with analog; e.g., some navigational instrumentsrequire positioning of a slide and reading of results rather than calculation of numbers).

(6.2.3.3) Some additional principles. NormanŸs third design principle is to make thingsvisible so that the user can more easily bridge the Gulfs of Execution and Evaluation(e.g., the Captain of an ocean going ferry boat should have an indicator light on thebridge which clearly signals that the front deck doors which prevent large waves fromwashing over the automobile deck have been closed). A fourth design principle is to getthe mappings right. That is, the results of usersŸ actions should match their

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expectations. For example, the motion of a throttle should require a forward push, asshould the motion of other controls associated with an increase in speed. Controls torotate 3D visualizations should move or point in the direction of intended motion. Oneof the more important of NormanŸs principles is that the designer should design forerror. The designer should assume human error will occur and allow for error bymaking it possible for the user to be able to identify when an error has occurred and tobe able to cope with or even avoid the undesirable consequences of the error.

(6.3) Human performance characteristics and limitations.

One of the earliest books on human-computer interaction to address human performancecharacteristics and limitations was written by Card, Moran, & Newell (1983). In this bookthe authors introduce the notion of, "the model human processor," as a way establishing aconceptual framework within which to discuss the operating characteristics and performancelimitations which might be assumed to be true of any human interacting with a computingsystem. Their discussion ranges from the level of individual key strokes and the componentsof reaction times involved in those keystrokes all the way up to the level of talking about theGoals, Operators, Methods, and Selection rules(GOMS) that might be involved in acontextually appropriate model of human problem solving. In the intervening years, therehave been a number of other books published which discuss the issue of how to factorhuman capabilities and limitations into the problems of interface and interaction design.Some which provide broad coverage and which should be readily available include Baecker& Buxton (1987); Baecker, Grudin, Buxton &Greenberg (1995); Booth (1989); andHelander (1989). Others provide an introduction to some of the more controversial issues inthe field of human-computer interaction (e.g., Carroll, 1987, 1991). The purpose of thisdiscussion is to provide a summary overview of some selected topics and their relevance tovisualization design.

(6.3.1) Demands on working memory. The contents of working memory are often thoughtof as having been selected for further processing by an attentional mechanism which isguided either by changes in sensory stimulation, by expectations resulting from immediatelyprior processing, by expectations resulting from information already stored memory, or bysome combination of the three.

(6.3.1.1) Working memory has a limited capacity and duration. Usually, workingmemory is described as having a limited storage capacity (seven plus or minus twochunks)for a relatively brief duration (estimates range from 12 to 30 seconds withoutrehearsal) before information is lost through simple decay. (i.e., After about 12secondsthese chunks have disappeared and are no longer available for use in workingmemory.). Another way in which information gets lost from working memory is whennew information displaces older information. (Think of watching a moving train fromyour office window, you can see only a limited number of boxcars moving along thetrack at any given time, those that have already passed out of view have been displacedby cars currently visible through the window. You know that the earlier boxcars werethere, but....) Although new chunks can be added to working memory, either byintroduction from the perceptual world or by activation of a node in long term memory,the capacity limitation will result in some chunks being lost if the number of chunksbeing juggled exceeds the capacity of working memory. Finally, it should be noted thata failure to recall something from working memory can also occur as the result ofinterference, i.e., the retrieval of an item which is not the correct one but which hassimilar characteristics. Even though interference between items does not strictly

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represent a loss of information from memory, if you query memory and retrieve anitem similar to but not identical with one which was put in, there is are call failure.

(6.3.1.2) Keeping memories alive in working memory. Decay in working memory canbe prevented by rehearsal of the chunk, i.e., by simple repetition. Information can bemaintained in working memory for periods of time longer than 12-20 seconds withmaintenance rehearsal (MR). However, this simple repetition of material does notappear to be very efficient at transferring information into long-term memory (LTM).Rather, elaborative rehearsal (ER), which requires working with the information insome way, appears to be the most effective set of processes for the transfer ofinformation into long-term storage. Considered as a unit of storage the chunk issomewhat problematic in that it turns out that sometimes chunks contain only one ofsomething and sometimes chunks contain much more. For example, a chunk may beonly a single one digit number from a randomly presented string. Or alternatively achunk might well contain several digits, as in the case of storing 555-PUCK intoworking memory so that one can easily recall the number to call for hockey tickets.There are reasons to believe that any principle or basis for the organization orstructuring of long term memory can serve as the basis for chunking. This also meansthe capacity of a chunk is variable, depending upon the complexity of the knowledgestructure upon which it is based.

(6.3.1.3) Some implications. While it is not possible to provide an exhaustivedescription of the implications of the various strengths and limitations of workingmemory, it is probably useful to describe a few examples which go beyond the effectsof obvious capacity limitation created by the number of chunks which can bemaintained in working memory.

(6.3.1.3.1) Given the apparent failure of maintenance reversal at transferringinformation into long-term memory, would you expect the user of an interactivevisualization system to be able to recall command strings where practice involvessimple repetition of commands? What type of experience with commands isneeded? What implications are there for the design of tutorial manuals and for thestructure and choice of exercises?

(6.3.1.3.2) When looking at and working with a visualization, domain experts andnovices will probably have very different bases for chunking information andtherefore will work with a particular representation in very different ways. Forexample, we would expect novices to focus on and extract relatively superficial ortrivial features from the visualization since their chunks. What type ofimplications does this have for error messages?

(6.3.2) Strengths and limitations of long term memory. In general long term memoryrefers to those memories which may persist of many years but are not active in workingmemory. LTM, which in many ways is an enigma, has an indefinitely large capacity forstorage of information for long periods of time. For example, it is useful to hink of theorganization of long term memory as being something like a complex semantic network ofconcepts and relationships among concepts. Activation of one node in the network can resultin an increased likelihood of activation of nearby nodes.

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(6.3.2.1) Capacity of long term memory. There is, however, no easy or obvious way todetermine the limits of how much information can be stored, or for how long it can bestored. Determination of capacity requires identification of a unit of storage. The rangeof things stored in long term memory is quite broad and includes such things asrepresentations of events that have been experienced and a variety of knowledgestructures that have been acquired by reading or thinking. Among the types ofinformation represented in LTM are such things as facts and events, motor andperceptual skills, knowledge of physical laws and systems of mathematics, a spatialmodel of the world around us, attitudes and beliefs about ourselves and others, etc. Thisinformation is more or less well organized, in a variety of ways, and varies in itsaccessibility as a function of several factors.

(6.3.2.2) Access to the contents of long term memory. The factors determiningaccessibility of the information in LTM include such things as the conditions whichexisted at the time the information was stored, the regency of its last use, its degree ofinter-relationship with other knowledge, its degree of uniqueness relative to otherinformation, etc. While most authors stop short of claiming that information oncestored in LTM is not forgotten, most discussions of failure to recall information fromLTM focus on explanations such as interference, the absence or inappropriateness ofretrieval cues, or some type of organic dysfunction such as brain damage. While thereare many researchers who argue strongly for the existence of a process of decay ofinformation in long term memory there is still controversy of the degree to whichfailure to recall information from LTM should be attributed to decay rather thaninterference or lack of appropriate retrieval cues.

(6.3.2.3) Some implications. It is clear that humans interacting with computers and withvisual representations of information can and do use existing memories and memorystructures to assign a meaning or interpretation to a wide variety of things, regardless ofwhether that meaning was the one intended by the designers. Consequently, it isimportant to understand and take account of existing knowledge structures and howpeople think about domain problems and the problems of interaction. As with workingmemory it is not possible to provide an exhaustive description of the implications of thevarious strengths and limitations of long term memory. However, it is again useful todescribe a few examples which go beyond the obvious fact if people do not have theappropriate knowledge structures they will not be able to correctly interpret what theysee, even if told what they are seeing.

(6.3.2.3.1) If getting the appropriate meaning for a word or if making sense out ofa procedural description or visualization depends upon having the appropriateknowledge structure in a personŸs head, how helpful will it be to providesomeone with set of instructions about what to look for in a visualization, or witha detailed set of step by step instructions for accomplishing a particular interactiongoal, when they do not have a clear and appropriate mental model and of whatthey looking at or what they are doing and why? Can such a mental model beestablished simply by following instructions?

(6.3.2.3.2) Imagine you are part of a product development team. Your companythrives on the sales of a visualization software toolkit. It is your teamŸs job tomigrate the software from Version 3.x, which has been quite successful in anon-GUI environment, into Version 4.0 which is expected to run under a GUI.Metaphorically speaking you have tied around your neck that large, cumbersome

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sea anchor known as, "an installed user base." This user base includes bothend-users of 3.x visualizations and the large number of developers employed byyour customer companies and/or research laboratories. The role of thesedevelopers is to use your tool kit to create in-house visualization environments foruse by the end-users (their user group). Given that a GUI involves a new model ofinteraction which is similar in some respects and which differs markedly in othersfrom what the developers and end-users are accustomed to, part of your task is tobuild Version 4.0 in such a way that your installed user base can migrate with thegreatest possible ease and least possible pain. Can you think of any reasons why itmight be important to convince your management to allow you to do frequentearly iterative evaluation of Version 4.0 with developers who use your existingtoolkit for building visualizations? Would knowing something about the existingknowledge structures of developers who use Version 3.x (e.g., how they thinkabout the current functionality, etc.) be helpful in finding a way to organize thefeatures, structure and functionality of Version 4.0 so as to ease their migration tothe new system?

(6.3.3) Human problem solving. A major turning point in the literature on problem solvingoccurred with the work of Newell and Simon (1972). Newell and Simon proposed that aproblem be analyzed in terms of a"problem space" representing various states of knowledgeof the problem solver, a series of transformations between states, and a set of operatorswhich produce those transformations. In other words, a problem exists when we have a gapbetween an initial state and a goal state. The means of solving the problem involvesselecting and applying the appropriate set of operators required to complete a series of statetransformations that will eliminate the gap. These transformations must be accomplishedwithout violating any of the conditions on the operators. It is this conception of humanproblem solving which motivated the GOMS model proposed by Card, Moran & Newell (1988)

(6.3.3.1) Problem solving schemas. As a result of repeated experience with a series ofidentical or very similar problems, problem solvers build up an organized body ofknowledge or information about the properties of a particular type of problem. Such anorganized body of knowledge is usually referred to as a schema (e.g., Norman, 1982).There are a wide variety of familiar problem schemas (Hayes, 1981), and the typicalindividual has built up a stock of problem solving schemas which come into play insolving problems. Some of these schemas are so familiar they are activated almostautomatically and without thought. Typically, the main effect of problem solvingschemas is to provide us with reasonably efficient methods of solving frequentlyencountered kinds of problems. However, sometimes a schema can interfere withproblem solving. Some of these side effects of schemas can be illustrated byconsidering two related concepts described in the older literature on problem solving.These concepts are problem solving set and functional fixedness.

(6.3.3.1.1) Problem solving set. The work of Luchins (1942) provides a reasonablyclear example of the concept of a problem solving set, i.e., the effect of priorexperience in limiting or constraining solution procedures. Luchins asked peopleto solve a series of problems, many of which required the exactly the sameprocedures. He found that several of his participants continued to use the solutionprocedure developed on the early problems even when they came to problemslater in the set which would solve by a simpler or more efficient procedure.

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(6.3.3.1.2) Functional fixedness. Functional fixedness is a second concept fromthe older literature on problem solving which can be seen as being related to theconcept of a schema. Coined by Duncker (1945), the term "functional fixedness"is used to refer to the inhibition created when an object with a familiar functionneeds to be used in a new and different way to solve a problem.

(6.3.3.1.2.1) A standard example of functional fixedness is Maier’s (1931)two-string problem. For this problem, you are in a room with two strings tiedto the ceiling. Both strings are of equal length. Your objective is to tie theends of the two strings together. The problem is that while the strings arelong enough to be tied together they are short enough that you are unable tojust take hold of one string, walk over to the other string, and tie themtogether. Scattered around the room there are a number of objects. Theseobjects include a plate, some books, a chair, a pair of pliers, and a book ofmatches.

(6.3.3.1.2.2) About 60% of the participants in Maier’s study failed to find asolution within a 10 minute time limit. These individuals saw the pliers onlyas a tool, not recognizing that the pliers could be used as a pendulum bob,swinging at the end of one of the two strings. (Significantly more participantswere able to find a solution to the two string problem in an alternativesituation. In this alternative situation there was an open tool box in the room.In the room were several hand tools, including a pair of pliers. In addition,visible in the tool box was a plumberŸs bob.)

(6.3.3.1.3) Representation and re-representation. Since it influences the problemsolver’s early analysis of the problem and determines which problem solvingschema, or schemas, will be brought to bear, the way in which a problem isrepresented can make a vital difference in how easily it can be solved. Sometimeswhat is needed to facilitate problem solution is an alternative representation (aproblem isomorph or a problem analog).

(6.3.3.1.3.1) Representations clearly make a difference in the ease with whicha problem can be solved. As Simon(1981) points out, "Solving a problemmeans representing it so as to make the solution transparent." For example,sometimes people debate which programming language is the best. Givenany five programmers there it is not unusual for there to be at least sixanswers. Does the multiplicity of answers suggest that the choice of a "best"language depends on what one wants to accomplish? Occasionally someonewill claim that anything which can be done in one language can be done inany other. But, would anyone choose to do intensive scientific computationin LISP or Prolog? Would anyone choose to do Artificial Intelligenceprogramming in FORTRAN?

(6.3.3.1.3.2) As Norman (1993) points out, the "powers of cognition comefrom abstraction and representation." Once one has a representation one canuse that representation to think about a situation. The difficulty is that onemust get the essential structure of the situation into the representation,otherwise the representation becomes misleading or a hindrance to solvingthe problem. When a problem that looks solvable is not solved as easily asexpected it is often quite fruitful to look to the adequacy or the nature of the

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representation of the problem, and to give consideration to changing thatrepresentation to a new one.

(6.3.3.2) The importance of problem solving in visualization. In considering the natureof human problem solving it is clear that visualization designers need to take account ofthe fact that users will bring to bear established problem solving strategies which aredeveloped from having solved problems that have been frequently encountered in thepast. While these established strategies can be used to facilitate usage they can alsocreate process blockages which may only be solved by changing the way in which theproblems of interaction are presented to the user or by creating a working environmentin which the user has the flexibility to redefine, reconceptualize or re-represent thetasks, the problems, and/or the visualizations which the software is being used to solve.In particular, the software should make it possible, without penalty, for the user tosuspend current activities and be able: a) to see more information about the software orthe problem domain; b) to re-examine or re-formulate some aspect of the visualization,the interaction problem or the domain problem, with an eye towards modifying thatproblem in some way; c) to completely re-represent or re-formulate the entireinteraction or domain problem; and d) to break up the interaction or domain probleminto manageable sub-problems in ways that allow for productive recombination ofelements or components.

(6.2.4) Other factors impacting the interaction flow. Clearly there will be, dependingupon the circumstances, a number of other variables which may impact the flow of theinteraction between the user and a visualization and therefore become relevant in thinkingabout the problems of interface and interaction design. For example, given that bothmemory structures and changes in external levels of stimulation can impact attentionalmechanisms and the focus of a user’s attention there are both internal and external factorswith which to contend.

(6.2.4.1) What will be the effect of repeated shifts of attention (on a userŸs memoryburden, say) of taking items which are conceptually related in the userŸs mind orwhich are integral sub-tasks of a single task and putting them in widely separatedscreen locations, in screen locations which differ from one screen to the next ,or ondifferent screens entirely?

(6.2.4.2) Suppose that a user is working in an environment where there is a continuousflow of information and a number of distracting events. Does this suggest anythingabout the kind of memory aids one might want to design for users who will be workingwith visualizations in a context where they will continually have to be shifting theirattention from one information stream to another?

(6.2.4.3) Suppose that in my working memory I have two pieces of information which Iwill need to use on the next task in a series of tasks on which I am working. Now Imust shift the focus my attention to working out a series of voice commands to preparethe computer software for the next task. What might happen to the contents of working memory?

(6.4) Implementation

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(6.4.1) Interaction Techniques, the three big issues here are:

(6.4.1.1)Direct manipulation (interacting with visual depictions of data directly).

(6.4.1.2) Integration of visualization and computation in a tight interaction loop forcomputational steering.

(6.4.1.3) Shared, collaborative visualizations and interactions, where users at differentscreens see the same visualizations and share interactive control of them.

(6.4.1.4) Individual techniques supporting these issues are

(6.4.1.4.1) selection

(6.4.1.4.2) moving

(6.4.1.4.3) editing

(6.4.1.4.4) probing

(6.4.1.4.5) other tasks (print file, delete file, ...)

(6.4.2) The Interface

(6.4.2.1) Can facilitate interaction

(6.4.2.2) Can inhibit interaction

(6.4.3) Interaction Components

(6.4.3.1) Visual and non-visual ICs

(6.4.3.2) Well designed ICs

(6.4.3.3) Ill designed ICs

(6.4.4) Consistency. Consistency in interface and interaction design is often said to be agood thing. But, consistency is a relational concept and has little or no meaning unless weanswer the question, "Consistent with what?" Suppose we are developing software with anew range of functionality. How useful will it be to put a higher premium on consistencywith a style guide than on consistency with the mental model we want the user to develop oruse in thinking about the task, the visualization, the software and the interaction?

(6.4.4.1) Standards

(6.5) Evaluation.

The development of visualizations and of interactive computing systems in general shouldbe an iterative process of design and redesign which involves users as participants in theprocess of development and refinement. For example, Hewett(1986, 1995) argued for theimportance of user-driven, iterative evaluation-evaluation as part of each design cycle-as amajor factor underlying the process of successful interactive computing system design. Onetype of evaluation formative involves monitoring the process and products of developmentand gathering user feedback for use in refinement and further development of the materials.

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A second type of evaluation summative involves assessing impact, usability andeffectiveness of the materials. Different stages and components in the development ofvisualizations an interactive computing materials require different types of evaluation.

(6.5.1) The role of evaluation. The first, and most important, thing to recognize aboutevaluation of visualizations and of interactive computing systems is that designers do it. Thesecond thing to recognize is that the users do it. The only uncertainties are when, and howsystematically, the evaluation will be done, and whether the evaluation results will be usedto guide redesign of the interface, the interaction, and visualization materials. Consequently,it is essential that the evaluation process be structured to create and maintain a clear focus onthe goals of the project and to provide useful, meaningful information to guide theimprovement of the visualization and the flow of interaction. Similarly, evaluationprocedures themselves sometimes need to be designed, evaluated, and redesigned.

(6.5.2) Formative and summative evaluation. In developing a systematic evaluation planfor any visualization or interactive computing environment it is essential to recognize thebasic difference between two types of evaluation formative and summative. This distinctionwas first discussed by Scriven (1967) in the context of the evaluation of educational andsocial action programs. These two types of evaluation have very different goals and it isimportant to keep them separate.

(6.5.3) Formative evaluation. As originally described by Scriven, formative evaluations theassessment done to evaluate progress towards completion of the project, prior to actuallyimplementing it. That is, formative evaluation is an assessment of the state of developmentof the relative to intended final form. This type of evaluations initially focused ondetermination of what the various components of the social action program should be.During development it is focused on whether or not the pieces of the project are beingbrought together as they should be. Is the project moving towards the stated conditionswhich must be in place before the program or intervention is actually carried out? Are thereunanticipated factors which necessitate a re-design of the plans? Was the original goalmisconceived and in need of be in re-thought? As applied to development of visualizationmaterials and interactive computing systems, formative evaluation involves monitoring theprocess and products of development and gathering user feedback for use in refinement andfurther development of the visualization, the interface, and the interaction flow. In otherwords, what are the goals of the project and is the project moving towards those goals?

(6.5.4) Summative evaluation. Scriven described summative evaluation as that type ofevaluation which is conducted when what is wanted is an assessment of the impact of thesocial action program once it has been put in place. Does intervention have its desired effectupon the people it is intended to benefit? Is the social action program more successful thanthe alternatives already in place? As applied to development of visualization materials andinteractive computing systems, summative evaluation involves assessing the impact,usability and effectiveness of the materials-the overall task performance of user given thevisualization. In other words, does the visualization make the user’s task easier, harder, or isthere no difference when compared with the alternative ways of interaction with the data orinformation and its representation.

(6.5.5) Formulating evaluation criteria. In thinking about conducting evaluations, perhapsthe most critical component of a successful evaluation is the selection or formulation ofappropriate criteria upon which to base an evaluative judgment. What follows is a samplelist of some of the types of evaluation criteria and judgments which might be appropriate for

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data and information visualizations.

(6.5.5.1) Is the visualization more efficient than the alternatives?

(6.5.5.2) Does the visualization allow the user to do or see something new and different?

(6.5.5.3) Is the visualization easily transportable, i.e., can it be used on more than onemachine or in more than one environment?

(6.5.5.4) Does the software which controls the visualization allow for "what if"manipulations of the representation?

(6.5.5.5) Does the software make adequate use of unique capabilities of the technology(e.g., Hypertext)?

(6.5.5.6) How much documentation is required to use the software?

(6.5.5.7) Can users who have browsed easily find out how to revisit points of interest?

(6.5.5.8) Does the software allow a user to interact directly with information?

(6.5.5.9) Does the software represent a special purpose tool or a generic tool withcapabilities for extension?

(6.5.5.10) Does the software seem cobbled together or does it exhibit a clean, coherent concept.

(6.5.5.11) Does the software offer important functionality not found elsewhere?

(6.5.5.12) Does the visualization enhance the userŸs ability to learn from and about thematerial being visualized?

(6.5.5.13) Does the infrastructure operate against natural convenient use of thevisualization or the software?

(6.5.6) Some general guidelines. Typically the development of a visualization or interactivecomputing system will require more than one design-evaluate cycle. There are some usefulguidelines to keep in mind during the iterative process of re-design and revaluation. Forexamples from the design of interactive computing systems, see Hewett (1986, 1995).

(6.5.6.1) Guideline One: "Know the User." Probably the single most basic admonitioninhuman factors and ergonomics is, "Know the user." However, this admonition mightbetter be formulated, "You have a model of the user, what is it?" Designers ofvisualizations and interactive software should seek early involvement with users as partof the design team (cf. Gould & Lewis, 1985; Hewett & Meadow, 1986) so as to avoidbeing trapped into erroneous assumptions about their visualization, the computerinterface, and the interaction flow.

(6.5.6.2) Guideline Two: "Seek Out a Variety of Users." Once a prototype of thevisualization and interaction software is available, and on a continuing basis there after,the designers should seek out as wide a variety of users as possible. For almost anygroup of users who get actively involved with evaluation of the materials there are

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strengths and weaknesses associated with the type of information they can provide. Forexample, volunteers will often go out of their way to be helpful to the designers, evento the extent of compensating for problems in the computer interface or not mentioningproblems encountered with the visualization. Consequently, designers should recognizethat it may be necessary in actual practice to repeat this type of formative evaluationmore than once to insure a sufficient variety of users.

(6.5.6.3) Guideline Three: "Pilot Test Summative Evaluation." There are importantbenefits to pilot testing summative evaluation procedures whenever possible. Designingan evaluation is much the same as designing anything else. It is possible to overlookimportant factors until one is actually engaged in putting the evaluation procedures intopractice. Also, a summative evaluation pilot test can have a very powerful formativeevaluation effect by forcing clarification and reassessment of overall project goals andhow best to determine whether or not they have been achieved. In day-to-day concernwith the details of visualization and interaction development it is possible to lose sightof the overall project goals, allowing the direction of the project to undergo subtleshifts. Facing up to the question of, "Compared to what?" can help one to realize thatthe visualization may not be as helpful as it ought to be and that project goals need tobe reformulated.

(6.5.6.4) Guideline Four: "Prepare for Contingencies and Trade-offs." Justas design,development, and delivery of a visualization and interactive software materialsinvolves making contingency plans and trade-offs, so too a summative evaluation ofthe effectiveness of those materials requires making contingency plans and trade-offs.Such planning can help minimize the impact of possible disruptions of evaluationstudies. Similarly, it can help maximize the gain from unexpected opportunities. Thekinds of trade-offs required in summative evaluation include: the type of users to test inthe evaluation studies, the size of the samples to be used, the kind and amount of datato collect, when and how often to test during the development of the participants’abilities, whether to use single or multiple tasks, and whether to use a "real" or astandardized task in the evaluation.

(6.5.7) Some procedures for conducting formative evaluations. While any good book onusability testing (e.g., Hartson & Hix, 1993, Lindgaard, 1994) will describe a variety oftechniques for gathering the information needed to improve the design of a visualization,most of these techniques can be summarized by noting that they have a number of basicthings in common. One commonality is that the procedures for conducting usability teststypically are open-ended enough to allow for the unexpected, i.e., to allow the users tobehave in relatively natural ways which provide useful information about their interactionwith the visualization and software. A second, and more important commonality is that theprocedures are designed to reduce or compensate certain kinds of biases which can eliminatethe benefits of conducting an evaluation. A reasonably accurate way of thinking the biasproblem is to consider a developer’s quite natural desire to see a visualization appreciatedand to want to coach the user through difficulties which might be eliminated by redesign.Such coaching is only really helpful if the designer is willing to provide such assistance anyuser of the visualization or the software.(Anyone who thinks that carefully conductedformative evaluation work is just too expensive should take a look at work reported in Bias& Mayhew (1993) before investing too much time and money in a project where noresources are being allocated for usability testing.)

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(6.5.7.1) An example. The following example, based upon work reported in Hewett &DePaul(under review), illustrates the use of three different formative evaluationtechniques to develop an understanding of the visualization needs of a researchmathematician and of the inadequacies of the visualization software to which hecurrently has access. The techniques used were interviewing, simple observation ofbehavior, and a variation of the thinking out loud and protocol analysis proceduredescribed by Ericsson & Simon (1996).

(6.5.7.2) The nature of the problem. The mathematician wants to produce geometricrepresentations of a particular class of equations known as soliton equations. Abouttwenty five years ago, certain equations which are highly complicated were found to beexactly solvable. These types of equations, complex equations with simple behavior,are referred to as soliton equations. There are a number of such equations known. Twoexamples of equations which behave in this manner are the equations for fiber opticsand the equations for smoke rings (or tornadoes).The mathematician being studied hasfound that these different equations share subtle correspondences. Furthermore, hebelieves that the soliton equations are so similar that there must be a generalizedgeometric analog, a curve moving in some space (within or beyond the thirddimension), which is common to all soliton equations. Currently, to solve therepresentation problem, the mathematician takes the information provided in thesolutions of the soliton equations, the bend and the amount it leaves a plane, andconverts that information to create a geometric representation.

(6.5.7.3) Information gathered from interviews. Through a series of interviews,conducted as preliminary information gathering sessions before directly observingproblem solving activities, some interesting things were learned about the problemsolving behavior of the mathematician. Although the information provided here wasself-reported by the mathematician and was reported post-hoc, there is reason tobelieve that the issues discussed in the interviews are representative of the actualbehaviors. For example, the problem solving behaviors described by thismathematician parallel those found in experts in other fields (e.g., Candy & Edmonds,1995; Clement, 1988; Dunbar,1995).

(6.5.7.4) Self-reported visualization needs. In the past year, the mathematician has puttogether a paper in which he argued that higher dimensions play a role in forming therepresentations of some soliton equations. With this knowledge he then found apossible representation for the particular soliton equation he was working on solving,are presentation which exists in some n-dimensional space. Since this type of solutionis very difficult to work with and since most people have trouble comprehending higherdimensions, the mathematicianŸs next step is to find either another soliton equationwhich could occur in 3Dspace, or another space in which a simplified version of thecurrent representation could occur. To begin this next step of the problem, themathematician is looking at the possibility of representing these equations in the threesphere, hyperbolic space, or one of several known space models where light does nottravel in a straight line, but nevertheless, travels in a controlled or understood manner.His reason for turning to these representation spaces is that his knowledge of thebehavior of the structure of these spaces has led him to believe the curves or surfaces ofthe soliton equations could potentially be characterized by the same type of behavior.

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(6.5.7.5) A need for three dimensional interactive visualizations. In terms of his3Dvisualization needs, the mathematician reports that he needs to be able to discretizethe equations in such a way that he is able to directly perceive some of their structure.The starting object in the visualization is a curve in the plane. He reports that he utilizesrotation regularly to help gain a sense of structure and often finds that a switch invisualization modes (from solid to grid or from grid to solid) is helpful in "seeing" whathappens. He reports being frustrated by the fact that the intersections of the surfaces areimportant and are often particularly hard to see. He also reports that it is important tohim both to see the full result surface and to see how the curve evolves frame by frame.Consequently he expresses a desire to have tools which he can use with a 3Dvisualization which will allow him to create an intersection focus, an edgeenhancement, and a local isolation of pieces of the picture for manipulation.

(6.5.7.6) Sources of frustration. Since the internal structure of the 3Dvisualrepresentations with which the mathematician works is often concealed by a surface,even with a grid visualization, he thinks he would also like to have some specialpurpose tools which would enable him to enhance the visibility of regions of theinternal structure. Analogies which seem to represent these needs, and which themathematician accepts, include the notions of "pearl growing," of "onion peeling," andof "surface crawling" or "paint spreading." Surface crawling or paint spreading wouldinvolve being able to endow a region of an internal portion of the surface with adifferent color or texture and then being able to move or spread this visible differencearound or across regions of the internal structure, thereby "highlighting" and makingthose regions more discriminable visually. The instantiation of pearl growing mightinvolve taking a small portion of the internal structure as the starting point and thenbeing able to watch as the surface "grows" into its full complexity. Similarly, onionpeeling in this context might involve taking the full 3D representation and selectivelypeeling off and discarding external portions of the surface, thereby bringing internalsurface into view. Interestingly, the analogies of pearl growing and onion peeling areconceptually the same as those investigated by Hewett & Scott(1987) in the context ofbibliographic database searching. These two analogies were shown to be appropriateways to conceptualize two frequently conducted types of bibliographic database search.In the one case a searcher starts with a singe article and"grows" it into a larger set ofrelated articles. In the other case the searcher starts with a large set of references andselectively "peels" away subsets to achieve a desired result.

(6.5.7.7) Observations of behavior and thinking out loud. During sessions when themathematician’s problem solving behavior was observed, a number of other interestingfactors became clear about his current working environment and the types of featureswhich would improve his ability to do his work an appropriately re-designedvisualization environment. Using a slightly modified version of Ericsson and Simon’s(1996) instructions, the mathematician was asked to "think aloud" while working on aprofessionally relevant problem. The problem was typical of those in which the hisresearch interests lie. He and a colleague were about to publish a paper when theyrecognized that there were errors in the mathematics. The observations of his problemsolving behavior were conducted while he was working to find the errors.

(6.5.7.8) Current visualization tools. The current "problem solving environment"employed by the mathematician includes a common symbolic computing program withvisualization capability, a textbook, personal notes, and a copy of the paper whichcontained the errors. Utilizing the textbook to find the formulas he needs, the

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mathematician then writes them out in standard form before modifying them withfeatures specific to the current problem. This may well be related to the fact that heuses one notation in his research papers, the textbook uses a different notation system,and the symbolic computing engine requires him to use still another notation system.When he is using formulas with variable notation the mathematician often getsconfused and plugs in numbers for the variables to be able to see a solution in terms ofnumbers rather than variables.

(6.5.7.9) Memory demands which could be avoided. The mathematician also ran intodifficulty in recalling the names he had assigned to specific formulas in the symboliccomputing software. The software allows great flexibility in naming equations and heseemed to assign the same labels over and over again, or to randomly assign a newlabel. He would follow a consistent trend in his equation naming convention until heran into a formula which was similar to earlier ones. In this situation he would produce a"random" name and then later have difficulty recalling the name. This difficulty withnames is not surprising, given that typically the mathematician tended to proceedthrough his search for errors in the mathematics in the paper in a careful stepwisemanner, systematically considering one possibility for a solution to a piece of theproblem and then moving on the next step. This careful checking process continued ashe both confirmed and disconfirmed his assumptions regarding the behavior of the equations.

(6.5.7.10) Criteria for a new visualization support environment. From consideration ofthe mathematician’s current working environment and observation of his behavior, it ispossible to identify some characteristics which should be built into a visualization tooldesigned for such a person. He clearly needs simultaneous access to multiple sources ofinformation. He periodically needs to concretize abstract aspects of the problem as away of checking on his progress. The fact that he had difficulty in recalling labels forformulas suggests that a shorthand naming directory would be helpful. In addition, thesymbolic computing software, rather than simply replacing a previous formula with thesame name as a new formula should keep a record of all formulas assigned that label.This would allow retrieval of a potentially lost formula. The existence and commonusage of multiple notation systems suggests that a visualization support environmentshould include a notation translation feature. Another helpful feature would be a syntaxchecker for the programs developed in the symbolic computing software. It was notunusual for him to drop a closing parenthesis, make a typographical error, or introducea syntax error which interfered with the program execution.

(6.5.7.11) A final note. In the example described above it is instructive to consider howdifficult it is to determine the utility of the visualizations actually produced by thesymbolic computing software. Much of the mathematician’s interactions with thesoftware involve preparation and work required to cope with problems generated by thesoftware interface and interaction rather than the mathematics and a visual representation.

(6.5.8) Some procedures for conducting a summative evaluation. The practice ofconducting summative evaluations basically requires some knowledge of and experiencewith behavioral science experimental methods. While any good book on research design(e.g., Kerlinger, 1973; Runkel & McGrath, 1972) or quasi-experimental design (e.g., Cook& Campbell, 1979) can provide detailed descriptions of such methodologies, most of thesetechniques can be summarized by noting that they have a number of basic things in

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common. One commonality is that the procedures for conducting a summative evaluationtypically involve the clear specification of one or more manipulated independent variables,and careful measurement of the dependent variables they are thought to affect. Anotherimportant commonality in the various procedures for setting up a summative evaluation isthe exercise of the procedures necessary to ensure an appropriate control group through theuse of randomization, or the exercise of the procedures necessary to ensure an appropriatecomparison group when randomization is not feasible. The basic guideline for conducting anappropriately designed summative evaluation is to utilize the skills of someone who hasexpertise in experimental design and the statistical analysis of experimental results.

(6.6) References

(6.6.1) Baecker, R. M. & Buxton, W.(Eds.) (1987). Readings in Human-ComputerInteraction: A Multidisciplinary Approach. Los Altos, CA: Morgan Kaufmann.

(6.6.2) Baecker, R. M., Grudin, J., Buxton, W. A. S., & Greenberg, S. (Eds.)(1995).Human-Computer Interaction: Toward the Year 2000. Los Altos, CA: Morgan Kaufmann.

(6.6.3) Bias, R. & Mayhew, D. (Eds.) (1994). Cost-justifying usability. Boston: Academic Press.

(6.6.4) Booth, P. (1989). An Introduction to Human-Computer Interaction. Hillsdale, NJ:Erlbaum.

(6.6.5) Candy, L. & Edmonds, E. A. (1995). Creativity in knowledge work: A process modeland requirements for support. Proceedings OZCHI ’95, pp.242-248.

(6.6.6) Card, S. K., Moran, T. P., & Newell, A. (1983). The psychology of human-computerinteraction. Hillsdale, NJ: Erlbaum.

(6.6.7) Carroll, J. M. (Ed.) (1987). Interfacing Thought: Cognitive Aspects ofHuman-Computer Interaction. Cambridge, MA: MIT Press.

(6.6.8) Carroll, J. M. (Ed.) (1991). Designing Interaction: Psychology at theHuman-Computer Interface. Cambridge: Cambridge University Press.

(6.6.9) Clement, J. (1988). Observed Methods for Generating Analogies in ScientificProblem Solving," Cognitive Science, 12, pp. 563-586.

(6.6.10) Cook, T. D. & Campbell, D. T. (1979). Quasi-Eperimentation: Design & analysisissues for field settings. Chicago: Rand McNally.

(6.6.11) Dunbar, K. (1995). How scientists really reason: Scientific reasoning in real-worldlaboratories. In R. J. Sternberg & N. Davidson (Eds.)The Nature of Insight. The MIT Press,Cambridge, pp. 365-395.

(6.6.12) Duncker, K. (1945). On problem solving. Psychological Monographs,58, WholeNo. 270.

(6.6.13) Ericsson, K. A. & Simon, H. A. (1996). Protocol Analysis: Verbal Reports as Data(rev ed.). Cambridge, MA: The MIT Press.

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(6.6.14) Gardiner, M. M., & Christie, B. (1987). Applying Cognitive Psychology toUser-Interface Design. New York: John Wiley & Sons.

(6.6.15) Gould, J. D. and Lewis, C. (1985). Designing for usability: Key principles and whatdesigners think. Communications of the ACM, 28,300-311.

(6.6.16) Hix, D. & Hartson, H. R. (1993). Developing user interfaces: ensuring usabilitythrough product and process. New York: John Wiley &Sons.

(6.6.17) Hayes, J. R. (1981). The Complete Problem Solver. Philadelphia: The FranklinInstitute Press.

(6.6.18) Helander, M. (1989). Handbook of Human-Computer Interaction. Amsterdam: North-Holland.

(6.6.19) Hewett, T. T. (1986). The role of iterative evaluation in designing systems forusability. In M. Harrison & A. Monk (Eds.) People and Computers: Designing for Usability.Cambridge: Cambridge University Press.

(6.6.20) Hewett, T. T. (1995). Towards a generic strategy for empirical evaluation ofinteractive computing systems. In G. Perlman, G. K. Green, & M. Wolgalter (Eds.). HumanFactors Perspectives on Human-Computer Interaction (167-171). Santa Monica, CA:Human Factors and Ergonomics Society.

(6.6.21) Hewett, T. T. (1997). Designing with the mind in mind: Basic phenomena in humanmemory and problem solving. Tutorial notes for User Interface ’97, Boston, MA: UserInterface Engineering.

(6.6.22) Hewett, T. T. & DePaul, J. L. (under review). General characteristics of a humancentered scientific problem solving environment. Submitted for possible presentation at theACM SIGCHI CHI 98 Conference on Human Factors in Computing Systems, April,1998,Los Angeles, CA.

(6.6.23) Hewett, T. T. and Meadow, C. T. (1986). On designing for usability: An applicationof four key principles. In Proceedings CHI ’86Conference on Human Factors in ComputingSystems. New York: Association for Computing Machinery.

(6.6.24) Hewett, T. T. & Scott, S. (1987). The use of thinking-out-loud and protocol analysisin development of a process model of interactive database searching. In H.-J. Bullinger & B.Shackel (Eds.), Human Computer Interaction-INTERACT ’87,51-56.

(6.6.25) Kerlinger, F. N. (1973). Foundations of behavioral research. (2nded.) New York:Hold, Rinehart & Winston.

(6.6.26) Lindgaard, G. (1994) Usability testing and system evaluation. London: Chapman & Hall.

(6.6.27) Luchins, A. S. (1942). Mechanization in problem solving. PsychologicalMonographs, 54, Whole No. 248.

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(6.6.28) Maier, N. R. F. (1931). Reasoning in humans II: The solution of a problem and itsappearance in consciousness. Journal of Comparative Psychology,12, 181-194.

(6.6.29) Newell, A., & Simon, H. A. (1972). Human Problem Solving. Englewood Cliffs,NJ: Prentice-Hall.

(6.6.30) Norman, D. A. (1982). Learning and Memory New York: W. H. Freeman.

(6.6.31) Norman, D. A. (1988). The Psychology of Everyday Things, Basic Books: New York.

(6.6.32) Norman, D. A. (1993). Things That Make Us Smart. Reading, MA: Addison-Wesley.

(6.6.33) Runkel, P. J. & McGrath, J. E. (1972). Research on human behavior: A systematicguide to method. New York: Holt, Rinehart & Winston.

(6.6.34) Scriven, M. (1967). The methodology of evaluation. In Tyler, R.W., Gagne, R. M.,& Scriven (Eds.) Perspectives on Curriculum Evaluation. Chicago: Rand McNally.

(6.6.35) Simon, H. A. (1981). The Sciences of the Artificial. (2nd ed.)Cambridge, MA: TheMIT Press.

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Theme 7: Models of the Visualization Process

This theme describes the visualization process as a flow of data and operations. Pipeline models areused as well as reference models to conceptualize the visualization process from various standpointsof systems builders and users. This theme concentrates on conceptual descriptions of the visualizationprocess through its evolution since the mid ´80s. In order to understand and compare the concepts ofcurrent visualization systems and guide designers and developers in their developments ofnext-generation systems, abstractions to describe main components of the visualization process andthe interfaces between them, including users and their behavior, are needed.

(7.1) Process models:

This chapter describes various diagrams used to explain models of visualization processes(suggested chronological order)

(7.1.1) pipeline: Pipelines show the data flow in a visualization process from left to right,with no or only limited opportunity to reverse actions.

(7.1.2) "Man in the loop": "Man in the loop" is an early taxonomy explaining data andvisualization processes together with human interaction.

(7.1.3) Visualization idioms: Visualization idioms provide a concept similar to thevisualization pipeline but with extended flexibility for the user.

(7.1.4) interactive models: on the level of data handling such models allow to interactivelypoint into the data, to use semantic interaction with data or an intelligent handling of largedata sets; on the level of representations these models allow the integration of differentvisualization methods into one image; on the level of the user interface such models allow tointerrupt the visualization process at any time to interactively change simulation parameters,rendering parameters etc.

(7.2) Reference model:

This chapter describes one reference model to compare the various approaches of processmodels.

(7.3) Enabling technologies:

This chapter describes various visualization processes through the aspects of softwareengineering and hardware technologies.

(7.3.1) Software and hardware: Software and hardware design issues relevant to theusability of visualization tools are discussed, such as batch mode versus interactivity,distributed environments and object oriented paradigms.

(7.3.2) User interface: Data flow diagramming for the user interface are discussed as wellas truly interactive systems.

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(7.3.3) Mapping strategies: Strategies of (automatic or assisting) visualization systems areexplained in detail, such as APT, VISTA, BOZ, SAGE, or IDIAS

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Theme 8: Systems and Tools

This theme contains descriptions of a wide variety of tools and systems currently in existence. Theclassification used is by multi-purpose tools and specialized systems.

(8.1) Wide variety of computer tools and integrated systems:

There have been a wide variety of computer tools and integrated systems developed forvisualizing the various types of data from the many disciplines that are active in this field.This chapter is grouped into two types: Multi-purpose tools and Specialized systems.

(8.2) Discuss multi-purpose tools (to handle wide variety of data, independent of a discipline):

The multi-purpose tools have been developed to handle a wide variety of data, independentof a discipline, although in many case it was a specific discipline that motivated thedevelopment. The tools range from languages that require the user to write complex anddetailed programs that allow the user to manipulate the date in a very easy and convenientmanner to produce complex visualizations.

(8.3) Discuss specialized systems (developed for specific disciplines) and are tailored for theunique date in that field):

The specialized systems have been developed for specific disciplines and are tailored for theunique data in that field. For example, GIS (geographic information systems) manipulateand display geographic based date to create maps. Typically such systems are large andcomplex and able to produce complex visualizations. However, they typically takesignificant amounts of time to learn and master since the they have many modes ofoperation and features.

(8.4) Examples of multi-purpose tools

(8.4.1) Languages and Libraries:

(8.4.1.1) The languages range from the traditional computer programminglanguage such as Visual Basic with its own graphics routines embedded inthe language, to C, and C++ where the user writes a structured program thatinterfaces to a graphics library such as OpenGL and OpenInventor. Theseexamples produce anywhere from a simple presentation to complexsimulations and animation. e.g. OpenGL, OpenInventor, VRML

(8.4.1.2) Graphics Systems for modeling, such as 3D Studio, Wavefront, SoftImage, MacroMedia Director. Recently a new and higher level set ofvisual languages have been developed that operate over networks and allowthe user to manipulate and interact with the visual display. These includeJAVA and VRML.

(8.4.1.3) OpenGL - A set of graphics routines originally developed by SGIthat are called from C and C++ to manipulate data and graphical images.

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(8.4.1.4) VRML - Virtual Reality Modeling Language

(8.4.1.5) Visual Basic - An extension of the original BASIC language toinclude structure and embedded graphics routines. It has the added value ofinteracting with any of the MicroSoft systems using OLE and �.

(8.4.1.6) C++ - The extension of the C language to include work with objectsand to interact with the OpenGL API.

(8.4.1.7) Java

(8.4.1.8) OpenInventor - It is both a graphics library and a graphicsprogramming model based on OpenGL and developed by SGI.

(8.4.2) Graphics Systems - modeling

(8.4.2.1) A number of high level modeling systems have been developed andare used extensively in the entertainment industry to create the complexmodels, render them, and produce animations and simulations. The modelingsystems are based on surface models and have a rich set of tools and featuresto produce very complex models. They are also able to interface with othergraphics systems to produce special effects so important to the entertainment. (8.4.2.2) 3D Studio(8.4.2.3) Wavefront(8.4.2.4) SoftImage(8.4.2.5) MacroMedia Director(8.4.2.6) Multigen

(8.4.3) Graphics Systems - rendering

(8.4.3.1) Lightscape(8.4.3.2) Blue Moon (free)(8.4.3.3) Radiance(8.4.3.4) POVRay (free)

(8.4.4) General Visualization Systems:

(8.4.4.1) IRIS Explorer (NAG)(8.4.4.2) Data Explorer (DX)(8.4.4.3) Ensight (MPGS)(8.4.4.4) Khoros(8.4.4.5) IDL(8.4.4.6) SpyGlass (Fortner Research)(8.4.4.7) Vis5D

(8.4.5) Symbolic Manipulation Systems

(8.4.5.1) MATLAB(8.4.5.2) Maple(8.4.5.3) Mathematica

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(8.4.6) Automated Systems

(8.4.6.1) SAGE, VISAGE(8.4.6.2) APT(8.4.6.3) AutoVisual(8.4.6.4) BOZ (Casner)

(8.4.7) spreadsheets

(8.4.8) Paint/Draw Programs

(8.5) Specialized Systems

(8.5.1) GIS Systems, e.g. Arc/Info and ArcView (ESRI), MGE (Intergraph), SmallWorld Systems (SWS), GRASS

(8.5.2) Data Mining, e.g. MineSet, DataMine, Clementine, NetMAP

(8.5.3) CAD Systeme, e.g. Catia, Solidworks

(8.5.4) Bimolecular

(8.5.5) Chemistry

(8.5.6) Physics

(8.5.7) Mathematics

(8.5.8) Statistics, e.g. S, S plus, SAS

(8.5.9) CFD, e.g. FAST

(8.5.10) Web Visualization, e.g. MS Frontpage

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3D Studio

What is 3D Studio?

3D Studio is a 3D modeling, rendering and animation package from Kinetix, which allow users tocreate three-dimensional, photo-realistic images for a variety of purposes. 3D Studio allowsprofesionell 3D artwork on PC systems.

More about 3D Studio:

Kinetix 3D Studio HomePage (http://www.ktx.com/3dsr4/)

All you need to know about 3DStudio. Download a free demo of 3D Studiofor MS-Windows

Autodesk HomePage (http://www.autodesk.com/)

HomePage of Autodesk, thedeveloper of 3D Studio. Information about the whole 3DStudio product family and otherCAD Software.

Tutorial Links (http://3dstudiomax.virtualave.net/tutors.html)A huge amount of tutorial links for3D Studio.

Gallery (http://rhino3d.com/gallery/architecture/gallery_index.htm)

Many Models rendered withSoftImage and 3dStudio (modelledwith Rhino3d)

Galleries (http://3dsearch.hypermart.net/Galleries/3DStudio/)Many links to 3dStudio-Galleries

Back

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http://www.ktx.com/3dsr4/

http://www.autodesk.com/

http://3dstudiomax.virtualave.net/tutors.html

http://rhino3d.com/gallery/architecture/gallery_index.htm

http://3dsearch.hypermart.net/Galleries/3DStudio/

Arc/Info

What is Arc/Info?

ARC/INFO is the world’s leading desktop geographic information system (GIS). ARC/INFO offersyou sophisticated GIS software tools for the management, analysis, display, and mapping ofgeographic information on Intel-based personal computers. ARC/INFO is a series of six integratedsoftware modules that combine basic GIS tools and utilities for cartographic design and query, dataentry and editing, raster and vector data translation, polygon overlay and buffering, and networkanalysis and modeling. Supported Platforms: Win32

More about ArcInfo:

Arc/Info HomePage (http://www.esri.com/software/arcinfo/index.html)

Arc/Info HomePage with newestInformation.

ESRI HomePage (http://www.esri.com/)Arc/Info is developed by ESRI. Getsome information about other ESRIproducts.

FAQ (http://www.esri.com/base/products/pcarc/faq.html)Frequently asked questions related toArc/Info

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http://www.esri.com/software/arcinfo/index.html

http://www.esri.com/

http://www.esri.com/base/products/pcarc/faq.html

ArcView

What is ArcView?

ArcView GIS is a desktop geographic information system (GIS) that allows you to organize your data,ask questions of your data using digital maps, and create new geographic data from your data.ArcView empowers users, either stand-alone or networked, to ask and answer questions of theirdatabases and maps.Supported Platforms: Win32, MAC, UNIX

More about ArcView:

ArcView HomePage (http://www.esri.com/base/products/arcview/arcview.html)

ArcView HomePage with newestinformation.

ESRI HomePage (http://www.esri.com/)ArcView is developed by ESRI.Get information about otherproducts of ESRI.

FAQ (http://www.esri.com/base/products/arcview/faq.html)Frequenty Asked Questionsrelated to ArcView.

Back

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http://www.esri.com/base/products/arcview/arcview.html

http://www.esri.com/

http://www.esri.com/base/products/arcview/faq.html

AutoVisual

What is AutoVisual?

Interactive visualization systems provide a powerful means to explore complex data, especially whencoupled with 3D interaction and display devices to produce virtual worlds. Although designing aquality static 2D visualization is already a difficult task for most users, designing an interactive 3Done is even more challenging. To address this problem, AutoVisual is developed, a rule-based systemthat designs interactive virtual worlds for visualizing and exploring multivariate relations. AutoVisualdesign interactive visualizations for n-Vision, a real-time, 3D, interactive visualization system forexploring multivariate functions. The primary interaction metaphor in n-Vision is worlds withinworlds, an interactive visualization technique that exploits nested, heterogeneous coordinate systemsto map multiple variables onto each of the three spatial dimensions. AutoVisual’s design process isguided by user-specified visualization tasks, and by a set of design principles encoded using arule-based language.

More about AutoVisual:

AutoVisual HomePage (http://www.cs.columbia.edu/graphics/projects/AutoVisual/AutoVisual.html)

AutoVisualHomePage withnewestinformation.

Clifford Beshers (http://www.cs.columbia.edu/~beshers/)

AutoVisual onClifford BeshersHomePage(developer ofAutoVisual)

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http://www.cs.columbia.edu/graphics/projects/AutoVisual/AutoVisual.html

http://www.cs.columbia.edu/~beshers/

Test

What is ?

More about :

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BlueMoon

What is BlueMoon?

The Blue Moon Rendering Tools (BMRT) are a collection of rendering programs which adhere to theRenderMan(R) interface standard (RenderMan is a registered trademark of Pixar). The toolkit consistsof a full implementation of the RenderMan standard which supports ray tracing, radiosity, area lightsources, texture and environment mapping, programmable shading in the RenderMan ShadingLanguage, motion blur, automatic raycast shadows, CSG, depth of field, support of imager andvolume shaders, and other advanced features. The toolkit also contains quick RIB previewers (usingGL or X11) to allow "pencil tests" of scenes and animations. BMRT is a shareware (for noncommercial use free) RenderMan-compliant, ray tracing and radiosity renderer. Supported platforms are: SGI, Linux, SUN, Win32, NextStep

More about BlueMoon:

BlueMoon HomePage (http://www.seas.gwu.edu/student/gritz/index.html)

The HomePage of Larry Gritz theauthor of BlueMoon.

User Manual (http://www.bmrt.org/bmrtdoc/index.html) Online User Manual.

BlueMoon Download (ftp://ftp.seas.gwu.edu/pub/graphics/BMRT/)

FTP-Site: Download the FREEBlueMoon software here.

Gallery (http://www.bmrt.org/bmrtgallery.html)Some Images rendered with theBlue Moon Rendering Tools

RenderMan (http://www.pixar.com/) Pixar HomePage

Back

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http://www.seas.gwu.edu/student/gritz/index.html

http://www.bmrt.org/bmrtdoc/index.html

ftp://ftp.seas.gwu.edu/pub/graphics/BMRT/

http://www.bmrt.org/bmrtgallery.html

http://www.pixar.com/

Catia

What is Catia?

IBM’s CATIA-CADAM product Family is a (3D) graphik software package including tools for:

mechanical design Shape Design & Styling Solutions Analysis & Simulation Manufacturing Infrastructure AEC Plant / Shipbuilding

More about Catia:

The Catia Homepage from IBM (http://www.catia.ibm.com/)

CATIA IBM Engineering Solutions Homepage

Gallery (http://www.catia.ibm.com/imagal/gallery.html)

The Catia Image Gallery

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http://www.catia.ibm.com/

http://www.catia.ibm.com/imagal/gallery.html

Clementine

What is Clementine?

Clementine is a "data mining" tool - it helps you find vital information within your data and deploythis new knowledge in areas such as estimation, forecasting, classification, diagnosis and decisionsupport. Clementine is like an Executive Information System (EIS) - it allows you to extract selecteddata, manipulate it, and visualise trends and relationships. Unlike most EISs, though, Clementine isopen and end-user configurable. And unlike traditional EISs, it uses machine learning techniques to"mine" knowledge from raw data. Yet for all its power, Clementine is easy to use. Thanks to its simple"visual programming" interface, Clementine takes less time to master than a spreadsheet. Clementineallows you to discover the high-value information implicit in your data.Supported Platforms: SUN, HP, SGI, ALPHA-UNIX, NT

More about Clementine:

Clementine (http://www.isl.co.uk/topclem.html) Clementine HomePage with newest information.

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http://www.isl.co.uk/topclem.html

Datamine

What is Datamine?

DataMine is a datamining software with the two main programs DATAMINE and GUIDE, whichcomplement each other. These are both built around a central Core. Within DATAMINE, the Coreprovides excellent data management through a proprietary relational database system, with extensivegraphics, statistics and drillhole data management. It also provides a comprehensive macro and menulanguage, with full screen user-definable layout and prompts. The Core of GUIDE is an interactivegraphics environment for the display and manipulation of drillholes, block (cell) models, surfacewireframe models (Digital Terrain Models), solid wireframe models, points and strings. It includesmany CAD features and full digitising, string manipulation and interactive plotting functions. ThusDATAMINE is used essentially as the system ’number cruncher’ while GUIDE provides the fullyinteractive 3-D graphics environment. However, there is a deliberate overlap between DATAMINEand GUIDE. This allows users to select the environment most suitable for their particular applications.

More about Datamine:

Datamine (http://www.datamine.co.uk/) Datamine HomePage with the newest information.

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http://www.datamine.co.uk/

Data Explorer

What is Data Explorer?

The IBM Visualization Data Explorer is a general-purpose software package for data visualization andanalysis. It employs a data-flow driven client-server execution model and provides a graphicalprogram editor that allows the user to create a visualization using a point and click interface. Supported platforms are: major Unix, Win32, multiprocessor systems from SUN, SGI, IBM

More about Data Explorer:

Data Explorer HomePage (http://www.research.ibm.com/dx/)

IBM’s Data Exploere HomePagewith newest information and adownloadable trial version of theData Explorer.

Data Explorer Repository (http://www.tc.cornell.edu/DX/)Collection of Data Explorerrelated web sites.

Data Explorer Repository (http://ftp.cs.umt.edu/DX/dx.home.html)

Another Repository.

Documentation (http://www.research.ibm.com/dx/docs/legacyhtml/index.html)

Online Documentation.

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http://www.research.ibm.com/dx/

http://www.tc.cornell.edu/DX/

http://ftp.cs.umt.edu/DX/dx.home.html

http://www.research.ibm.com/dx/docs/legacyhtml/index.html

Excel

What is Excel?

Microsoft Excel is a "spreadsheet application" which means that it is designed to help the usermanipul numerical data (such as budgets, research data analysis, etc.). Excel is designed to quicklyaccurately perform complex and tedious calculations. Excel can help automate the process ofcalculating your data as well as lay it out in a more appealing and meaningful way. MicrosoftWindows is the only one supported platform.

More about Excel:

MS-Execl HomePage (http://www.microsoft.com/excel/)

Microsoft Hompage with newestinformationon Excel.

Tutorial (http://www.lacher.com/toc.htm) Online Tutorial on Excel.

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http://www.microsoft.com/excel/

http://www.lacher.com/toc.htm

FrontPage

What is FrontPage?

Microsoft FrontPage is a very powerfull Web site administrating tool. With more than 30 built-intemplates and wizards you are able to create individual pages easily. With the FrontPageWYSIWYG-Editor, there is no need to know HTML. Insert hyperlinks and add information fromMicrosoft Office and other sources with drag-and-drop simplicity. With the graphical tools in theFrontPage Explorer you can easily manage your whole Web site. Microsoft Windows is the only one supported platform.

More about FrontPage:

MS-Frontpage HomePage (http://www.microsoft.com/frontpage/)

Microsoft Hompage with newestinformation on Frontpage.

FAQ’s (http://www.pmpcs.com/support/fp/faqs.htm)A link list to webpages withFrequently Asked Questions.

FrontPage Tutorial (http://www.wsabstract.com/frontpage.htm)

Online-Tutorial on Frontpage.

Tips and HOWTOS (http://www.pmpcs.com/support/fp/howto.htm)

Tips ’n tricks and HOWTOS onFrontPage

Frontpage Links (http://www.jazzpiano.com/frontpage97/index.htm)

WebSite with a lot of FrontPagerelated links.

microsoft.public.de.german.frontpage98 microsoft.public.frontpage.client microsoft.public.frontpage.extensions.unix microsoft.public.frontpage.extensions.windowsnt

NewsGroups related to FrontPage

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http://www.microsoft.com/frontpage/

http://www.pmpcs.com/support/fp/faqs.htm

http://www.wsabstract.com/frontpage.htm

http://www.pmpcs.com/support/fp/howto.htm

http://www.jazzpiano.com/frontpage97/index.htm

GRASS

What is GRASS?

GRASS (Geographic Resources Analysis Support System) is a public domain raster based GIS, vectorGIS, image processing system, and graphics production system. Written by the US Army Corps ofEngineers, it is used extensively at government offices, universities and commercial organisationsthroughout the world. It is written mostly in C for various UNIX based machines. GRASS containsover 40 programs to render images on monitor and paper; over 60 raster manipulation programs; over30 vector manipulation programs; nearly 30 multi-spectral image processing manipulation programs;16 data management programs; and 6 point file management programs. GRASS’ strengths lie in several fields. The simple user interface makes it an ideal platform for thoselearning about GIS for the first time. Users wishing to write their own code can do so by examiningexisting source code, interfacing with the documented GIS libraries, and by using the GRASSProgrammers’ Manual. This allows more sophisticated functionality to be fully integrated within GRASS. Supported Platforms: Win32, Linux, UNIX, MAC

More about GRASS:

GRASS HomePage (http://www.geog.uni-hannover.de/grass/index2.html)

Official GRASS GISHomepage Europe

Download (http://www.geog.uni-hannover.de/grass/download.html)

Download the latest version ofGRASS for free.

FAQ (http://www.geog.uni-hannover.de/grass/faq/index.html)Frequently Asked Questionsrelated to GRASS.

LinkPage (http://www.geog.uni-hannover.de/grass/links.html) Links and Screenshots

Back

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http://www.geog.uni-hannover.de/grass/index2.html

http://www.geog.uni-hannover.de/grass/download.html

http://www.geog.uni-hannover.de/grass/faq/index.html

http://www.geog.uni-hannover.de/grass/links.html

IDL

What is IDL?

IDL, Interactive Data analysis Language, is a complete package for the interactive reduction, analysis,and visualization of scientific data and images. Optimized for the workstation environment, IDLintegrates a responsive array oriented language with numerous data analysis methods and an extensivevariety of two or three dimensional displays into a powerfull tool for resarchers. IDL is useful inphysics, astronomy, image and signal processing, mapping, medical imaging, statistics, and othertechnical disciplines requiring visualiation of large amounts of data.

More about IDL:

IDL at NCSA (http://zonker.ncsa.uiuc.edu/docs/viz/Idl/) IDL HomePage.

Gallery (http://www.rsinc.com/gallery/index.cfm?product=IDL) IDL Images

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http://zonker.ncsa.uiuc.edu/docs/viz/Idl/

http://www.rsinc.com/gallery/index.cfm?product=IDL

IRIS Explorer

What is IRIS Explorer?

Explorer is a system for creating powerful visualization maps, each of which comprises a series ofsmall software tools, called modules. A map is a collection of modules that carries out a series ofrelated operations on a dataset and produces a visual representation of the result. Explorer is a systemfor creating custom visualization programs and applications in the fields of fluid dynamics, chemistry,meteorology, cosmology, physics and mathematics. Supportet platforms are: SGI, Intel NT, Alpha NT, HP, Solaris

More about IRIS Explorer:

IRIS Explorer HomePage (http://www.nag.co.uk/Welcome_IEC.html)The IRIS ExplorerHomPage with newestinformation.

Tutorial (http://www.nag.co.uk/visual/ie/talk/stanford.980129/paper/hepvis.html)

Introduction in DataVisualization usingIRIS Explorer

Another Tutorial (http://www.scs.leeds.ac.uk/iecoe/tutorial/main-frm.html)

University of Leeds

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http://www.nag.co.uk/Welcome_IEC.html

http://www.nag.co.uk/visual/ie/talk/stanford.980129/paper/hepvis.html

http://www.scs.leeds.ac.uk/iecoe/tutorial/main-frm.html

Java 3D

What is Java 3D?

The Java 3D API is an application programming interface used for writing three-dimensional graphicsapplications and 3D applets. It gives developers high level constructs for creating and manipulating3D geometry and for constructing the structures used in rendering that geometry Applicationdevelopers can describe very large virtual worlds using these constructs, which provide Java 3D withenough information to render these worlds efficiently. Java 3D is a library package for JAVA.

More about Java 3D:

The Java 3D Repository (http://java3d.sdsc.edu/)

Start yoursearch forJava 3DrelatedWWW-Pageshere.

Java 3D API Specification (http://www.javasoft.com/products/java-media/3D/forDevelopers/3Dguide/j3dTOC.doc.html)

The libraryspecification.

JAVA MEDIA Framework API (http://java.sun.com/products/java-media/3D/index.html)

Get Java 3Das a part ofthe JAVAMEDIAFrameworkAPI.

Java on SUN Microsystems (http://java.sun.com/)Moreinformationabout JAVA.

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http://java3d.sdsc.edu/

http://www.javasoft.com/products/java-media/3D/forDevelopers/3Dguide/j3dTOC.doc.html

http://java.sun.com/products/java-media/3D/index.html

http://java.sun.com/

Khoros

What is Khoros?

Khoros is software for data processing, data exploration and visualization, image processing, andsoftware development. As a complete software environment, Khoros lets you work more efficientlythan any combination of end-user applications. Khoros includes a visual simulation and programminglanguage environment, a suite of software development tools, an interactive user interface editor, aninteractive image display package and an extensive collection of image processing, data manipulation,scientific visualization, geometry, and matrix operators.

More about Khoros:

Khoros HomePage (http://www.khoral.com/)The KhorosHomePage withnewest information.

FAQ (http://www.khoral.com/support/faqs/)Frequently AskedQuestions relatedKhoros.

Manual (http://www.tnt.uni-hannover.de/soft/imgproc/khoros/khoros1/manual.html)

Online MAnual

Short Discription (http://www.uni-koeln.de/themen/Graphik/ImageProcessing/Khoros/)

Signal- undBildverarbeitung mitKhoros 2.1 (miteinigen Screenshots)

Links (http://www.iam.unibe.ch/~fkiwww/Khoros/index.html#links)Links to Khorosrelated web sites.

comp.soft-sys.khorosNewsGroup relatedto Khoros

Course (http://www.eece.napier.ac.uk/dip_course_alpha/html/dip.html)Digital ImageProcessing withKhoros 2.0

Back

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http://www.khoral.com/

http://www.khoral.com/support/faqs/

http://www.tnt.uni-hannover.de/soft/imgproc/khoros/khoros1/manual.html

http://www.uni-koeln.de/themen/Graphik/ImageProcessing/Khoros/

http://www.iam.unibe.ch/~fkiwww/Khoros/index.html#links

http://www.eece.napier.ac.uk/dip_course_alpha/html/dip.html

LightScape

What is LightScape?

Lightscape is the first software product to combine radiosity and ray tracing with physically basedlighting. This gives you unprecedented design realism, interactivity and control. Export Lightscape files directly from 3D Studio MAX® with our translator plug-in, orimport DXF or 3D Studio® R4 files. Then put Lightscape to work. Subtle lighting effects, such as softshadows and color bleeding, make Lightscape renderings the best representations of reality available.Whether you’re involved in architecture, interior design or lighting design, Lightscape lets youcommunicate your ideas with a level of realism never before possible. Supported platforms: Windows NT, SGI

More about LightScape:

LightScape HomePage (http://www.lightscape.com/)The LightScape HomePage with newestinformation. Download a free viewer.

Lightscape Gallery (http://www.lightscape.com/gallery/)

Some Pictures rendered with Lightscape

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http://www.lightscape.com/

http://www.lightscape.com/gallery/

MacroMedia Director

What is MacroMedia Director?

The MacroMedia Director 6 provides everything you need to create professional multimedia projects,from corporate presentations and engaging Web pages to interactive advertising, kiosks, CD titles, andShocked CDs (hybrid CD+Internet applications). The Director features tightly integrated tools forcreating 2D and 3D images, sound, animation, and a complete authoring environment with the mostadvanced Web authoring features available.

More about MacroMedia Director:

MacroMedia HomePage (http://www.macromedia.com/)The MacroMedia DirectorHomePage with the newestinformation on MacroMedia.

FAQ (http://hakatai.mcli.dist.maricopa.edu/director/faq/index.html)

MacroMedia related FrequentlyAsked Questions.

GalleryFew Links to interactive websitesusing Macromedia Director

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http://www.macromedia.com/

http://hakatai.mcli.dist.maricopa.edu/director/faq/index.html

Maple

What is Maple?

Maple is a powerful interactive computer algebra system that provides a complete mathematicalenvironment for the manipulation of symbolic albebra expressions, arbrtrary-precision numerics,graphics, and programming. Maple V’s library features over 2700 functions that are used in manyscientific and engineering applications.Supported platforms: MAC, MS-Windows, major UNIX and Linux

More about Maple:

MapleSoft (http://www.maplesoft.com/)Maple HomePage with newestinformation and a free downloadabledemo.

Introduction to Maple V (http://www.math.uic.edu/maple/labs/index.html)

Online Tutorial for Maple V

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http://www.maplesoft.com/

http://www.math.uic.edu/maple/labs/index.html

Mathematica

What is Mathematica?

Mathematica is a software system and computer language for use in mathematical applications. Thethree classes of Mathematica computations are: numerical, symbolic, and graphical. Mathematica canbe used as a calculator with a much higher degree of precision than traditional calculators.Mathematica can perform operations on functions, manipulate algebraic formulas, and do calculus.Mathematica is also able to produce both two- and three-dimensional graphs. Mathematica supports itsown high-level programming language.

More about Mathematica:

Mathematica HomePage (http://www.wri.com/)Mathematica Hompage with newest informationand a downloadable free demo.

Tutorial (http://saaz.lanl.gov/math/Math_Home.html)

Online tutorial to Mathematica.

FAQ (http://www.wri.com/FAQs/)Frequently Asked Questions related toMathematica.

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http://www.wri.com/

http://saaz.lanl.gov/math/Math_Home.html

http://www.wri.com/FAQs/

Matlab

What is Matlab?

MATLAB is an interactive, matrix-based system for scientific and engineering calculations. You cansolve complex numerical problems without actually writing a program. The name MATLAB is anabbreviation for MATrix LABoratory. Supported Platforms are: MAC, DOS, UNIX, OS/2, VMS, Win32

More about Matlab:

Matlab HomePage (http://www.mathworks.com/)Matlab Hompage withnewest information.

MatLab HomePage (http://www.cis.yale.edu/MatLab) MatLab at Yale

Documentation (http://www.mathworks.com/access/helpdesk/help/helpdesk.shtml)

MathworkDocumentation (helpdesk)

comp.soft-sys.matlabNewsGroup related toMatlab

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http://www.mathworks.com/

http://www.cis.yale.edu/MatLab

http://www.mathworks.com/access/helpdesk/help/helpdesk.shtml

MGE

What is MGE?

Intergraph’s flagship GIS product, MGE meets all the spatial data needs of an enterprise. MGEimproves GIS workflows by enabling users to view and analyze digital data and by adding smartfeatures to CAD data. MGE is used by the transportation, utilities and natural resources industries, andby various state and local governments. The program allows users to input, manage, analyze andpresent data, and with the addition of ODBC support, expands users’ options for storing and usingdata. In addition to ODBC support, RIS is still supported in MGE 7.0. Supported Platforms: WinNT, UNIX

More about MGE:

MGE Intergraph HomePage (http://www.intergraph.com/mge/default.htm)

MGE on Intergraph HomePage withnewest information.

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http://www.intergraph.com/mge/default.htm

MineSet

What is MineSet?

MineSet 2.0 is Silicon Graphics’ award winning flagship product for data mining and visualization.Combining powerful integrated, interactive tools for data access and transformation, analytical datamining, and visual data mining, MineSet provides you with a revolutionary paradigm for knowledgediscovery that will give you the maximum value from your vast data resources. MineSet enables youto gain a deeper and more intuitive understanding of your data by helping you to discover hiddenpatterns, important trends, and new knowledge. Unleash the power of your creativity and reduce thetime to insight. The only supported platform is SGI.

More about MineSet:

MineSet HomePage (http://www.sgi.com/software/mineset/)

Silicon GraphicsMineSetHomePage withnewestinformation anda downloadablefree evaluationcopy.

Tutorial and Demos (http://www.sgi.com/software/mineset/demos.html)PostscriptTutorial andMineSet-Movies

Documentation (http://techpubs.sgi.com/library/tpl/cgi-bin/browse.cgi?coll=0650&db=bks&cmd=toc&pth=/SGI_EndUser/MineSet_UG_A)

Online User’sGuide

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http://www.sgi.com/software/mineset/

http://www.sgi.com/software/mineset/demos.html

http://techpubs.sgi.com/library/tpl/cgi-bin/browse.cgi?coll=0650&db=bks&cmd=toc&pth=/SGI_EndUser/MineSet_UG_A

MuPad

What is MuPad?

MuPad is computer algebra system developed by the University Paderborn. MuPad is a mathematicalenvorionent for manipulation of symbolic expressions and graphics. It’s very similar to Maple. MuPadcomes with an own programming language to implement complex mathematical algorithmsSupported platrorms are: MAC, MS-Windows, major UNIX, Linux and Amiga (alpha-Version)

More about MuPad:

MuPad Homepage (http://www.mupad.de/)

All you need to know about MuPad. Download a for non-commercial use FREE copy ofMuPad

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http://www.mupad.de/

Netmap

What is Netmap?

NETMAP is a general purpose, information visualisation tool which supports analysts in the processof identifying patterns, trends and targets from multiple data sources. NETMAP has been used inmany industry sectors during its history as a Data Mining tool. It has proved most effective in miningpotential fraud cases from large qualitative, text based datasets distributed across large corporatenetworks. It is in this area that most of the installed base of users exists, particularly within the USGovernment. NETMAP has also been used for many years as an organisational analysis tool todiscover, by means of a questionnaire, the hidden informal teams working within large organisations.Supported platforms: HP, SUN, DEC, IB, NT (BETA)

More about Netmap:

Netmap HomePage (http://www.altaanalytics.com/) NetMap HomePage with newest information.

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http://www.altaanalytics.com/

OpenGL

What is OpenGL?

OpenGL is a software interface for applications to generate interactive 2D and 3D computer graphics.OpenGL is designed to be independent of operating system, window system, and hardware operations,and it is supported by many vendors. OpenGL is available on PCs and workstations. Using theOpenGL library is free. Licensing only for including OpenGL in other system software.

More about OpenGL:

OpenGL HomePage (http://www.opengl.org/)Here you can get a languagespezification, tutorials and downloadoptions.

SGI-OpenGL HomePage (http://www.sgi.com/software/opengl/)

Courses and documentation, alsoinformations about OpenGL-Optimizerand Fahrenheit

Books on OpenGL (http://www.opengl.org/Documentation/Books.html)

OpenGL Programming Guides

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http://www.opengl.org/

http://www.sgi.com/software/opengl/

http://www.opengl.org/Documentation/Books.html

Open Inventor

What is Open Inventor?

Open Inventor is an object-oriented 3D toolkit offering a comprehensive solution to interactivegraphics programming problems. It presents a programming model based on a 3D scene database thatdramatically simplifies graphics programming. It includes a rich set of objects such as cubes,polygons, text, materials, cameras, lights, trackballs, handle boxes, 3D viewers, and editors that speedup your programming time and extend your 3D programming capabilities. Open Inventor serves as thebasis for the VRML (Virtual Reality Modeling Language) standard. Open Inventor is a high levellibrary at the base of OpenGL. Implementations of Open Inventor are availible for all major UNIX systems and all MS-WINDOWS versions.

More about OpenGL:

Open Inventor HomePage from SGI (http://www.sgi.com/Technology/Inventor/)

All you need to know aboutOpen Inventor.

Documentation (http://www.sgi.com/Technology/Inventor/doc.html)Open Inventor libraryspezification Online Tutorial

Open Inventor from TGS (http://www.tgs.com/Products/openinv-index.html)

Open Inventor 2.5

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http://www.sgi.com/Technology/Inventor/

http://www.sgi.com/Technology/Inventor/doc.html

http://www.tgs.com/Products/openinv-index.html

POVRay

What is POVRay?

POV is a 3-dimensional raytracing engine. It takes information you supply and simulates the way lightinteracts with the objects you’ve defined to create stunning 3d pictures and animation. In addition tothe process known as "raytracing," newer versions of POV can also use a variant of the process knownas "radiosity" to add greater realisim to scenes, particularly those which use diffuse light such as thefluorescent lighting you might find in an office building. POVRay is free. The sourcecode for POVRay is also availible.

More about POVRay:

POVRay User Documentation (http://www.povray.org/docs/) Online Tutorial for POVRay

Povzine (http://www.povray.org/povzine/index.html)The electronic magazine for allPOVRay users.

PovRay HomePage (http://www.povray.org/)

HomePage of PoyRay withnewest information.Download POVRay forMS-Windows, MAC, SUN,Amiga, Linux

Friedemann’s POV-Ray-Gallery (http://home.fhtw-berlin.de/~s0049669/gallerye.html)

Images rendered with POV-Ray

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http://www.povray.org/docs/

http://www.povray.org/povzine/index.html

http://www.povray.org/

http://home.fhtw-berlin.de/~s0049669/gallerye.html

Radiance

What is Radiance?

Radiance is a suite of programs for the analysis and visualization of lighting in design. Input filesspecify the scene geometry, materials, luminaires, time, date and sky conditions (for daylightcalculations). Calculated values include spectral radiance (ie. luminance + color), irradiance(illuminance + color) and glare indices. Simulation results may be displayed as color images,numerical values and contour plots. The primary advantage of Radiance over simpler lightingcalculation and rendering tools is that there are no limitations on the geometry or the materials thatmay be simulated. Radiance is used by architects and engineers to predict illumination, visual qualityand appearance of innovative design spaces, and by researchers to evaluate new lighting anddaylighting technologies. Radiance is free. Supported Platforms are: SGI, SUN

More about Radiance:

Radiance Hompage (http://radsite.lbl.gov/radiance/HOME.html)

The Radiance Hompage with newestinformation and online tutorials.

Download Radiance (ftp://radsite.lbl.gov/rad/) FTP-Distributon Site for Radiance.

Gallery (http://radsite.lbl.gov/radiance/frameg.html)Radiance rendered images showingbuildings, object and impressive lighting

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http://radsite.lbl.gov/radiance/HOME.html

ftp://radsite.lbl.gov/rad/

http://radsite.lbl.gov/radiance/frameg.html

Smallworlds

What is Smallworlds?

A Geographic Information System (GIS) is a set of software and hardware tools that perform spatialanalysis and produce geographic displays, such as maps, using a database that models the real world.Today these systems make vast amounts of data available to many users at many locations and formany purposes. The data includes:

Locational data such as streets, rivers or contours Facility data such as buildings, water or gas mains, or electric or telephone lines Attributes related to either the location or the facility data

Many GIS systems are map-oriented. Today there is a new generation of GIS -- the Smallworldgeneration. The Smallworld GIS uses powerful object-oriented technology to produce real bottom lineresults for users in many disciplines, such as electric and gas utilities, water utilities,telecommunications companies, business geographics, public works and many more. The Smallworldgeneration has already proven its extraordinary value in multiple arenas. Smallworld functions as:

A spatial data management tool An analytical instrument An unusually effective communications vehicle

Running on UNIX and Windows NT operating systems, it is hardware independent and rigorouslyadheres to software standards to achieve a true open systems architecture. This enables integrationwith other systems that has previously been impractical.

More about Smallworlds:

Smallworld HomePage (http://www.smallworld-us.com/)

Smallworld HomePage with newest information.

German Smallworld HomePage (http://www.smallworld.de/)

Informations about Products, German Partnersetc.

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http://www.smallworld-us.com/

http://www.smallworld.de/

SoftImage

What is SoftImage?

SoftImage is a professionel 3D rendering tool.Supported platforms are: Windows NT, SGI

More about SoftImage:

SoftImage Hompage (http://www.softimage.com/)The SoftImage HomePage withnewest information on SoftImage.

General SoftImage HomePage (http://www.3drender.com/) SoftImage related WebPage.

Tutorial (http://www.lumis.com/softimage/tutorials/)Site with links to tutorials onSoftImage.

Links (http://vizlab.beckman.uiuc.edu/softimage/links.html)This Site contains a lot of links toSoftImage related pages.

Gallery (http://rhino3d.com/gallery/architecture/gallery_index.htm)

Many Models rendered withSoftImage and 3dStudio(modelled with Rhino3d)

Mailing-List-Archive (http://vizlab.beckman.uiuc.edu/softimage/mail-archive/)

SoftImage Mailing-List. Contactother SoftImage users andsubscribe in the Mail-Server.

comp.graphics.apps.softimageA NewsGroup related toSoftimage.

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http://www.softimage.com/

http://www.3drender.com/

http://www.lumis.com/softimage/tutorials/

http://vizlab.beckman.uiuc.edu/softimage/links.html

http://rhino3d.com/gallery/architecture/gallery_index.htm

http://vizlab.beckman.uiuc.edu/softimage/mail-archive/

Solidworks

What is Solidworks?

SolidWorks Corporation addresses the needs of mainstream mechanical design engineers for asolution that handles complex design tasks easily, accurately, and affordably. The main goal is to putthe power of production solid modeling on the desktop of every engineer. SolidWorks is a CAD software which is focused on the MS-Windows platform.

More about Solidworks:

Solidworks (http://www.solidworks.com/)

HomePage of Solidworks with newestinformation andFREE File-Viewer-Plugin for your InternetBrowser

SolidGallery (http://www.solidworks.com/html/gallery.htm)

The Solidworks Gallery contains over 70case studies

Newsgroup Newsgroup related to Solidworks

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http://www.solidworks.com/

http://www.solidworks.com/html/gallery.htm

Vis5D

What is Vis5D?

Vis5D is a software system for visualizing data made by numerical weather models and similarsources. Vis5D works on data in the form of a five dimensional rectangle. That is, the data are realnumbers at each point of a "grid" which spans three space dimensions, one time dimension and adimension for enumerating multiple physical variables. Of course, Vis5D works perfectly well ondata sets with only one variable or one time step (i.e. no time dynamics). However, your data shouldhave some depth in all three spatial dimensions. Vis5D is free. Supported platforms are: SGI,IBM, HP, DEC, SUN, LINUX

More about Vis5D:

Vis5D HomePage (http://www.ssec.wisc.edu/~billh/vis.html)Vis5D HomePage with newestinformation.

Documentation (ftp://iris.ssec.wisc.edu/pub/vis5d-4.3/README)README-File of the Vis5DDistribution in ASCII

Download (http://www.ssec.wisc.edu/~billh/view5d.html) Download-Site for Vis5D

Example (http://www.cita.utoronto.ca/~armitage/vis5d/vis5d.html)ZEUS simulations of thestream-disk interaction

Another Example (http://www.scinet.prairie.edu/Dem2v5d/MapImages.html)

Wichita Mountain WildlifeRefuge

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http://www.ssec.wisc.edu/~billh/vis.html

ftp://iris.ssec.wisc.edu/pub/vis5d-4.3/README

http://www.ssec.wisc.edu/~billh/view5d.html

http://www.cita.utoronto.ca/~armitage/vis5d/vis5d.html

http://www.scinet.prairie.edu/Dem2v5d/MapImages.html

Visage

What is Visage?

Visage is a novel and powerful environment for exploring, analyzing, and visualizing information.Visage’s information-centric approach enables people to create custom visualizations and interactivepresentations by directly manipulating data from any source or of any type.

More about Visage:

Visage HomePage (http://www.maya.com/visage/)

Visage HomePage on MAYA with newestinformation.

The Sage Visualization Group (http://www.cs.cmu.edu/~sage/)

Everything about the Sage Visualization Project.

Visage Samples (http://www.cs.cmu.edu/~sage/visagedd.html)

The Sage Visualization Group at the CarnegieMellon University

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http://www.maya.com/visage/

http://www.cs.cmu.edu/~sage/

http://www.cs.cmu.edu/~sage/visagedd.html

VRML

What is VRML?

VRML is an acronym for Virtual Reality Modeling Language. VRML is the file format standard for3D multimedia and shared virtual worlds on the Internet. This language is used by applicationdevelopers and content creators to implement interactive 3D graphics and multimedia content that canbe published on the Internet and on stand-alone computers. Just as HyperText Markup Language(HTML) led to a population explosion on the Internet by implementing a graphical interface, VRMLadds the next level of interaction, structured graphics, an extra dimension (z-axis), and time to theonline experience.

More about VRML:

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Silicon Graphics Incorporated (http://vrml.sgi.com/)

SGI is the maindeveloper of VRMLrelated programms.Here you can find areport on currentaffairs, a VRMLGallery, varioussoftware and muchmore about VRML.

Cosmo Player (http://cosmo.sgi.com/)

Free VRML-Browser.Is part of NestscapeCommunicator 4.0Professionell Edition.(Platforms: IRIX,WINDOWS) Microsoft’s InternetExplorer 4 comeswith the AltaVistaVRML-Plugin.

VRML Repository (http://www.sdsc.edu/vrml/)Start your search forVRML relatedWWW-Pages here.

VRML 2.0 Tutorial from SIGGraph 96’ (http://www.sdsc.edu/siggraph96vrml/)

A very good tutorialabout learing VRML2.0.

VRML 97 Specification (http://www.vrml.org/Specifications)The offical Languagespecification.

The Annotated VRML 2.0 Reference Manual (http://cseng.aw.com/bookdetail.qry?ISBN=0-201-41974-2&ptype=0)

This is a popularVRML2.0 handbookin HTML.

VRML Projects (http://cs.millersv.edu/cs373.dir/vrml.dir/vrml.html)

VRML Projects at theMillersvilleUniversity (MUVirtual CampusProject)

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http://vrml.sgi.com/

http://cosmo.sgi.com/

http://www.sdsc.edu/vrml/

http://www.sdsc.edu/siggraph96vrml/

http://www.vrml.org/Specifications

http://cseng.aw.com/bookdetail.qry?ISBN=0-201-41974-2&ptype=0

http://cs.millersv.edu/cs373.dir/vrml.dir/vrml.html

Wavefront

What is Wavefront?

Wavefront is a collection of graphik tools which includes:

3D software for industrial design and automotive styling animation software to create sophisticated digital imagery and explosive special effects forfeature films, broadcast television and post production. animation software for games and interactive

More about Wavefront:

Wavefront HomePage (http://www.aw.sgi.com/pages/home/index.html)

Silicon Graphics HomePagewith newest information onWavefront

Tutorial (http://www.cica.indiana.edu/cica/faq/wavefront/wavefront.html)

Online-Tutorial on Wavefront

Gallery (http://www.aw.sgi.com/pages/home/pages/site_map/index.html)

Images created withWavefront

comp.graphics.apps.wavefrontNewsgroup related toWavefront

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http://www.aw.sgi.com/pages/home/index.html

http://www.cica.indiana.edu/cica/faq/wavefront/wavefront.html

http://www.aw.sgi.com/pages/home/pages/site_map/index.html

Projects

Click here to go to the suggested data sets and projects right away

Visualization in practice is often done without strategy. 3D graphics and visualization tools are useduntil the result seems suitable. It is very important to have students of a visualization course work withreal data to generate expressive and effective visualizations. They need to learn to use mapping strategies for visualization (see Theme 4) to arrive at representations that can be interpreted orexplored quickly and accuratly. We have gathered three data sets. For each of the data sets thestudents should discuss:

Problem domain (E.g. What is the background of the problem? Will experts of certain disciplinesneed to interpret resulting pictures? Is the use of specific visual attributes, such as colors, orvisual representations "common sense" for these users? )

Data model (What do I know of the data - their origin, characteristics, default representations, ...?)

Visualization goals/tasks (What are the questions users will try to answer from the representations)?

User characteristics (What do I know of the user - desires, abilities, disabilities... ?)

Possible representations (What representations are expressive for this data model? Out of these,which are effective for the specific user and task?)

Necessary interactions (Is interaction necessary, what kind, can it be done with available hardware/software?)

Possible tools to use to generate representations (what libraries or tools will be able to helpgenerate useful visualizations, what kind of visual context will be necessary)?

How will the students know their solution is a useful (visual) representation?

before they start with programming.

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It will be gratifying to see how students improve their pictures while their knowledge on thevisualization process improves. It might be worth having them redo projects again at the end of theircourse to see what they missed the first time. Having each student (or group of students) present theirproject in front of the class, everyone will learn from other mistakes and ideas.

Have fun!

Click here for suggested data sets and projects.

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Three suggested Projects for a Visualization Course

Project One: Data Sets "IRAS"

With NASA sponsored mission IRAS (Infrared Astronomical Satellite) the entire sky was mapped in1983 at four far-infrared wavelengths. Following are four data arrays (sky-flux plate 75, HCON 3)representing band 1 (12 micrometers), band 2 (25 micrometers), band 3 (60 micrometers) and band 4(100 micrometers). The images show the Milky Way through the center and Lambda Orionis at thelower right. Preprocessing of images is a courtesy of the Infrared Processing and Analysis Center.Destriping and flattening by Gitta Domik..

Each of the four data arrays has the size of 512 * 512 pixels * 1 byte.

p077h3b1.byt (band 1) p077h3b2.byt (band 2)

p077h3b3.byt (band 3) p077h3b4.byt (band 4)

Download all four data-files in IRAS.zip

Assumed task: Astronomers want to find "similar" objects in the images. Can you show "similarity"in measured data?

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Possible solution: Merge three images in an RGB or HLS transformation (e.g. map band 4 to red,band 3 to green, band 1 to blue). Similar objects obtain the same color. Examples.

Project Two: Data Set "Leaves"

The following table shows (ficticious) measurement results of the growth of leaves from threedifferent types of trees (maple, aspen and pear) at different growing periods:

Type of Tree Age of Leaf Length of Leaf Width of Leaf Maple 3 weeks 2.2 cm 1.8 cm

Maple 2 months 4.6 cm 5.5 cm

Maple 4 months 8.8 cm 10.0 cm

Aspen 3 weeks 1.2 cm 1.2 cm

Aspen 2 months 3.6 cm 3.6 cm

Aspen 4 months 7.5 cm 7.5 cm

Pear-Tree 3 weeks 3.2 cm 1.2 cm

Pear-Tree 2 months 7.0 cm 2.5 cm

Pear-Tree 4 months 11.0 cm 4.0 cm

Task: Exploration of data - let the students decide on specific tasks.

Hint: A rigorous data model (data characteristics, e.g. what variables are quantitative, ordinal,nominal) helps to avoid errors.

No examples provided.

Project Three: Data Set "Flow"

The data set is a snap shot of water flowing through a channel. Winds acting upon the (open) surfaceof the water create turbulences inside the water. Movements of water particles (caused by the winds)were calculated in a supercomputing class by Lloyd Fosdick, University of Colorado, in 1992. File "field2.irreg" contains data describing the particle movement in a 2d slice perpendicular to the lengthof the channel. Data is given for a regular 82 x 82 grid in the following format: starting position (x,y,z)and relative movement (u,v,w).

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Detailed format of field2.irreg:

Integer value Number of spatial dimensions ("3")


3 Integer valuesInteger values describing the extension in each dimension:(Dim_x,Dim_y,Dim_z)=("82, 82, 1")


Real array ofdimension (6,82,82,1)

First value "6" describes the number of data entries for each vector,followed by "x,y,z,u,v,w" as described above.

Suggested tasks:(a) Give a visual overview of the data!(b) Show Symmetry in the flow!(c) Where are the quickest / slowest movements (whithout losing sight for the whole flow)?

Discuss advantages/disadvantages/errors of the provided solutions.

Please send back any suggestions and/or complaints to [email protected]

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Example Solutions for the IRAS-Project

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Example Solutions for the Flow-Project

Video

flow.mpeg

Images

Click on an Image to see the original scale!

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Optional Topics: In-depth skills

Skills other than the ones derived from knowledge of core topics might be necessary to developexpertise in visualization, e.g. on

Mathematics, e.g. vector and matrix algebra

Computer Science, e.g. display architectures, hardware/software concepts

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Topics from Related Disciplines

Some topics that are clearly embedded in other disciplines may have a strong impact on the educationof specific visualization skills. Such topics are

Aesthetics Importance of aesthetics A theory of aesthetics

Physical and Cognitive Disabilities Perceptual Issues Cognitive Issues Other physical issus Potential solutions

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A Classification of Skills

A variety of jobs are open to visualization experts. Different skills are required for different jobs,therefore the following classification of skills should distinguish between various requirements of thejob market:

Visualization researcher: Visualization researchers perform leading edge research in all areas of thecore topics of visualization. Typical job markets are Universities or governmental or industrialresearch labs.

Application oriented researcher: Researches for expressive and effective visualizations aimed at onespecific application area and with specific contraints given. Are able to value and classify their workinside the existing research literature. Contributions to visualization conferences are typically underthe category "case studies". Job markets are governmental and industrial research labs and softwaredevelopment departments of large industrial plants.

Professional developer: Develops software for representations in a constraint environment. Typicaljob markets are software devopment departments and companies.

Professional user: User of a visualization or graphics software package with choices to make such ascolor tables, choice of representations or even choices of software packages to use.

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Matrix relating skills and topics

Theme/Topic A=All, M=most, S =some or skim

Vis-ResearcherApplication oriented Researcher

Professional Developer

Professional User

1. Introduction A M M M

1.1 History A A S S

1.2 Definitions A M M M

1.3 Sample applications A M M M

1.4 Impact of future technology

A A A S

2. Data A M M S

2.1 Examples of complexdata sets

A A A M

2.2 Data and the Worldbeing modeled

A A A A

2.3 Data processing A M M S

2.4 Data models A M M S

3. User and task A M A S

3.1. Human performance issues

A M A M

3.1.1 Perception A M A M

3.1.2 Evaluation A M A -

3.2. Visualization goals A M A S

3.2.1 Examples A M A S

3.2.2 Task analysis A M A -

4. Mapping Process A M M S

4.1 Introducing themapping process

A A A M

4.2 Strategies A A M S

4.3 Parameters in themapping process

A M M S

4.4 Difficulties in themapping process

A M A M

4.5 Visual context A A A M

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5. Representations A A A S

5.1 Catagorization of representations

A A A M

5.2 Computer graphics M M A S

5.3 Single representation techniques

A M A M

5.4 Organizational structures

A M A M

6. Interaction A S M S

6.1 Interaction flow A S M S

6.2 Interaction design A S M S

6.3 Human performance A M M S

6.4 Implementation issues A S A S

7. Visualization concepts A M A S

7.1. Models of the static process

A M A S

7.2 Models for interactive systems

A M A S

7.3 Reference model A S A S

7.4 Design issues for HWand SW

A S A -

8. Systems and tools A S A S

8.1 Categorization of systems

A A A M

8.2 Multi-purpose tools A S A S

8.3 Specialized systems A S, some in detail A S

In-depth skills Math, CG, other Math, CG, other Math, CG, other -

Related Disciplines Special needs Special needs Special needs Special needs

Theme/Topic A=All, M=most, S =some or skim

Vis-ResearcherApplication oriented Researcher

Professional Developer

Professional User

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Visualization Courses WorldWide:

Visualization Courses held throughout the US and Europe:

NORTH AMERICA:

C. R. Johnson, University of Utah Introduction to Scientific Visualization

A. J. Hanson, Indiana University (Bloomington):

Scientific Visualization

N. Ezquerra, Georgia Tech Visualization Techniques in Science and Engineering

N. L.Max, University of California, Davis


H. Levkowitz, University ofMassachusetts Lowell

Scientific Data Visualization

D. J. Bouvier, University of Arkansas Scientific Visualization

F. D. Fracchia, Simon FraserUniversity, Burnaby


N.C. Schaller, Rochester Institute of Technology

Computer Graphics Lab - Projects course

D.A. Schoenefeld, University of Tulsa Advanced Computer Graphics

J. A.Cross, Indiana University of Pennsylvania

Computer Graphics

Steve Cunningham, California StateUniversity, Stanislaus

Computer Graphics and Visualization (http://www.cs.csustan.edu/~rsc/NSF)

GERMANY:

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http://www.cs.csustan.edu/~rsc/NSF

H. Schreiter, TU Chemnitz-Zwickau

Scientific Visualization Seminar

H. Hagen, Univ. of Kaiserslautern Scientific Visualization

S. Mueller, TU Darmstadt Visualization &Virtual Reality

B. Brüderlin, TU Ilmenau Erfassung und Visualisierung von 3D-Daten

J. Encarnação,TU DarmstadtVirtuelle Realitaet- Werkzeuge fuer Echtzeit-Visualization und 3D-Interaktion

F. Nake, University of Bremen Visualization in general - Project course

H. Schumann , Univ. of Rostock Visualization of scientific data

Thomas Ertl, University of Stuttgart

Visualization

Thomas Ertl, University of Stuttgart

Advanced Visualization Techniques

R.T. Rau, University of Tuebingen


Dietmar Saupe, University of Konstanz

Summer School "Scientific and Mathematical Visualization"

Gitta Domik, University of Paderborn

Computer-Generated Visualization

AUSTRIA:

Eduard Groeller, Technical University Vienna Visualization ( lecture + lab)

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Dr. Eduard Groellerhttp://www.cg.tuwien.ac.at/staff/EduardGroeller.html (http://www.cg.tuwien.ac.at/staff/EduardGroeller.html)Institute of Computer Graphics-http://www.cg.tuwien.ac.at/courses/ (http://www.cg.tuwien.ac.at/courses/)Technical University Vienna

Title of visualization course: Visualization (lecture+lab) List topics of course/Objectives of the course:

Lecture Visualization: introduction into the concepts of scientific visualisation. Introduction (historical roots of visualisation, explanation of terms) Visualization pipeline Data and problem classification Usage of color in scientific visualization Volume visualisation Surface reconstruction Direct volume visualization Visualisation of vector data and tensors Flow visualisation

details to Lecture Visualization-http://www.cg.tuwien.ac.at/courses/Visualisierung/VO.html (http://www.cg.tuwien.ac.at/courses/Visualisierung/VO.html) Lab Visualisation: By solving small programming exercises practical experience invisualisation will be gained. Furthermore there are assignments where visualisationsystems (e.g., AVS, Mathematica) will be used to solve small visualization tasks. Onconsultation with the lecturer students can select a programming task from the field ofvisualisation themselves. details to Lab Visualisation-http://www.cg.tuwien.ac.at/courses/Visualisierung/LU.html (http://www.cg.tuwien.ac.at/courses/Visualisierung/LU.html)

Lab.: Hardware used in the lab: SGI, PC’s Software for the course: AVS, Mathematica, Visual C++

Extended Information on course

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http://www.cg.tuwien.ac.at/staff/EduardGroeller.html

http://www.cg.tuwien.ac.at/courses/

http://www.cg.tuwien.ac.at/courses/Visualisierung/VO.html

http://www.cg.tuwien.ac.at/courses/Visualisierung/VO.html

http://www.cg.tuwien.ac.at/courses/Visualisierung/LU.html

http://www.cg.tuwien.ac.at/courses/Visualisierung/LU.html

Extended information on course

General Information:

Number of students in course: 15-30

Number of hours for course: 2 hrs. lecture + 2 hrs. lab

media used for teaching your course: overhead slides,overhead display panel, black board,video.

how long "your" hour is: 45 minutes

Audience:

describe your students (undergrads, grads, actual level, their background): undergraduate, graduate, computer scientists, good background in computer graphics.

Evaluation:

written midterm and/or final: final exam (90 minutes) for the lecture (oral and a written part).

Used assignments (how many, example): 3 assignments for the lab. see details , example

evaluation scheme:

lecture: students can get up to 100 points for the final exam:

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0-49 points: fail, 50-63: pass (sufficient), 64-77: pass (satisfying),

78-90: pass (good), 91-100: pass (very good=highest grade)

Prerequisites:

Background knowledge in computer science and computer graphics.

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Visualisierung WS 96/97

Vorlesungszeiten:

Vorbesprechung und 1. Vorlesung: Dienstag 8. Oktober 1996Zeit: Di, 14:15 - 15:45Ort: HS 13, HauptgebäudeVorlesung am: 8.10, 15.10, 29.10, 5.11, 12.11, 19.11, 26.11, 3.12, 10.12, 17.12, 14.1, 21.1Achtung: die Vorlesung entfällt am 22.10 Videovorführung: Montag, 20.1.1997, InformatikHS

Prüfung

schriftlich am 28.1.1997, mündlich in der Woche danach, Anmeldelisten zur mündlichen Prüfungwerden rechtzeitig am Institut im 2. Stock, rechts neben dem Sekretariat ausgehängt,

Laborübung

Übungsablauf: Im Rahmen der Übung sind 3 Beispiele mit folgender Gewichtung zu lösen

1. Kennenlernen von AVS (Application Visualization System) 20 Punkte2. Kennenlernen von Mathematica 20 Punkte3. Lösung einer Aufgabenstellung aus dem Bereich der Visualisierung 60 Punkte

Übungsort:

Computergraphik und Pattern Recognition Laboratory (CGPR-Lab). Das CGPR-Lab befindet sich imInformatiklabor, Hauptgebäude, Stiege 5, 1. Stock.


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Laborübung: Visualisierung

Übungsangabe 1. Beispiel

Abgabetermin: Fr. 15.11.1996, 13.00-15.00

Das erste Beispiel dient dem Kennenlernen von dem Visualisierungssystem AVS (AdvancedVisualization System). Gruppen zu je 2 Studenten erhalten die Gelegenheit, an einer Silicon GraphicsGraphik-Workstation mit AVS zu arbeiten. Es soll dabei eine der nachfolgenden einfachenAufgabenstellungen gelöst werden. Welche Aufgabe zu lösen ist ergibt sich aus der Summe derMatrikelnummern der Kandidaten einer Gruppe.

Beim Gerät liegt ein AVS-Tutorial (ca. 25 Seiten) auf. Mit diesem Tutorial, zusammen mit derOnline-Hilfe, sollte es kein Problem sein, die verlangten Aufgaben zu lösen (Das Tutorial muß daherauf alle Fälle beim Gerät verbleiben, es wird ein Kopieroriginal beim Gerät aufliegen, das kopiertwerden kann).

Die Beurteilung erfolgt durch ein kurzes Abgabegespräch (10 Min.). Dazu gibt es Anmeldelisten,welche beim Gerät selbst aufliegen. Bei diesem Gespräch werden die Übungsteilnehmer kurz zu denvon Ihnen erstellten AVS Modulnetzwerken befragt. Die von den Übungsteilnehmern erstelltenModulnetzwerke können unter dem eigens dafür eingerichteten Account abgespeichert werden, odersie werden während des Abgabegesprächs rekonstruiert (was aufgrund des kleinen Umfangs dererstellten Netzwerke kein Problem sein sollte).

Aufruf des Programmes: avs Testdaten gibt es im Verzeichnis: /usr/local/avs/data

Bei inhaltlichen Fragen wenden Sie sich bitte an Eduard Gröller (Sprechstunde Di 10.00-11.00), beitechnischen Problemen wenden Sie sich bitte an unseren Techniker Herrn Meyer (Sprechstunde Di10.00-11.00). Die Nummer der von Ihnen zu lösenden Aufgabe ergibt sich folgendermaßen: (Summeder Matrikelnummern der Gruppenteilnehmer) mod 4

Aufgabe 0

Erstellen Sie ein Modulnetzwerk, das die Module ’image viewer’ und ’graph viewer’ beinhaltet. Erstellen Sie ein Modulnetzwerk, das einen Schnitt durch 3D Volumsdaten bewerkstelligt.

Aufgabe 1

Erstellen Sie ein Modulnetzwerk, das die Module ’geometry viewer’ und ’image viewer’ beinhaltet. Erstellen Sie ein Modulnetzwerk, das Vektordaten (also Strömungsdaten) veranschaulicht.

Aufgabe 2

Erstellen Sie ein Modulnetzwerk, das die Module ’geometry viewer’ und ’graph viewer’ beinhaltet. Erstellen Sie ein Modulnetzwerk, das eine Animation erzeugt (z.B. eine Schnittebene wird durcheinen Volumendatensatz bewegt oder der Isowert einer Isofläche wird animiert).

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Aufgabe 3

Erstellen Sie ein Modulnetzwerk, das mindestens drei Bildverarbeitungsaufgaben (z.B.averaging, clamping, ...) an einem 2D Rasterbild durchführt und die Resultate in separatenFenstern ausgibt. Erstellen Sie ein Modulnetzwerk, das eine Isofläche in einem 3D-Volumendatensatz berechnet.


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Gitta Domik, [email protected]

3D Graphics, Visualization and Image Processing

http://www.uni-paderborn.de/cs/vis (http://www.uni-paderborn.de/cs/vis)

Title of visualization course: Computer-Generated Visualization Goal of lectures and lab: Understanding and using concepts, methods and techniquesnecessary to visualize data sets. These data sets may be large, of complex structure, andrepresentative of different disciplines. Students of other disciplines are welcome in thecourse. Students may bring their own data sets for the groups to work on during their lab work. Topics: History and Definitions - Data, Data models - Influence of user abilities andvisualization aims on the expressiveness and effectiveness of graphical presentations -Different mapping strategies from data to pictures - visualization techniques - User´sinteraction - Visualization pipeline - Visualization systems. Hardware and Software for lab work can be chosen by students. We offer an SGI and Sunpools, an introduction to VRML and to IDL. . References: Tutorial by instructor (http://www.uni-paderborn.de/cs/vis/tutorial/index.htm) Extended information on course

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http://www.uni-paderborn.de/cs/vis

http://www.uni-paderborn.de/cs/vis/tutorial/index.htm

Prof. Beat D. Brüderlin-http://rabbit.prakinf.tu-ilmenau.de/bdb.html (http://rabbit.prakinf.tu-ilmenau.de/bdb.html)

Erfassung und Visualisierung von 3D-Daten

Technische Universität Ilmenau-http://rabbit.prakinf.tu-ilmenau.de/grafik1.html (http://rabbit.prakinf.tu-ilmenau.de/grafik1.html)

Fachgebiet Graphische Datenverarbeitung


Graphische Standards und Visualisierung

Grundlagen der Graphischen Datenverarbeitung

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http://rabbit.prakinf.tu-ilmenau.de/bdb.html

http://rabbit.prakinf.tu-ilmenau.de/grafik1.html


Doz. Dr.-Ing. habil. K.-H. Franke

Wahlpflichtfach für Studiengang Informatik , empfohlen im 9. SemesterWahlobligatorisch für Studiengang Elektrotechnik, empfohlen im 9. Semester(2 SWS Vorlesung)

Inhalt:

Koordinatensysteme und Transformationen Erfassung von 3D-Koordinaten - Wesen und Aufgaben (Verfahren der subpixelgenauenStrukturortsbestimmung, Monokulare und Binokulare Verfahren, Multibildphotogrammetrie,Codierter Lichtansatz, Phasenshiftverfahren, Laserrangefinder, 3D-Laserscanner,Entfernungsbildkamera, Schnittechniken) Kurven- und Flächendarstellung (Analytisch gegebene Kurven und Flächen, Freiformkurven und-flächen) 3D-Punktwolkenanalyse und -handling (Polygone, Splines, Dreieckszerlegung, Patches, Nurbs,Surfaces, Fitting analytischer Funktionen) Computergrafische Darstellung (Visibilitätsverfahren, Beleuchtungsmodelle) Geometrische Modellierung (Klassifizierung von geometrischen Modellierungssystemen undGrundlagen, Applikation CAD)

Voraussetzungen: günstig Grundlagen der Bildverarbeitung, GDV I

Abschluß: Bewerteter Schein

Literatur:

Foley, van Dam, Feiner, Hughes: Computer Graphics: Principles and Practice, second Edition,Addison Wesley, 1990 Encarnacão, J., Straßer, W., Computer Graphics, R. Oldenburg Verlag, München, 1988 K. Kraus: Photogrammetrie Bd. I, Dümmler- Verlag, Bonn 1994 B. Breuckmann: Bildverarbeitung und optische Meßtechnik in der industriellen Praxis,Franzis-Verlag, München 1993


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Graphische Standards und Visualisierung

Dipl.-Ing. R. Barth

empfohlen im 6. Semester, Studiengang Informatik( 2 SWS Vorlesung, 1 SWS Praktikum )

Inhalt:

Graphische Standards

Zweckbestimmung graphischer Standards Standardisierungsgremien Design von Graphik-Standards GKS - Graphisches Kernsystem (GKS-Funktionalität im Überblick, GKS-Workstationkonzept,Zustände des GKS) PHIGS - Programmer’s Hierarchical Interactive Graphics System (PLUS - Plus Lumiere andSurfaces) (Zustände, Datenorganisation, Strukturen im PHIGS, PHIGS-Extensions to X (PEX)) OpenGL (Prinzipien, Design, Funktionsüberblick, Programmierung der OpenGL,Transformationen, Rendering, Animation) Computer Graphics Reference Model(CGRM)

Wissenschaftlich-technische Visualisierung

Begriffsbestimmung Simulationszyklus Visualisierungspipeline Datenaufbereitung/Filter Datenmapping (ausgew&uaml;hlte Algorithmen) Rendering Volumenvisualisierung CFD - Computational Fluid Dynamics Visualisierungssysteme (AVS, Explorer)

Voraussetzungen: GDV I

Literatur:

International Organisation of Standardization, Information Processing Systems - ComputerGrapphics - Graphical Kernel System (GKS); ISO 7942:1985 J. Bechlars, R. Buhtz, GKS in der Praxis, Springer Verlag, 1986 W.A. Gaman, W.A. Giovanazzo, PHIGS by Example, Springer Verlag, 1991 T. Gaskins, PHIGS Programming Manual, O’Reilly & Associates, Inc., 1992 Adobe Systems Inc., PostScript, Addison Wesley,1985 OpenGL Architekture Review Board, OpenGL- Reference Manual: The Official ReferenceDocument for OpenGL, Release 1, Addison Wesley, 1992 J. Neider, T. Davis, M. Woo, OpenGL- Programming Guide: The Official Guide to LearningOpenGL, Release 1, Addison Wesley, 1993 R. Barth, E. Beier, B. Pahnke, Grafikprogrammierung mit OpenGL, Addison Wesley, 1996

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Paul Ho, Frequently Asked Questions list about OpenGL(TM); Internet-Newsgroup comp.graphics.api.opengl.

Abschluß: unbewerteter Schein


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Grundlagen der Graphischen Datenverarbeitung

Prof. B. Brüderlin, Dipl.-Ing. B. Pahnke

empfohlen im 5. Semester, Studiengang Informatik( 2 SWS Vorlesung, 1 SWS Seminar)

Inhalt:

Vektoren und Transformationen von 3D-Objekten Geometrisches Modellieren: Modell-Datenstrukturen (CSG, Boundary Representation, Sweep,Voxel, Octree, Molekülmodelle) Freiformkurven und -flächen (Kubische Splines, Be´zier-Kurven, B-Splines und NURBS) Farbenphysiologie, Farbmodelle: RGB, CMY, HSV Hardware: Farbdiskretisierung, Farbbildröhre, LCD, Laserprinter, Ink jet, etc. Rastergraphik (Bresenham, Polygon filling Algorithmus) Rasteroperationen: Dithering, Anti Aliasing View Transformationen, Clipping, Hidden Line 3D Rendering, Reflexion (diffuse, spekulare), Z-Buffer, Gouraud Shading, Phong Shading Advanced Rendering: Ray tracing, Radiosity, Texture mapping Graphische Funktionen: Point in Polygon, Flächeninhalt, Schneiden von Linien und Flächen,Berechnen von Winkeln und Abständen, Rotieren um eine Achse Computergraphische Animation (Kollision, Physikalisch basiertes Modellieren )

Voraussetzungen: keine

Literatur:

Foley, van Dam, Feiner, HughesComputer Graphics: Principles and Practicesecond Edition, Addison Weley, 1990 Encarnacão, J. , Straßer, W.Computer GraphicsR. Oldenburg Verlag, München, 1988 Rogers, D.F.; Adams, J.A.Mathematical Elements for Computer GraphicsMc Graw-Hill, Hamburg, 1990 Barth, R., Beier, E., Pahnke, B.Grafikprogrammierung mit OpenGLAddison-Wesley, Bonn, 1996

Abschluß:bewerteter Schein


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Professor Dr.-Ing. Dr. h.c. Dr. E.h. José Luis Encarnação (http://www.igd.fhg.de/~jle/)

House of Computer Graphics Darmstadt(GRIS, IGD, ZGDV) (http://www.igd.fhg.de/)

Lectures-http://www.gris.informatik.tu-darmstadt.de/lehre/index_en.html (http://www.gris.informatik.tu-darmstadt.de/lehre/index_en.html)

The Interactive Graphics Systems Group (GRIS) offers a variety of different lectures in theComputer Graphics area. Lehrveranstaltungen des Fachgebiets GRIS an der THD (GRISLectures)

Virtuelle Reliitaet- Werkzeuge fuer Echtzeit-Visualisierung und 3D-Interaktion Visualisierung und virtuelle Realität Mobile Information Visualization department(ZGDV) Department Visualization and Virtual Reality (Fraunhofer Institute for C.G.) http://www.igd.fhg.de/www/igd-a4/ (http://www.igd.fhg.de/www/igd-a4/)

Weather Visualization - RASSIN

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http://www.igd.fhg.de/~jle/

http://www.igd.fhg.de/

http://www.gris.informatik.tu-darmstadt.de/lehre/index_en.html

http://www.igd.fhg.de/www/igd-a4/

Lehrveranstaltung: Virtuelle Realität - Werkzeuge für Echtzeit - Visualisierungund 3D-Interaktion

Veranstaltungsform: Seminar (0+3)

Hochschullehrer:

Prof. Dr. J. Encarnação, Dr. M. Göbel,Dr. P. Astheimer, Dr. F. Dai, Dr. W. Felger

Voraussetzungen: GDV I, Vordiplom

Inhalt:

Darstellung und Diskussion neuer, z. T. noch experimenteller Konzepte der Mensch-MaschineKommunikation Anforderungen an Systemlösungen für spezielle Anwendungsgebiete internationalerForschungen Themengebiete:

Erarbeitung des aktuellen Standes der VR-Forschung in unterschiedlichen Disziplinen Diskussion gesellschaftlicher, ökonomischer und ethischer Aspekte im Zusammenhang mitdieser neuen Technologie Anwendungsszenarien: Hintergründe und Erfolge


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Lehrveranstaltung: Visualisierung und virtuelle Realität

Veranstaltungsform: Vorlesung und Übung (2+2)Hochschullehrer: Dr. Stefan MüllerVoraussetzungen: GDV I, Vordiplom

Inhalt:

Einführung in die Problematik der Visualisierung und VR, Datenvorverarbeitung,Datenvisualisierung, Datenpräsentation, Interaktion mit Daten, Geräte- und Rechnertechnologien,Hochleistung-Renderingverfahren, Volumenrendering, aktuelle Visualisierungstechniken und-systeme, VR Anwendungsbeispiele, Datenschnittstellen und Standards, Echtzeitsimulationsverfahren,Kollisionserkennung, Haptik, Parallelisierungsstrategien.


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Prof. Dr. Frieder Nake

[email protected]

University of Bremen

Title of Visualization Course:

NOT on SCIENTIFIC Visualization, but on Visualization in general. I have also had what wecall "project" (a four semester course of study where approx. 20 students work on a specific topicwith the aim of developing some prototype software). In this case, it was a project onVisualization of graphics algorithms.

Objectives of the course:

The course aimed at a general understanding of what pictures are, what words are, how these twoways of expression are necessary for intelligent work and development of intelligence, whatvisualization is in respect to algorithmic methods, and how postmodern society turns to pictures.There was a very broad spectrum of references, rooted deep in philosophy through somepsychology and semiotics and way into computer graphics.

References:

In fact, we had a series of seminar talks embedded into my lectures, and the students had topresent talks on chapters in the book, edited by R. Sutherland and J. Mason, "Exploiting mentalimagery with computers in mathematics education" (Springer).

Lab:

No lab work was included (but there was a heavy practical experience part in the projectmentioned above.

Hardware: Unix workstations, Sun and Indy.

Software: C, C++, Java.


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General Information: The general course on visualization was a one-semester Number of hours: 4 hours per 14 weeks (one hour is 45 minutes) Number of students in the course: About 25 students survived through the course. Media used in the course: blackboard and some overhead transparencies as simple media.

Audience: Describe you students(undergrad,grads,actual level, background):

course for advanced students of informatics (in their fifth or higher semester, after theVordiplom).

Evaluation: No exams or the like.

Those who gave a talk on one of those papers got the type of credit we grant.


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Dietmar Saupe, Universitaet Konstanz

http://www.inf.uni-konstanz.de/cgip/saupe/index.shtml.en (http://www.inf.uni-konstanz.de/cgip/saupe/index.shtml.en)

International Summer School

Title of course: International Summer School on Scientific and Mathematical Visualization. Objectives of the course:

The participants will gain an overview and a working knowledge of the entire field of scientificand mathematical visualization.

topics covered by the lecturers include Mathematical Computer Graphics: Manifolds Physically Based Methods for Topology Visualization Scientific Visualisation and Volume Rendering Various techniques for flow visualization Interactive visualization and computational steering

detailierte Informationen(german)

Summer School Program

Lab/Hardware

Silicon Graphics and Siemens-Nixdorf send three Highend-Workstations to this event.


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http://www.inf.uni-konstanz.de/cgip/saupe/index.shtml.en

International Summer Schoolon Scientific and Mathematical Visualization

Preliminary ProgramEttenheim, September 22-27, 1996

Sunday

18:00 Registration

Monday

9.00 Welcome and Opening

9.15 B. Metz, Mayor of Ettenheim

Message of Greeting

9.30 J. van Wijk

Various Techniques for Flow Visualization I

10.30 Coffee break

11.00 J. van Wijk

Various Techniques for Flow Visualization II

12.30 Lunch break

14.30 J. van Wijk

Interactive Visualization and Computational Steering I

15.30 Coffee break

16.00 J. van Wijk

Interactive Visualization and Computational Steering II

17.00 Social Event I

Guided walk through Ettenheim and surroundings

Reception at the local winery ‘‘Weber’’

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Tuesday

9.00 L. Hesselink

Physically Based Methods for Topology Visualization I

10.00 coffee break

10.30 L. Hesselink

Physically Based Methods for Topology Visualization II

12.00 lunch break

13:30 R. Scopigno

Scientific Visualisation and Volume Rendering I

14.30 Coffee break

15.00 R. Scopigno

Scientific Visualisation and Volume Rendering II

16.00 Talks by the participants

18.00 Poster session

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Wednesday

9.00 L. Hesselink

Physically Based Methods for Topology Visualization III

10.00 Coffee break

10.30 L. Hesselink

Physically Based Methods for Topology Visualization IV

12.00 Lunch break

14.00 Social Event II

Bus trip to the Rheinauen with boat tour

Transfer to the Kaiserstuhl and small hike

18.00 Wine tasting

22.00 Arrival at Ettenheim

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Thursday

9.00 G. Francis

Mathematical Computer Graphics: Manifolds I

10.00 coffee break

10.30 G. Francis

Mathematical Computer Graphics: Manifolds II

12.00 Lunch break

13:30 R. Scopigno

Scientific Visualisation and Volume Rendering III

14.30 Coffee break

15.00 R. Scopigno

Scientific Visualisation and Volume Rendering IV

16.00 Software demonstration and participant talks

20.00 Video Night

Friday

9.00 G. Francis

Mathematical Computer Graphics: Manifolds III

10.00 coffee break

10.30 G. Francis

Mathematical Computer Graphics: Manifolds IV

12.00 Lunch break

13.00 End of summerschool and departure

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Extended information on summer school

Duration of course: 5 days

Presentation

actual works in in short lectures, a Postersession, computer demonstrations and a Videopraesentation.

Audience

about 70 participants of 10 countries including students and professionals from universities,research institutes and industry who want to get a comprehensive view of visualization includingrecent results.

Organisation: M. Rumpf and D. Saupe.

The summerschool was sponsored by a "Graduiertenkolleg" at the University of Freiburg andwas supported by EUROGRAPHICS and the Organisation for Informatik.


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Detailierte Informationen - Themen der Vorlesung

Interaktive Echtzeit Computeranimation fuer mathematische Anwendungen vonFlaechenkonstruktionen mit Moebiusbaendern, Klein’schen Flaschen bis zu geodaetischenFluessen, der Eversion der Sphaere und Virtual Reality im CAVE reichen (Vortragender:G.Francis, University of Illinois). Visualisierung von Vektorfeld-Topologien in CFD Anwendungen (Computational Fluid Flow)und von Tensor-Feldern mit Hyperstreamlines(Vortragender: Y. Levy, Stanford University).

Wissenschaftliche Visualisierung von Volumendaten:

optimierte Flaechenextraktionsverfahren, direktes Rendering mit Raycasting undProjektionsmethoden, Daten auf irregulaeren Triangulierungen (Vortragender: R. Scopigno,Universitaet Pisa).

Einzelvortraege zu adaptiven Gittern (M. Rumpf), fraktaler Bildkompression (D. Saupe) undVektorfeldvisualisierung (Ch. Hege).


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Prof.Dr.Ing.habil.H.Schumann- http://www.informatik.uni-rostock.de/~schumann/ (http://www.informatik.uni-rostock.de/~schumann)

University of Rostock

Institute for Computer Graphics, Computer Science Department

http://www.icg.informatik.uni-rostock.de/Lehre/ (http://www.icg.informatik.uni-rostock.de/Lehre/)

Title of visualization course: visualization of scientific data.

more detailed information - http://wwwicg.informatik.uni-rostock.de/Lehre/VWD_de.html (http://wwwicg.informatik.uni-rostock.de/Lehre/VWD_de.html)

Objectives of the course: basic knowledge in Visualization

In der Vorlesung werden grundlegende Möglichkeiten zur Visualisierung unterschiedlicherDatenmengen aus den Bereichen Wissenschaft und Technik sowie Umwelt, Medizin u.a.behandelt. Dazu gehören Methoden zur Visualisierung von Raumdaten, Strömungsdaten sowieMultiparameterdatensätze. Die vermittelten Konzepte werden anhand von realen Datensätzen ausverschiedenen Anwendungen diskutiert und bewertet. Abschließend wird mit dem IRIS-Explorervon Silicon Graphics ein modernes Visualisierungssystem vorgestellt.

List topics of course

Lab.:

Hardware: SGI-workstations.

Software: EXPLORER, own programs and systems.

References:

Course text: my script

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http://www.informatik.uni-rostock.de/~schumann

http://www.icg.informatik.uni-rostock.de/Lehre/

http://wwwicg.informatik.uni-rostock.de/Lehre/VWD_de.html

Selected readings: Brodlie, Earnshaw, Rosenblum, Hearnshaw, Thalmann, Kaufman,Nielson, several proceedings.


Proj Intelligente Vis.-system -http://www.icg.informatik.uni-rostock.de/Projekte/VISU/visu_projekt.html (http://www.icg.informatik.uni-rostock.de/Projekte/VISU/visu_projekt.html)

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http://www.icg.informatik.uni-rostock.de/Projekte/VISU/visu_projekt.html

http://www.icg.informatik.uni-rostock.de/Projekte/VISU/visu_projekt.html


General Information: Number of hours for course: 2+1+1

2h lectures 1h training (exercise) 1h lab (EPLORER-programming)

Media used for teaching the course: blackboard, overhead in lectures workstations. how long "your" hour is: 45 minutes.

Audience: Describe your students (undergrads, grads, actual level, their background):

grads, prefered knowledge in Computergraphics.

Evaluation: written midterm and/or final: no, but participation on course. use assignments (how many, example): solution of one practical exercise.

Prerequisites:

basic knowledge in algorithms and programming.


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1) introduction (2h) 2) the visualisation process

2.1) data handling and selection (3h) 2.2) Mapping (incl. support for mapping) (3h)

3) Visualization of Multiparameterdata 3.1) data visualization (4h) 3.2) visualizatin of spatial dependence (1h) 3.3) visualization of temporal dependence (1h) 3.4) human factors in visualization (1h)

4) Volume visualization 4.1) general overview and definitions (1h) 4.2) methods for surface extraction (2h) 4.3) direct volume rendering (2h) 4.4) further problems (special methods for irregular grids,combination with geometricrendering ...) (1h)

5) fluid flow visualization 5.1) general overview and definitions (1h) 5.2) special methods (4h)

6) Visualization systems (2h) 7) Information visualization (2h)


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Thomas Ertl-http://wwwvis.informatik.uni-stuttgart.de/~ertl/ (http://wwwvis.informatik.uni-stuttgart.de/~ertl/)

[email protected]

Outdated information:

LS f. Graph. Datenverarbeitung, Universitaet Erlangen

http://www9.informatik.uni-erlangen.de/ (http://www9.informatik.uni-erlangen.de/)

Title of visualization course: Visualization Objectives and topics of course(german) Lab:

Hardware: SGI workstation pool (13 Indigo R4000 on campus and many others in thedepartmental lab). Software: Iris Explorer, Xmdvtools, VolVis, ....

References: General references: Rosenblum et al., Ghallager, Earnshaw, Watson, IEEE VIS, EG VISC,SIGGRAPH Proceedings.


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http://wwwvis.informatik.uni-stuttgart.de/~ertl/

http://www9.informatik.uni-erlangen.de/


General information

Number of students in course: 30 - 50

Number of hours for course: 1. 3h lecture, 1h exercise

Media used for teaching your course: Blackboard, overhead, slides, video

how long "your" hour is: 45 min

Audience:

Describe your students (undergrads, grads, actual level, their background):

grads in computer science after having a one-semester (3+1) CG course.

Evaluation:

use assignments (how many, example): One assignment per week containing theoreticalan implementation.

evaluation scheme: Each assignment is graded (10 points each). The course is passed withmore than 60%.

Prerequisites:

Computer graphics course (lecture and exercises on grad level).


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Objectives and topics of course

Inhalt der Vorlesung(Contents and Objectives):

Die Visualisierung beschäftigt sich mit allen Aspekten, die im Zusammenhang stehen mit dervisuellen Aufbereitung der (oft großen) Datenmengen aus technisch-wissenschaftlichenExperimenten oder Simulationen zum Zwecke des tieferen Verständnisses und der einfacherenPräsentation komplexer Phänomene. Dazu werden sowohl bekannte Verfahren der interaktivenComputer-Graphik als auch völlig neue Methoden angewandt. Die Vorlesung gibt eineEinführung in die grundlegenden Algorithmen und Datenstrukturen, sowie einen Überblick überdie verfügbaren Softwarewerkzeuge und verbreiteten Dateiformate.

Themen (Topics): Numerische Simulation und Visualisierungsszenarien Gitterstrukturen und Interpolation Verfahren für 2D Skalar-, Vektor- und Tensorfelder Verfahren für 3D Vektor- und Tensorfelder Volumenvisualisierung mit Isoflächen Direktes Volume-Rendering (DVR) Probleme der Farbwahl und andere Visualisierungsfehler

Voraussetzungen(Prerequisits): LV: Computergraphik Geeignet für Hauptstudium ab 5. Semester

Evaluation: Scheinerwerb: Übungsaufgaben(exersises)

genauere Gliederung (more detailed contents in german)


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Gliederung der Vorlesung Visualisierung im SS 95

Prof. Dr. Thomas Ertl

1. Einfuehrung Motivation und Definition Historie -- ViSC-Report Anwendungsgebiete: Messungen und Simulationen Computersimulationen und generierte Datenmengen

2. Grundlagen Der Visualisierungsprozess: Filter - Mapper - Renderer Visualisierungsszenarios: vom Video bis zur interaktiven Steuerung Datentypen und Operationen

Skalar-, Vektor-, Tensorfelder Gradient, Divergenz, Rotation, Laplace

Koordinatensysteme Gittertypen (kartesisch, recti/curvi-linear) und Zeitabhaengigkeit Modelle und Numerik: Nicht/Linear, Eigenwert, ODE, PDE Klassifikation von Visualisierungsverfahren

3.Visualisierungspakete Systemarchitektur: User Interface, Control Flow, Data Management, Vis.-Module,Graphikschnittstelle Klassifikation: Bibliotheken, Turnkey-Systeme, Application Builder (MPE: ModularProgramming Environment) Verteilte Systeme Beispiele: Iris Explorer, AVS, IBM Dataexplorer, Wavefront Datavisualizer, PLOT3D,ISVAS, Grape Dateiformate: HDF und netCDF

4.Verfahren fuer 1D-Datensaetze Diskret: Balken-, Tortendiagramme, Histogramme Kontinuierlich: Graphen, Scatterplots, Polardiagramme Datenpunkte und Interpolation, Fehlerbalken

5.Verfahren fuer 2D-Skalarfelder Hoehenlinien

Algorithmus fuer kartesische Gitter Sonderfaelle, Ausnahmebehandlung Bearbeitungsreihenfolge und Rendering Erweiterungen: elevated/shaded contour

Rasterbilder: Transformationsreihenfolge beachten Hoehenfelder: Gitterlinien, schattiert, farbkodiert

6. Multivariate Daten Attributierung: Hoehenfelder + Farbe, Rasterbilder (hue + saturation) Parallele Koordinaten, Dimensional Stacking, Scatterplots Icons und Glyphs

Textur Icons Chernoff-Plots

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Data Jacks, Injection Glyphs 7. 2D-Vektorfelder

Experimentelle Methoden Vektorpfeile Stroemungslinien

Experimente -- Teilchenbahnen, Streichlinien, Zeitlinien Kartesische Gitter: Algorithmus, Zellensuche, Interpolation, Integration Strukturierte Gitter: P-Space -- C-Space Verfahren, Jacobi-Matrizen, Stencil-Walk, Inverse Distance Weighting Unstrukturierte Gitter: Interpolation, Nachbarzellensuche

Vektorfeld-Topologie: Klassifikation kritischer Punkte Line Integral Convolution: filtere Rauschen entlang der Stroemungslinie

8. Volumenvisualisierung Ueberblick und Klassifikation: Surface Fitting und Direct Volume Rendering Isoflaechen

Konturverknuepfung und Cuberille-Verfahren Marching Cubes: Algorithmus und Zweideutigkeiten Dividing Cubes: Vorteile bei medizinischen Daten Chain of Cubes: Fortsetzungsmethoden

Segmentierung: fuzzy Klassifikation Volume Scanning: Raycasting (image space)

Projection (voxel space) Einfache Verfahren: MIP, Roentgen, Schwellwert Schattierungsverfahren: depth-only, depth-gradient, Phong-Voxel-Shading Transferfunktionen: manuelle und automatische Opazitaetszuordnung Komposition semitransparenter Voxel (over Operator) Traversierung: BTF -- FTB mit Pseudocode Raycasting beliebig orientierter Volumina

Samplingstrategien: Adaptive Ray/Screen Sampling, Subpixel Beschleunigung: Ray-Templates, Space-Leaping Volumenrotation: Eulerwinkel und Zerlegung in Scherungen

Projektionsverfahren: Vorteile, V-Buffer Splatting: Gauss Rekonstruktions-Kernel, Footprint Theorie des Strahlungstransports: Bilanzgleichung, Integralform,

Rendering/Radiosity Eq., Emissions-Absorptions-N"aherung, Diskretisierung

Volumengraphik -- Voxelization 9. 3D-Vektorfelder

Lokale Verfahren: Volume Probe, Vektor-Statistik, Texture Splats Globale Verfahren: Stream Surface Topologie, Baender und Roehren

10. Wahrnehmung und Farbe Das menschliche Auge: Tristimulus-Theorie Farbraeume: Geraetenah, wahrnehmungsorientiert und -uniform Farbwahlkriterien: Vermeidung von Illusionen, Video und Saettigung Farbtabellen: Auswahlkriterien

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Thomas Ertl-http://wwwvis.informatik.uni-stuttgart.de/~ertl/ (http://wwwvis.informatik.uni-stuttgart.de/~ertl/)

[email protected]

Outdated information:

Graph. Datenverarbeitung, Universitaet Erlangen

http://www9.informatik.uni-erlangen.de/ (http://www9.informatik.uni-erlangen.de/)

Title of visualization course: Advanced Visualization Techniques Inhalt der Vorlesung(Topics of the course):

In der Lehrveranstaltung werden neuere Verfahren und Algorithmen aus den folgendenBereichen derVisualisierung detailliert besprochen:

Strömungsvisualisierung Volumenvisualisierung Multivariate Datensätze Wahrnehmungsaspekte Parallelisierungsstrategien Methoden des Interactive Steering

Lab.: Hardware: SGI workstation pool (13 Indigo R4000 on campus and in the departmental lab). Software: Iris Explorer, Xmdvtools, VolVis, ....

References.: General references: Rosenblum et al., Ghallager, Earnshaw, Watson,

IEEE VIS, EG VISC, SIGGRAPH Proceedings

Voraussetzungen(Prerequisits): Geeignet für Hauptstudium ab 5. Semester , Vorausgesetzte LV: Visualisierung


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http://wwwvis.informatik.uni-stuttgart.de/~ertl/

http://www9.informatik.uni-erlangen.de/


General information: Number of students in course: 10 - 15 Number of hours for course: 2h lecture Media used for teaching your course: Blackboard, overhead, slides, video how long "your" hour is: 45 min

Audience: Describe your students (undergrads, grads, actual level, their background):

grads in computer science after having a one-semester (3+1) CG course

Evaluation: use assignments (how many, example): One assignment per week containing theoreticalan implementation. evaluation scheme: Each assignment is graded (10 points each). The course is passed withmore than 60%.

Prerequisits:

Computer graphics course (lecture and exercises on grad level).


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Prof. Dr. Hans Hagen-http://davinci.informatik.uni-kl.de/ (http://davinci.informatik.uni-kl.de/)

University of Kaiserslautern-http://www.informatik.uni-kl.de/ (http://www.informatik.uni-kl.de/)

Department of Computer Science

Title of Visualization course: Scientific Visualization


computational geometry fundamentals

visualization of large unstructured data sets

quality analysis algorithm

photorealistic rendering

virtuell reality

medical imaging

flow visualization

visualization of vector- and tensorfields

mesh generation

Lab:

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http://davinci.informatik.uni-kl.de/

http://www.informatik.uni-kl.de/

hands on experience: hands-on exercises

Hardware: SGI- workstations


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Genearal information: Number of students of the course: 18 -26. Number of hours for the course: 4h + 2H Lab. Media used in the course: overhead, electronic media.

Audience: graduate students

Prerequisites: computer graphics course. computer aided geometric design (optimal).


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Dr. Stefan Mueller, [email protected]

Fraunhofer Institute for Computer Graphics (IGD)

TU Darmstadt

Visualization &Virtual Reality

Title of visualization course: "Visualization and Virtual Reality"


The objective of this lecture to give students deep insight in the technology and application ofscientific visualization and virtual reality.

List topics of course:

The lectures covers an introduction to scientific visualization, decribes algorithms for volumereconstruction, rendering methods, and gives an overview over existing visualization systems. Anindtroduction to virtual reality is also given, describing hardware technology, interaction,collision detection, real-time rendering, radiosity, modelling, VRML, distributed VR. Finaly, thecombination of visualization and virtual reality is outligned and application examples for alldescribed subjects are given.

Lab.:

Hardware: Mainly SGI HW (Indy, Indigo, Onyx), CAVE, motion platform,PHANToM,several I/O devices.

Software: Self developed systems.


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General information: Number of students in course: 30-50 Number of hours for course: 14 times 2 hours Media used for teaching your course:

slides, video and software examples (video beamer.)

how long "your" hour is: 45 min. Audience:

Describe your students (undergrads, grads, actual level, their background):

grads with good knowledge in computer graphics.

Evaluation: written midterm and/or final: final


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Rene’ T. Rau

University of Tuebingen, WSI/GRIS

http://www.gris.uni-tuebingen.de/gris/vvz/vz.html (http://www.gris.uni-tuebingen.de/)

Title of visualization course:

Scientific Visualization (Visualisierung wissenschaftlicher Daten).

Objectives of the course

basic definitions and concepts of scientific visualization.

discussion on concrete problems with examples.

applying the lectures in exercises.

get to know visualisation techniques (assignments)

introduction in various visualization systems.

List topics of course(only german)

Geschichtliche Entwicklung der Visualisierung

Daten- und Problemklassifikation

Grundlegende Methoden der Computer Graphik und der ‘Scientific Visualization‘(Visualisierungsszenarien, visuelle Repraesentationen)

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http://www.gris.uni-tuebingen.de/

Sampling und Rekonstruktion mehrdimensionaler Daten

Filterung mehrdimensionaler Daten

Visualisierungskonzepte mehrdimensionaler Daten

Verwendung von Farbe in der Visualisierung

Volumenvisualisierung (Isoflaechenerzeugung, Direkte Volumendarstellungen).

Visualisierung von Stroemungsfeldern (Visualisierung von Vektoren und Tensoren).

Anwendungsgebiete der Visualisierung (u.a. Medizin, Chemie, Geologie).

Visualisierungssysteme: Bestandteile, Konzepte, Software (u.a. AVS,apE,Mathematica, Explorer).

References on course


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Voraussetzungen: Grundstudium Informatik. Weitergehende Kenntnisse aus der GraphischenDatenverarbeitung, ‘Bildverarbeitung‘ und ‘GeoMod‘ sind willkommen. Literatur:

[1] M. G-obel and J.C. Teixeira. Graphics Modeling and Visualization. Springer, 1993. [2] M. Frühauf and M. G-obel. Visualisierung von Volumendaten. Springer Verlag, 1991. [3] H. Hagen, H. M-uller, and G. M. Nielson. Focus on Scientific Visualization. SpringerVerlag, 1993. [4] P.R. Keller and M.K. Keller. Visual Cues for Practical Data Visualization. IEEEComputer Society Press, 1993. [5] R. A. Earnshaw and N. Wiseman (Eds.). An Introductory Guide to Scien tificVisualazation. Springer, 1992. [6] D. Thalmann (Ed.). Scientific Visualization and Graphics Simulation. Wiley, 1990. [7] N. M. Patrikalakis (Ed.). Scientific Visualization of Physical Phenomena.Springer-Verlag, 1991. [8] Gregory Nielson. Algorithms for the analysis and visualization of mul tivariate data. InWerner Purgathofer, editor, Eurographics ’91, pages 519{520. North-Holland, September1991. [9] Gregory M. Nielson, Thomas A. Foley, Bernd Hamann, and David Lane. Visualizingand modeling scattered multivariate data. IEEE Computer Graphics and Applications,11(3):47{55, May 1991. [10] F.H. Post and A. J. S. Hin (Eds.). Advances in scientific Visualization. Springer, 1992. [11] K. W. Brodlie u.a. (Eds.). Scientific Visualization - Techniques and App lications.Springer, 1992. Bemerkungen: Im Bereich ‘Scienti,c Visualization‘ ergeben sich imRahmen des Sonderforschungsbereichs 382 ‘Verfahren und Algorithmen zur Simulationphysikalischer Prozesse auf H-ochstleistungsrechnern‘ zahlreiche interessanteAufgabenstellungen für Studien- und Diplomarbeiten.


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[1] M. G-obel and J.C. Teixeira. Graphics Modeling and Visualization. Springer,1993.

[2] M. Fr-uhauf and M. G-obel. Visualisierung von Volumendaten. Springer Verlag, 1991.

[3] H. Hagen, H. M-uller, and G. M. Nielson. Focus on Scientific Visualization. Springer Verlag, 1993.

[4] P.R. Keller and M.K. Keller. Visual Cues for Practical Data Visualization.IEEE Computer SocietyPress, 1993.

[5] R. A. Earnshaw and N. Wiseman (Eds.). An Introductory Guide to Scientific Visualazation.Springer, 1992.

[6] D. Thalmann (Ed.). Scientific Visualization and Graphics Simulation. Wiley,1990.

[7] N. M. Patrikalakis (Ed.). Scientific Visualization of Physical Phenomena. Springer-Verlag, 1991.

[8] Gregory Nielson. Algorithms for the analysis and visualization of multivariate data. In WernerPurgathofer, editor, Eurographics ’91, pages 519{520. North-Holland, September 1991.

[9] Gregory M. Nielson, Thomas A. Foley, Bernd Hamann, and David Lane.Visualizing and modelingscattered multivariate data. IEEE Computer Graphics and Applications, 11(3):47{55, May 1991.

[10] F.H. Post and A. J. S. Hin (Eds.). Advances in scientific Visualization.Springer, 1992.

[11] K. W. Brodlie u.a. (Eds.). Scientific Visualization - Techniques and Applications. Springer, 1992.


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Prof. H. Schreiter, [email protected]

Technische Universitaet Chemnitz-Zwickau

Fakultaet fuer Informatik

Title of visualization course: Scientific Visualization Seminar.


deepening and extension to the lectures on computer graphics.

getting to know selected topics and case studies.


survey on scientific visualization 2 h.

application areas 8 h (visualization in medicine and environmental protection, fluid flowvisualization).

foundations 8 h (virtual pipeline, interaction techniques, data structures).

data modeling 4 h (surface and volume modeling).

rendering 2 h.

References:

General references:Hagen, H./Müller,H./Nielson, G.M. (ed.): Focus on ScientificVisualization, Springer 1993.

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General information: Number of students in course: 12 Number of hours for course: 24 h / 45 min Media used for teaching the course: black board, overhead, computer.

Audience: Describe your students (undergrads, grads, their background):

students in the seventh semester (applied informatics).

Prerequisites:

courses on computer graphics, computer geometry and geometric modeling.


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Dennis J. Bouvier, Assistant Professor

Department of Computer Systems Engineering

http://www.uark.edu/ (http://www.uark.edu/)

University of Arkansas

Title of visualization course: Scientific Visualization

Lab.:

Hardware: general purpose workstations and PCs, most are SUN Sparc5 or Sparc10.

Software: Most use custom software.

References:

Course text: Rosenblum, et al, "Scientific Visualization"

Selected readings: several including:

classic marching cubes paper

"14 Ways to Lie With Visualization"

Prerequisits: progamming ability, willingness to work, interest in visualization.


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http://www.uark.edu/

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General Information: Number of students in course:10 Number of hours for course: 3 hours lecture per week (50 minutes)

(44 class periods per semester).

Media used for teaching the course:

white board, electronic media (web delivered material), handouts demonstrations.

Audience: Describe your students (undergrads, grads, actaul level, background):

grads and few undergrads, background is BS in Computer Science or Engineering, weak indigital signal processing, and perception, strong in programming, and data structures.

Evaluation: written midterm and/or final: Yes (both) use assignments (how many, example): 4 or 5

implementing various techniques of visualizaton (all in C).

Evaluation scheme: 30% testing 40% projects 20% research paper 10% small homework assignments


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Prof. John A. Cross-http://www.cosc.iup.edu/jacross/ (http://www.cosc.iup.edu/jacross/)

Indiana University of Pennsylvania

Computer Science Department-http://www.cosc.iup.edu (http://www.cosc.iup.edu/)

Title of visualization course: Computer Graphics (with maybe a week on data visualization).

Objectives of the course: undergraduate intro to computer graphics.

List topics of course: (http://www.cosc.iup.edu/jacross/355/) data visualization is a specialtopic I do late in the course.

Lab.:

Hardware and software:

PC’s with Win 95; VRML, Superscape demos, Microsoft Office 95 briefly, other tools TBA.

References:

Course text: VRML 2.0 Sourcebook and lots of notes.


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http://www.cosc.iup.edu/jacross/

http://www.cosc.iup.edu/

http://www.cosc.iup.edu/jacross/355/


General information Number of students in course: 23 Number of hours for course: 29 hours lecture 13 hours lab Media used for teaching the course: a computer project panel using a Win 95 PC how long "your" hour is: one hour = 60 minutes

Audience. Describe your students (undergrads, grads, their background): undergrads, mostly third year

Evaluation:

written midterm and/or final: YES, open book but challengingused assignments evaluation scheme

Prerequisites: CS-2 (Data Structures)


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Evaluation scheme

I will grade you on projects, quizzes as needed, a midterm exam, and a final. I will announce any quizat least one class prior to when I give it. Your final examination will seem to be cumulative because ofthe nature of the course, but questions from material that was part of the midterm exam period willfocus on absolutely fundamental ideas or something that was missed on a previous examination. Insimpler terms: after the first exam, study current material, old exams, and projects.

I base your final letter grade on the total number of points you have earned relative to what I judge tobe appropriate cutoffs for different letter grades. You may expect that 90%-80%-70%-60% will earnno less that A-B-C-D. I will consider moving students who are close to a cutoff point up or down,depending on your exam scores, your attendance and participation in each class, and other indicationsof knowledge of the subject matter of CO 355. You may check your grades by using the"grade-check." The APPROXIMATE point distribution will be:

Mid-term Exam 100 points Final 100 Projects 150-250 Quizzes 0-50


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Assigments

All handins must be neat, complete, and clearly labeled with your name, course, and project number.All parts of your handin should include an appropriate date.

You may submit assignments in class or in my assignment submission box in the HETlab. Allassignments are due by 4:30 P.M. on the due date. I am under no obligation to grade late assignmentsand I may simply lose them. If you are not finished working when a program is due, submit what youhave finished. I will assess up to a 5-point penalty per meeting for all late assignments or assignmentsrequiring resubmission (grade R), up to 50% off. If you fail to hand in any assignment, I may assign aone-letter grade penalty in addition to a zero for the assignment.

Required handins for all assignments include a listing, adequate demonstration that your program doeswhat it is supposed to do, and verification of the correctness of the results of your sample programexecution. In most cases, you will also need to explain what you tried to do, what happened duringyour work on the project, and your analysis of the results. Make it clear to me how you responded tothe assignment and what happened while you were working on it. You may have to submit a 3.5" diskwith your code so that I can run your program and data, so keep a few extra disks on hand. I will alsorequire you to publish some things on the Web. Keep backups of everything.

My style requirements are concerned with pragmatics more than strict standards: if your code worksand I can follow it, that is adequate for this course. Your programs must be named as I state in yourproject assignment. I may have to run your programs, so make my job easy or I may not give you thecredit you deserve. I insist that you annotate your listings with references to the stated problemrequirements. I recommend that you put these annotations into your program as comments.


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Norberto F. Ezquerra

http://www.cc.gatech.edu/gvu/people/Faculty/Norberto.F.Ezquerra.html/ (http://www.cc.gatech.edu/gvu/people/Faculty/Norberto.F.Ezquerra.html/)

Graphics, Visualization & Usabilty Center

Georgia Institute of Technology

Title of course:Visualization Techniques in Science and Engineering


Fundamentals of visualization; learning to use viz software tools; learning the differentdisciplines that underlie the field.

List topics of course :

Brief history and rationale for visualization.

Fundamental issues: symbolic and discrete data types, the different disciplines that underliethe field (HCI, graphics, vision).

Graphical concepts: models, rendering, animation.

Representation: Acquired versus symbolic data representations.

Algorithms: Scalar, Vector and Tensor Field visualization.

Multivariate data; multimodality data.

The use of visualization in education ("seeing" concepts in physics,mathematics,engineering)

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http://www.cc.gatech.edu/gvu/people/Faculty/Norberto.F.Ezquerra.html/

http://www.cc.gatech.edu/gvu/people/Faculty/Norberto.F.Ezquerra.html/

HCI concepts: user interface design, interactivity, usability.

Perception conepts: human vision, perception, psychophysics.

Tools: Hardware (including virtual environments), viz, software tools (AVS, Explorer, ApE,etc.).

Applications: Statistics, medicine, meteorology, education.

Case Studies: Invited lectures by practitioners working in different areas of apps of viz.

Lab:

Projects require lots of exploration in the lab; there are lectures however explaining the differentlab tools.

hands-on experience: each student must do to visualize something (data, a process, amodel) of his/her own, or (if the student wants to), using data provided.

Hardware: (including virtual environments), viz,

Software: AVS, Explorer,ApE, etc. Applications: Statistics, medicine, meteorology, education


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General Information: number of hours of course : 3 hours lectures,no lab (but projects are central to the course). kind of media used for teaching the course: white board, electronic media, overhead,... how long "your" hour is : 3 lectures a week, one hour each; plus projects

Audience. describe your students (undergrads, grads, act. level, their background):

Primarily grad students from CS, Mech. Eng., Elec. Eng., Psychology, Literature (butprimarily CS, ME, and EE).

Evaluation. written midterm and/or final: both assignments (how many, example): each student must do one project to visualizesomething (data, a process,a model) of his/her own, or (if the student wants to), using data provided. evaluation scheme: half the grade is from exams, the other half from the project.

Prerequisites: graduate standing and consent of instructor.


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F. David Fracchia, [email protected]

School of Computing Science,

Simon Fraser University, Burnaby, BC, Canada



The course introduces students to the field of scientific visualization.

It stresses the importance and necessity of visualization, and presents

the current approaches and tools for developing visualization systems.

Students will gain valuable knowledge and experience by applying the

lectures to an actual problem/data in another area or discipline (such

as Geography, Physics, Engineering, etc.). This may involve the pairing

of students with other faculty/students.


Topics include: the necessity/importance of visualization and its impact

on science, approaches to visualization (current trends, the role of the

computer scientist, identification of the purpose, data, and audience, user

interface issues), existing tools and techniques for data, future trends, and

social impact. Tufte, Bertin, Ware, Goodman. Applications range from

medical imaging to architecture.

Lab.:

Hardware used in the lab: SGI (Indigo, Indigo2 Extreme, High Impact), Sun

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Software: OpenGL, Motif - and in-house developed systems.

References:

Course text (if any is used): None - compilation of papers.

Selected readings: Visualization conference, TOVCG, SIGGRAPH,...

General references: Visualization, Graphics papers.


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General information: Number of students in course: 10-25 Number of hours for course: 3 lecture hours / week Media usied for teaching your course: Mainly slides, video tape and overheads. Also livedemonstrations as well as web access online through PC or SGI. how long "your" hour is: 50 minutes

Audience: Describe your students (undergrads, grads, actal level, their background):

Course is undergrad/grad combined. All undergrads have a computer graphics background. Most grads have some background. By permission students from philosophy, dance, music, physics, and biology have taken the course and collaborated on projects with computer scientists.

Evaluation: written midterm and/or final: Take home midterm use assignments (how many, example): The assignments are mainly the presentationof visualization papers. The bulk of the work is on a collaborative project involvingfaculty/students from other disciplines. Many of these projects have becomeVisualization papers/case studies! (example: Archaeological Visualization (Vis94 casestudy),Visualizing Philosophical Logic (Vis95 case study), Visualizing FeedingBehaviours of Humpback Whales (vis96 case study)... evaluation scheme:

Assignments/Presentations: 30% Take-home Midterm: 20% Collaborative Project: 50%

Prerequisites: 3rd year course in graphics (4th year prefered), or by premission of instructor for otherdiscplines.


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Andrew J. Hanson

email: [email protected]

Indiana University (Bloomington)

Title of visualization course : Scientific Visualization


Provide info for PhD students in many disciplines (CS, Chem, Physics, Economics, AthleticPerformance, Geology,...) to develop visualization methods and products for use in presentingtheir research ( in their thesis, conferences, publications, etc.).


Cognition and human perceptual properties and response. Tools and the rapid prototypeparadigm. Interface design. Information types (signal data, statistical data, spatial data, volumes).Information display methods and techniques. Case studies with pros and cons.

References:

Visualization Toolkit

Visualization of Natural Phen (Wolf and Yaeger)

Visual Cues (Keller and Keller)

Lab. :

Hands-on experience : Lab. one hour in SGI lab/week.

Hardware : SGI Indy’s, 64 MB memory, 32bit image memory.

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Software : Mathematica, Maple, Matlab,Spyglass, Slicer, S-Plus, NCSA Image, xv,gnuplot. Will add Java and Visualization Toolkit soon.


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General Information: Number of students in course : 30 Number of hours for course : 3 lect, 1 lab Kind of media uses for teaching the course : Overheads, blackboard, videos, computerlive demos. how long "your" hour is : two 75 minute lectures

Audience: Description of students (undergrads, grads, their background):

Grads, PhD candidates in 10 different departments.

Evaluation written midterm and/or final: no used assignments: many small exercises on different systems, including e.g. writing a colortable manipulator directly in X/Motif. evaluation scheme: Homework exercises plus final project by each student, with 1/2 hourclass presentation and written report.

Prerequisites: Active in research, needing visual tools for research work.


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Chris Johnson

Department of Computer Science, University of Uta

http://www.cs.utah.edu/~cs523/ (http://www.cs.utah.edu/~crj/cs523/)

Title of visualisation course: Introduction to Scientific Visualization.

Course Goals:

important definitions and current issues in scientific visualization

commonly used algorithms and techniques in scientific visualization

discussion of examples of visualization in a variety of fields

critique the effectiveness of a scientific visualization

use of at least one scientific visualization software package

know where to locate further visualization resources

complete a project in scientific visualization.


Week 1: Overview of scientific visualization and demonstrations.

Week 2: Simple graphical techniques and programs.

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http://www.cs.utah.edu/~crj/cs523/

Week 3: Data structures in scientific visualization.

Week 4: Computational geometry - surface and volume representations.

Week 5: Visualization software; Geometric modeling systems.

Week 6: Surface visualization techniques.

Week 7: Volume visualization techniques.

Week 8: Vector field visualization.

Week 9: Animation and video.

Week 10: Color, hue, lighting; Presentation of final projects.

Lab:

Hardware: We have a lab consisting of 10 SGI Indigo2 workstations.

Software: NAG Iris Explorer, VTK Visualization Toolkit , IBM Data Explorer, Geomview,a large number of public domain visualization packages/programs.

References:

Course text: Computer Visualization by Gallagher.

Selected readings: Many papers from Visualization ‘9X Conferences.

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general references: The Visualization Toolkit by Schroeder et al.


Examples of Visualisation Techniques-http://www.cs.utah.edu/~crj/cs523/examples.html (http://www.cs.utah.edu/~crj/cs523/examples.html)

Scientific Visualization Projects 1996-http://www.cs.utah.edu/~crj/cs523/projects96/projects96.html (http://www.cs.utah.edu/~crj/cs523/projects96/projects96.html)

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http://www.cs.utah.edu/~crj/cs523/examples.html

http://www.cs.utah.edu/~crj/cs523/projects96/projects96.html

http://www.cs.utah.edu/~crj/cs523/projects96/projects96.html

Extended Course Information:

General Information: Number of students in course: 30-35 Number of hours for course: 3 lecture hours per week. used media (black board, white board, electronic media, overhead, .): Everything ! how long "your" hour is : 50 minutes

Audience:

advanced undergraduates and beginning graduate students in science and engineering. All havesome programming experience, but some do not have any previous graphics experience. Somehave a year’s worth of computer graphics courses.

Evaluation: written midterm and/or final: No! used assignments (how many, example): 5 homework assignments and a visualization project. evaluation scheme: grading homework and projects. prerequisites: good programming background.reasonable math background (calculus,linear algebra, odes, some pdes).


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Dr. Haim Levkowitz

http://www.cs.uml.edu/~haim/ (http://www.cs.uml.edu/~haim/)

University of Massachusetts Lowell

Institute for Visualization and Perception Research

http://www.cs.uml.edu/~haim/ivpr.html (http://www.cs.uml.edu/~haim/ivpr.html)

Title of visualization course: Scientific Data Visualization

Objectives of the course: At the time I (co-) taught it, it was

mostly to teach ‘‘new’’ idea in visualization. Later we shifted goals

to cover classic vis before getting into the eclectic stuff ...

List topics of course: I’d have to dig the details; this goes back 3 years

References:

Selected readings: Two sets of copies of articles from literature

General references: Several recommended, none required

Lab. :

hands-on experience: projects, but no lab

Software for the course : When I taught, we used various home grown software (of theexvis variety)


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http://www.cs.uml.edu/~haim/

http://www.cs.uml.edu/~haim/ivpr.html

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General information: Number of students in course: ~10 Number of hours for course: 3 used media for teaching:

mostly overhead, slides and some projection from laptop.

how long "your" hour is : 50 mins. Audience:

Describe students (undergrads, grads, actual level, their background):

Grads, upper masters/lower doctorate.

Evaluation: written midterm and/or final: Projects, papers used assignments (how many, example):

3-4 assignments (small projects).

Prerequisites: Graphics, equivalent.


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Nelson L. Max

[email protected]

Department of Applied Science, University of California, Davis

http://www.llnl.gov/graphics (http://www.llnl.gov/graphics)


General Information:

Number of students in course: 6

Number of hours for course: 3

media used for teaching your course: Blackboard; occasional videotapes.

how long "your" hour is: 50 minutes

Objectives of the course: Mostly volume and flow visualization.

References:

Course text : Volume Visualization, By Arie Kaufman

Audience:

Describe your students (undergrads, grads, actual level, their background): Graduate,and upper division undergraduate

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http://www.llnl.gov/graphics

Evaluation:

written midterm and/or final: Yes

Prerequisits:

first course in computer graphics

Research:

Research Documents-http://www.llnl.gov/graphics/biblo.html (http://www.llnl.gov/graphics/biblo.html)

Research Software-http://www.llnl.gov/graphics/software.html (http://www.llnl.gov/graphics/software.html)

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http://www.llnl.gov/graphics/biblo.html

http://www.llnl.gov/graphics/software.html

Prof. Nan C. Schaller

Computer Science Department

http://www.cs.rit.edu/~ncs (http://www.cs.rit.edu/~ncs)

Title of course:

Computer Graphics Lab / projects course.

Number of hours for course:

meets for 4 50-minute hours a week for 10 weeks.

Teachers:

professors from other departments sponsor projects.

Topics:

not a formal visualization course but may actually have visualization projects.

Lab. Hardware:

Sun’s and SGI’s as platforms and the students select what software they will use.

Courses/Assignments-http://www.cs.rit.edu/~ncs/Courses.html (http://www.cs.rit.edu/~ncs/Courses.html)

Lab Project Examples-http://www.cs.rit.edu/~icss571/Projects962.html (http://www.cs.rit.edu/~icss571/Projects962.html) References Classes

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http://www.cs.rit.edu/~ncs

http://www.cs.rit.edu/~ncs/Courses.html

http://www.cs.rit.edu/~icss571/Projects962.html

Syllabus

GENERAL INFORMATION

Instructor: Nan Schaller E-mail: [email protected] or [email protected] URL: http://www.cs.rit.edu/~ncs/homepage.html

PREREQUISITE : Introduction to Computer Graphics/Graphics I

TEXT : No required text.

REFERENCES (on reserve at the library):

Avro, Graphics Gems II (T385.G6972 1991)

Bonatouch and Bouville, Photorealism in Computer Graphics (T385.P49 1991)

Durrett, Color and the Computer. (T385.C548 1987)

Foley, Van Dam, etal, Computer Graphics: Principles and Practices. (T385.C587 1990)

Glassner, Graphics Gems (T385.G697 1990)

Green, Parallel Processing for Computer Graphics (T385.G739 1991)

Kirk, Graphics Gems III (T385.G6973 1992)

Benoit Mandelbrot, The Fractal Geometry of Nature. (QA447.M357)

Peitgen & Richter, The Beauty of Fractals. (QA447.P45)

Peitgen & Saupe, The Science of Fractal Images. (QA614.86.S35)

Prusinkiewicz, The Algorithmic Beauty of Plants. (QH491.P773)

Rogers and Adams, Mathematical Elements For Computer Graphics. (T385.R6 1990)

1992 Symposium on Interactive 3D Graphics (T385.S895 1992)

Watkins, Programming in Three Dimensions: 3D Graphics, Raytracing and Animation (T385.W3771992)

Watt and Watt, Advanced Animation and Rendering Techniques (T385.W378 1992)

Watt, 3D Computer Graphics (T385.W382 1993)

Whitman, Multiprocessor Methods for Computer Graphics Rendering (T385.W54 1992)

Wilt, Object-Oriented Raytracing in C++ (T385.W56 1994)

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1991 Siggraph Conference Proceedings (T385.C597 1991)

1992 Siggraph Conference Proceedings (T385.C597 1992) (also available on CD-ROM)

1993 Siggraph Conference Proceedings (T385.C597 1993) (also available on CD-ROM)

1994 Siggraph Conference Proceedings (Schaller Folder #1) (also available on CD-ROM)

1995 Siggraph Conference Proceedings (Schaller Folder #2) (also available on CD-ROM)

1996 Siggraph Conference Proceedings (Schaller Folder #3)

(T385.A22 1990 Vol. no.) 1990 ACM/SIGGRAPH Course Notes:

2. Color and Computer Graphics. 5. Generation of Three-Dimensional Data for Computer Image Synthesis. 6. Stereographics. 10. Character Animation by Computer. 12. Solid Modeling: Architectures, Mathematics, and Algorithms. 15. Fractals: Analysis and Modeling. 21. Radiosity. 24. Advanced Topics in Ray Tracing. 28. Parallel Algorithms and Architectures for 3D Image Generation.


10. Color Theory and Models for Computer Graphics and Visualization 15. Curve and Surface Design: From Geometry to Applications 16. Particle System Modeling, Animation and Pysically Based Techniques


1. Character Motion Systems 3. Developing Large-scale Graphics Software Toolkits 16. Stereo Computer Graphics with Application to Virtual Reality 22. Making Radiosity Practical 24. Graphic Design for User Interfaces 40. Modeling in Computer Graphics 41. Volume Visualization 42. Global Illumination 44. Procedural Modeling and Rendering Techniques 60. An Introduction to Physically Based Modeling 82. Curve and Surface Design: From Geometry to Applications

NOTE: 1994 ACM/SIGGRAPH Course Notes are available on CD-ROM

NOTE: See also library guides: Guide to Computer Graphics Sources and Online ComputerDatabases Related to Computer Science Available through Wallace Library.

POSSIBLE TOPICS:

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The following is a non-inclusive list of possible research and programming project areas. You maychoose to study one or more of these areas:

- Animation - Antialiasing - Lighting and Reflectance Models - Geometric Modeling/Solid Modeling - Global Illumination - Fractals - Interactive Techniques - Parallel Graphics Algorithms - Shading and Color Models - Stereographics - Three Dimensional Modeling Methods - Visible Surface Algorithms

FINAL PROJECT

You (or your group) is to design, implement, and demonstrate a significant computer graphics systemusing the color capabilities of the Sun and/or SGI workstations. (Requests to work on other equipmentwill be considered.) The system should include one or more advanced techniques found in the topic ofyour research paper (or papers if you are working in a group) and include a good user interface design.In addition, you must provide appropriate documentation. Part of the grade for the project will bedetermined by a peer evaluation of your system.

RRESEARCH PRESENTATION :

The topic of your research paper is to be presented to the class in a scheduled one-half hour block oftime. You should be prepared to answer questions from the class, and be prepared to lead a discussionon the topic. Your presentation will be graded by the instructor and by an aggagate of critiques byyour peers.

CLASS PARTICIPATION :

Topics to be discussed will be posted prior to their presentation. All students will be required to readabout the material presented, and will be prepared to ask questions on the topics presented.


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ClassesMonday, December 2: Introduction Handouts Talk about papers, projects, and project journals (general) Introduce Marla Schweppe, Film and Video (X2780) (e-mail: mks) Introduce Paul Craig, Chemistry (X6145) (e-mail: [email protected]) Discuss Paper/Project Ideas Informal Group Organization

Wednesday, December 4: Discuss Paper/Project Ideas Graduate Journals Entry Due Introduce John Kester, Computer Science Librarian (X2238) (e-mail: [email protected]) (Bib. Lab., Third Floor of Library, 6 P.M.)

Monday, December 9: ***PAPER PROPOSALS DUE***, either hand into CS office or e-mail to "ncs". NO FORMAL CLASS, I suggest that you come to class anyway and meet with perspective team members on project proposals. Discuss Paper/Project Ideas

Wednesday, December 11: ***FINAL PAPER PROPOSAL APPROVAL*** Graduate Journals Entry Due Discuss Paper/Project Ideas Marla Schweppe, Computer Animation program and Alias Wavefront demo (mks, x2780, 6 PM) (7B-1226)

Monday, December 16: ***PROJECT PROPOSALS DUE, 1 per group*** Discuss Project Ideas Firm Up Project Groups Introduce Jack Slutzky (X5614, [email protected]) who will be talking about Animation, etc. (5:30 P.M.), (ORDER VCR)

Wednesday, December 18: ***FINAL PROJECT APPROVAL*** Graduate Journals Entry Due Discuss Project Ideas Marla’s student (student arrange through Marla Schweppe X2780) will showing discussing student work (ORDER VCR) (5:30) ******* CHRISTMAS BREAK ******** Monday, January 6: Presentation by Peter Anderson (Graduate Computer Science) (X2979) Pixel Shuffle (6 PM) Wednesday, January 8: Graduate Journals Entry Due ***PAPER DUE*** Tour of Remote Sensing Lab (Imaging Science) by ???? (Bldg. 76 - 2101). 5:30 PM (X5170, John Schott) (e-mail: [email protected]) Monday, January 13: Graduate Journals Entry Due Presentation by Alejandro Engel (Mathematics) (X2123) Pixel Selection Algorithm (5:30 PM) Discuss making technical presentations. Discuss projects.

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Wednesday, January 15: Paper Presentations - Rachelle Schiffli - Color Hoa Thang - GUI Vadim Sanberg - GUI/interactive Techniques Christine Maglio - texture mapping Monday, January 20: Graduate Journals Entry Due Paper Presentations Robert Andreasen - OpenGL Kashif Ansari - OpenGL Joseph DiPiano - VRML Michael Gentile - Animation Techniques Wednesday, January 22: Paper Presentations Dina D’albenzio - 3D Modeling Dan Davison - 3D Modeling Eugene Shapiro - 3D Modeling Jerry DaSilva - BSP Monday, January 27: Graduate Journals Entry Due Paper Presentations Kevin Day - Particle Systems Josh Schricker - Particle Systems Michael Zucca - Particle Systems Joseph Mikolajczyk - Stereographics Wednesday, January 29: Paper Presentations Michael Kelly - Global Illumination Fred Adjei - Ray Tracing Philip Sansone - Ray Tracing Wade Stiell - Ray Tracing Monday, February 3: Graduate Journals Entry Due Paper Presentations Sherri Smith - Fractals and chaos Jason Smith - Fractals Alain Leroy - Simulation (?) Donald Smith - Simulation Wednesday, February 5: Bob Kozdemba, SGI (5 PM, please be on time!) Show and Discuss Projects - where you are and Problems Monday, February 10: Graduate Journals Entry Due Presentation of Video: NOVA: The Science of Chaos (ORDER VCR) Discuss Projects and Problems Wednesday, February 12: Discuss Projects and Problems Multimedia Presentation by Steve Jacobs (X7803) at 5 PM Monday, February 17: Graduate Journals Entry Due Discuss Projects and Problems Wednesday, February 19: Discuss Projects and Problems Wednesday, February 26: (In ICL1/This is reading day) Project Presentations: Electronic submittal of project due, including documentation Hardcopy of user and technical documentation due Course, Peer, and Self Evaluations due

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Dale A. Schoenefeld-http://www.ens.utulsa.edu/~schoend/ (http://www.ens.utulsa.edu/~schoend/)

Mathematical and Computer Science-http://euler.mcs.utulsa.edu/bulletin/mcs.home.html (http://euler.mcs.utulsa.edu/bulletin/mcs.home.html)

Title of visualization course: Advanced Computer Graphics (which included graphics andvisualization ,graduate level course but I assumed no prerequisites).


This is a computer graphics course that will also include a significant introduction tovisualization. At the end of the course, students will be able to use most of OpenGL, use a subsetof Open Inventor, and use a subset of the Visualization Toolkit. Students will also be able toarticulate many algorithms and techniques for computer graphics rendering visualization. Thiscourse is not a research course. However, the course will support a variety of other researchefforts and spawn new research efforts by providing a survey of tools, techniques and concepts.

Objectives/Prerequisits

List topics of course : about 20 lectures for OpenGL and Open Inventor, about 10 lectures forVisualization and VTK

Complete overview on course-http://www.ens.utulsa.edu/~schoend/cs7413/index.html (http://www.ens.utulsa.edu/~schoend/cs7413/index.html)

Lab.:

no formal labs but students are assigned four projects and have access to Windows 95, Sun,and HP workstations in public labs.

Software: OpenGL, Open Inventor , VTK(Visualization Toolkit)

References


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http://www.ens.utulsa.edu/~schoend/

http://euler.mcs.utulsa.edu/bulletin/mcs.home.html

http://www.ens.utulsa.edu/~schoend/cs7413/index.html

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General information: Number of students in course: 18 during Fall 1996 Number of hours for course: 30 75-minute lectures Media used for teaching the course: much blackboard, but I am getting there with lecturepresentations supplemented with multimedia organization and integration provided by webtechnologies

Audience: Describe your students (undergrads, grads, their background): first year graduatestudents, fairly strong mathematics background

Evaluation: written midterm and/or final: two exams, four individual projects, one group project, onepresentation use assignments (how many, example): see evaluation evaluation scheme

Prerequisites: no graphics, just programming, OO experience, and data structures

Future:

My next round will be a full course dedicated to understanding and using the Schroeder book -- the whole book. I know there are a lot of other topics that one could be including but my goal will be to have students be proficient with hands on tools that will be useful in support of visualizations that are typical done in engineering, science, and mathematics


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About the Visualization ToolKit (VTK)

Refernces:

W. Schroeder, K. Martin, B. Lorensen, The Visualization Toolkit, An Object-Oriented ApproachTo 3D Graphics, Prentice Hall, 1996. ISBN 013199837-4.

Description:

The Visualization ToolKit (vtk) is a software system for 3D Computer Graphics andVisualization. Vtk is "public domain", includes a C++ class library, and a Tcl implementationbased on the class library. Vtk has been implemented on nearly every Unix-based platform andWindows NT and 95. Vtk can use many renderers including openGL or Mesa. The design andimplementation of the vtk library has been strongly influenced by object-oriented principles. Thegraphics model in vtk is at a higher level of abstraction than rendering libraries like openGL. Thismeans it is much easier.

to create useful graphics and visualization applications. In vtk applications can be written directlyin C++ or in Tcl, an interpretive language developed by John Ousterhout. In fact, using Tcl andTk, a graphical user interface toolkit based on Tcl, it is possible to build useful applicationsreally, really fast.The software is a true visualization system. Vtk supports a wide variety ofvisualization algorithms including scalar, vector, and tensor visualization, and advancedmodelling techniques like implicit modelling, polygon reduction, and Delaunay triangulation.Vtk has been used in many application contexts ranging from algorithm visualization andscientific visualization to medical imaging -- including the National Library of Medicine VisibleHuman project.

Further information on VTK-http://public.kitware.com/VTK/ (http://public.kitware.com/VTK/)


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http://public.kitware.com/VTK/

Objectives/Prerequisits of the course

About the course:

First half (actually it got to be almost 2/3) of the course was spent understanding the underlyingconcepts and the use of OpenGL and Open Inventor. The last part of the course was spent in thesame way with VTK (the visualization toolkit) - the Schroeder, etal. VTK book was the requiredtext for the visualization part of the course and I found it to be very useful.

Objectives:

Computer graphics has become a "huge" discipline. This course is a computer graphics coursethat will include a significant introduction to visualization. At the end of the semester, the studentwill be able to:

1. proficiently use most of OpenGL, a subset of Open Inventor, and a subset of the VisualizationToolkit.

2. articulate many computer graphics rendering algorithms and techniques in detail. Thealgorithms will be primarily polygonal-based with some exposure to alternate algorithms.

3. articulate many visualization algorithms and techniques in detail.

4. articulate the application of object oriented techniques to the design of software and datastructure architectures for computer graphics and visualization.

5. articulate the application of computer graphics and visualization techniques in a variety ofapplication contexts

Prerequisits:

This course will assume that students know elementary data structures, know how to program inthe C language, and that students have experience with (or are willing to "adapt" to)object-oriented design methodologies and C++. Students should have a background inmathematics that includes geometry, trigonometry, linear algebra, and calculus. This course willNOT assume previous experience with computer graphics and, in particular, our undergraduatecourse will not be a prerequisite.

About OpenGL


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Evaluation scheme and Requirements

Required reading will be assigned. A total of 600 points can be accumulated as follows: four projects (4 * 25 = 100), one major project (100), one presentation (100), one midterm examination (150), and one final examination (150). A project is due at the beginning of the class lecture on the due date. Projects turned in within one week of the due date will be assessed a 50% penalty. No points will be awarded for a project completed later than one week after the due date of the project. Make-up examinations will normally not be given and a grade for any examination missed for reasons determined valid by the instructor will be assigned after consideration of the subsequent final examination. When possible, the student should contact the instructor before missing an examination.


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About OpenGL

References:

Neider, J. et al., OpenGL Architecture Review Board, OpenGL Programming Guide, The OfficialGuide to Learning OpenGL, Release 1, Addison Wesley, 1993, ISBN 0-201-63274-8.

Description:

OpenGL® is a software interface that generates interactive 2D and 3D computer graphics for avariety of applications. OpenGL is a software

standard developed by Silicon Graphics. OpenGL is designed to be independent of operatingsystem, window system, and hardware operations, and it is supported by many vendors. Theinterface consists of about 120 distinct commands. The functions enable programmers to buildgeometric models, view models interactively in 3D space, control color and lighting, manipulatepixels, and perform such tasks as alpha blending, anti-aliasing, creating atmospheric effects, andtexture mapping. OpenGL is available at no cost for Windows 95 and Windows NT sincedynamic link libraries are included or are available. Many implementations of OpenGL,representing a wide cost range, are available for UNIX systems. Mesa is a "public domain" 3-Dgraphics library which uses the OpenGL API (Application Programming Interface). Although nottechnically an "implementation" of OpenGL, Mesa may be a valid alternative to OpenGL. Mesacan be installed on almost any Unix/X, Windows 3, 95, NT, or Macintosh system. Mostapplications written for OpenGL can be recompiled for Mesa without making any source code changes.

interesting links on Open GL/Mesa:

About Open GL: http://www.sgi.com/software/opengl/ (http://www.sgi.com/software/opengl/), About Mesa: http://www.ssec.wisc.edu/~brianp/Mesa.html (http://www.ssec.wisc.edu/~brianp/Mesa.html)

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http://www.sgi.com/software/opengl/

http://www.ssec.wisc.edu/~brianp/Mesa.html

About Open Inventor

Refernces:

Wernecke, Josie, Open Inventor Architecture Group, The Inventor Mentor, ProgrammingObject-Oriented 3D Graphics with Open Inventor, Release 2, Addison Wesley, 1994. ISBN0-201-62495-8.

Description:

Open Inventor(TM) is an object-oriented toolkit for developing interactive, 3D graphicsapplications. Open Inventor is a software standard developed by Silicon Graphics. Open Inventoris built on OpenGL. While OpenGL is a powerful graphics software interface for graphicshardware, Open Inventor is a higher level object-oriented toolkit that further aids the programmerby providing a 3D database, a built in event model for user interaction, and the ability to printobjects and exchange data with other graphics formats. Written in C++, Open Inventor alsoincludes C bindings. Open Inventor is available from Template Graphics Software and fromPortable Graphics. The cost varies depending on the environment; but, e.g., the retail cost of aWin32 version from Template Graphics is $995. Applications for Open Inventor includeanimation, CAD, mechanical CAE, medical and scientific imaging, molecular modeling,simulation, scientific data visualization and virtual reality. Open Inventor also serves as the basisfor the VRML (Virtual Reality Modeling Language) standard. The Virtual Reality ModelingLanguage (VRML) is a language for describing multi-participant interactive simulations -- virtualworlds networked via the global Internet and hyperlinked with the World Wide Web. All aspectsof virtual world display, interaction and internetworking can be specified using VRML. It is theintention of its designers that VRML become the standard language for interactive simulationwithin the World Wide Web. OpenGL and Open Inventor are also the basis for othervisualization tools. For example, The Numerical Algorithms Group Ltd (NAG), worldwidelicensee of the Silicon Graphics software, has chosen a Template Graphics implementation ofOpen Inventor for porting IRIS Explorer to non-Silicon Graphics platforms. IRIS Explorer is a"relative" of the Advanced Visualization System (AVS) available on HP workstations in KEH L2.

intersting link for Inventor-http://www.sgi.com/software/inventor (http://www.sgi.com/software/inventor/)


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http://www.sgi.com/software/inventor/


Open GL Reference: Neider, J. et al., OpenGL Architecture Review Board, OpenGL Programming Guide, TheOfficial Guide to Learning OpenGL, Release 1, Addison Wesley, 1993, ISBN0-201-63274-8.

Open Inventor Reference: Wernecke, Josie, Open Inventor Architecture Group, The Inventor Mentor, ProgrammingObject-Oriented 3D Graphics with Open Inventor, Release 2, Addison Wesley, 1994. ISBN0-201-62495-8.

Visualization ToolKit (VTK) Reference: W. Schroeder, K. Martin, B. Lorensen, The Visualization Toolkit, An Object-OrientedApproach To 3D Graphics, Prentice Hall, 1996.ISBN 013199837-4


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Roni Yagel, Assistant Professor

http://www.cis.ohio-state.edu/~yagel/ (http://www.cis.ohio-state.edu/~yagel/)

Department of Computer and Information Science, The Ohio State University

http://www.cis.ohio-state.edu/~yagel/788/syllabus.html (http://www.cis.ohio-state.edu/~yagel/788/syllabus.html)

Title of course: Volume Graphics


A survey of existing methods for volume representation, manipulation, and rendering.Presentations of major algorithms and review of important scientific, biomedical, and industrial applications.


Class Schedule

Reading List

Lab:

Hardware: HP workstations and Silicon Graphics machines.

Software: X11, Motif, Starbase or OpenGL.

Evaluation:

presentation: 30%

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http://www.cis.ohio-state.edu/~yagel/

http://www.cis.ohio-state.edu/~yagel/788/syllabus.html

project: 30%

Project-Evaluation

quizzes: 20%

class participation: 20%

Title of another course: Introduction to Volume Visualization and its Medical Applications

B.Lorensen, U.Tiede, and R.Yagel,http://www.cis.ohio-state.edu/~yagel/vbc96.html (http://www.cis.ohio-state.edu/~yagel/vbc96.html)

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http://www.cis.ohio-state.edu/~yagel/vbc96.html

Schedule

In Postscript file one can see how the presentations made in class cover the volume graphics pipeline.

Week 1: March 25 Introduction to 3D Graphics

Week 2: April 1 Introduction to Volume Graphics

Week 3: April 8

Surface Tracking

Week 4: April 15

Marching and Dividing cubes

Discrete spaces Voxelization

Week 5: April 22 Forward I: BTF and FTB Forward II: Shearing Forward III: Splatting

Week 6: April 29 Shading Backward I: Ray casting Backward II: Ray tracing

Week 7: May 6 Illumination I: Blinn Illumination II: Kajiya Illumination III: Kruger

Week 8: May 13 Hybrid I: V-buffer Hybrid II: Ebert Modeling I : Hypertextures

Week 9: May 20 Modeling II : Greene Modeling III : Kajiya Efficient Forward

Week 10: May 27 Memorial Day - NO CLASS Efficient Backward Parallel Volume Rendering

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Volume Graphics - TOPICS

Introduction

Review of relevant material in 2D and 3D graphics.

Overview of volume rendering

background

definitions

representation

rendering

Surface extraction

Surface tracking

Marching and dividing cubes

Discrete spaces

3D discrete topology

Voxelization algorithms

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Rendering

Forward methods: BTF and FTB, sharing, splatting

Backward methods: ray casting

Hybrid methods: V-buffer

Illumination models

Absorption only models

Single scattering models

Multi scattering models

Shading

Modeling

Advanced topics

Acceleration of volume rendering

Irregular grids

Parallel algorithms

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Reading List

The following reading list covers the material that you should be reading as we cover material in class.I expect you to keep up with the reading as we cover material in class; there will not be specificreading assignments given. The reading list is arranged to correspond with the sequence that materialwill be covered in class.

Volume Visualization, Arie E. Kaufman (ed.), IEEE computer Society Press, 1990. (recommended).

READINGS

Introduction to 3D Graphics See slides on 2D/3D graphics Introduction to Volume Graphics See slides on volume graphics (Postscript). See slides on volume rendering (Postscript).

W3.1: Surface Tracking D. Gordon, J. K. Udupa, Fast Surface Tracking in Three-Dimensional Binary Images, ComputerVision, Graphics and Image Processing, vol. 45, No. 2, February 1989. G. Frieder, G.T. Herman, C. Meyer, and J. Udupa, "Large Software Problems for SmallComputers: An Example from Medical Imaging", IEEE Software, September 1985, pp. 37-47. W3.2: Marching and Dividing cubes H. E. Cline, W. E. Lorensen, S. Ludke, C. R. Crawford, and B. C. Teeter, Two Algorithms for theThree-Dimensional Construction of Tomograms, Medical Physics, vol 15, No. 3, May/June 1988,pp. 320-327. G. Wyvill, C. McPheeters, and B. Wyvill, Soft objects, EUROGRAPHICS’87. W3.3: Discrete spaces T.Y. Kong and A. Rosenfeld, Digital Topology: Introduction and Survey, Computer Vision,Graphics, and Image Processing, 48, pp. 357-393, 1989.

W4.1: Voxelization D. Cohen and A. Kaufman, Scan-Conversion Algorithms for Linear and Quadratic Objects, inVolume Visualization, A. Kaufman (ed.), pp. 280-301. A. Kaufman, Efficient Algorithms for 3D Scan-Conversion of Parametric Curves, Surfaces, andVolumes, Computer Graphics, vol. 21, No. 4, July 1987, pp. 171-179 W4.2: Forward I: BTF and FTB G. Frieder, D. Gordon, and R. A. Reynolds, Back-to-Front Display of Voxel-Based Objects,IEEE Computer Graphics and Applications, 5(1)52-60, January 1985. R. A. Reynolds, D. Gordon, and L. S. Chen, A Dynamic Screen Technique for Shaded GraphicDisplay of Slice-Represented Objects, June 1987, Computer Vision, Graphics and ImageProcessing, vol 38, No. 3, pp. 275-298. W4.3: Forward II: Shearing R. A. Drebin, L. Carpenter, and P. Hanrahan, Volume Rendering. Computer Graphics,22(4):64-75, August 1988. Hanrahan, P.,"Three-Pass Affine Transforms for Volume Rendering", Computer Graphics,

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Volume 24, No. 5, November 1990, pp. 71-77.

W5.1: Forward III: Splatting Westover, Footprint Evaluation for Volume Rendering. Computer Graphics, 24(4):367-376,August 1990. W5.2: Shading R. Yagel, D. Cohen and A. Kaufman, Context Sensitive Normal Estimation for Volume Imaging.In Scientific Visualization of Physical Phenomena, N. M. Patrikalakis editor, Springer-Verlag,June 1990, pages 211-234. U. Tiede, K. H. Hoehne, M. Bomans, A. Pommert, M. Riemer, and G. Wiebecke, Investigation ofMedical 3D-Rendering Algorithms, IEEE Computer Graphics & Applications, vol. 10, No. 3,March 1990, pp. 41-53. W5.3: Backward I: Ray casting M. Levoy, Display of Surfaces from Volume Data. IEEE Computer Graphics and Applications,8(5):29-37, May 1988.

W6.1: Backward II: Ray tracing R. Yagel, A. Kaufman, and D. Cohen, Discrete Ray Tracing, IEEE Computer graphics &Applications, 12, 5, September 1992, pp. 19-28. W6.2: Illumination I: Blinn Blinn J.F., Light Reflection Functions for Simulation for Clouds and Dusty Surfaces, ComputerGraphics, 16, 3, 21-29, July 1982. W6.3: Illumination II: Kajiya Kajiya J.T., and von Hrzen B.P., Ray Tracing Volume Densities, Computer Graphics, 18, 3,165-174, July 1984.

W7.1: Illumination III: Kruger Kruger W., The application of transport theory to visualization of 3-D scalar data fields",Computers in Physics, Jul/Aug 1991, pp. 397-406. W7.2: Hybrid I: V-buffer C. Upson and M. Keeler, V-BUFFER: Visible Volume Rendering. Computer Graphics,22(4):59-64, August 1988. W7.3: Hybrid II: Ebert Ebert, D. S., Parent, R. E.,"Rendering and Animation of Gaseous Phenomena by Combining FastVolume and Scanline A-buffer Techniques", Computer Graphics, Vol.24, No. 4, August 1990,pp. 357-366.

W8.1: Modeling I : Hypertextures N. Greene, Voxel Space Automata: Modeling with Stochastic Growth Processes in Voxel Space,Computer Graphics, 23, 3, 175-184, July 1989. W8.2: Modeling II : Green K. Perlin, E. M. Hoffert, Hypertexture, Computer Graphics, 23, 3, 253-262, July 1989. W8.3: Modeling III : Kajiya J. T. Kajiya, T. L. Kay, Rendering Fur with Three Dimensional Textures, Computer Graphics,23,3, 271-280, July 1989.

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W9.1: Efficient Forward D. Laur and P. Hanrahn, Hierarchical Splatting:A Progressive Refinement Algorithm for VolumeRendering, Computer Graphics, 25, 4, July 1991, pp. 285-288. W9.2: Efficient Backward Yagel R. and Kaufman A. "Template-Based Volume Viewing", EUROGRAPHICS’92,Cambridge England, September 1992. M. Levoy, Efficient Ray Tracing of Volume Data, ACM Transactions on Graphics, 9, 3, July1990, pp. 245-261. W9.3: Parallel Volume Rendering R. Machiraju, and R. Yagel, Efficient Feed-Forward Volume Rendering Techniques for Vectorand Parallel Processors, Proceedings SUPERCOMPUTING’93, Nov. 1993. Schroder, P., Stoll, G.,"Data Parallel Volume Rendering as Line Drawing", Proceedings of 1992Workshop on Volume Visualization, October 1992, Boston, MA, pp. 25-32.

W10: Volume Rendering Irregular Grids Max N., Hanrahan P. and Crawfis R. "Area and Volume Coherence for Efficient Visualization of3D Scalar Functions". Computer Graphics, 24, 5, December 1990, pp. 27-33. Garrity, "Ray Tracing Irregular Grids", Computer Graphics, 24, 5, December 1990.


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Projects

Every student has to implement one of the projects listed bellow. The following list of projects is byno means a final list. We might come up with some more topics in the future. I encourage you to comeup with your own ideas for a project not listed here! When you have picked a project: - send me emailto sign up for a specific project. - come and see me in my office hours to discuss the project.

Directed Projects These projects have been proposed by some members of the Volume Graphics group. They will beavailabe to advise, consult, and collaborate on these.

Incremental Image Deformation Surface Crawler Parallel Volume Rendering

Undirected Projects Dividing cubes. Implement the dividing cubes algorithm and display the list of points. Marching cubes. Implement some variation on the marching cubes algorithm and display the listof triangles. (2) Surface tracking. Implement a surface tracking algorithm. Point display. Implement a fast display program that accepts a list of points and efficientlydisplay them by using splatting. Transfer functions. Devise several transfer function and display images rendered by eachmethod. (1-2) Transfer function tool. Implement a tool for interactive definition of various types oftransfer functions. Shear-based rendering. Implement an efficient shear-based volume rotation and display.Explore 1D, 2D, and 3D sampling or parallel implementation. (1-2) Voxelization of algebraic surfaces. Write an algorithm for the efficient voxelization ofsome algebraic surfaces. Fuzzy voxelization. Implement an efficient algorithm for the voxelization of fuzzy (antialiased)objects (sphere, polygon). Slice-based voxelization. Implement an efficient slice-based voxelization. (2) Terrain visualization. Render voxel-represented terrain models. Compositing schemes. Implement several compositing schemes such as linear and exponentiallight attenuation. Polygonal slicing. Extract an the voxels on the surface of a polygonal mesh intersecting a voluemdataset. Irregular grid. Ray cast an arbitrary 2D irregular (possibly concave) grid. Proximity clouds. Implement the 3D space leaping by proximity clouds. PC rendering. Implement a volume renderer on a PC using avilable graphics libraries. Volumetric fractal. Voxelize and visualize a Julia set fractal. Distributed rendering. Implement a PVM-based distributed splatter or ray caster.

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Incremental Image Deformation (DavidReed)

Assume you have an image divided into regions by a regular grid. Assume also that somechangeoccurs in some of the regions. Implement a renderer that incrementally updates images as the regulargrid deforms. Assume that initially the grid is regular and then vertices are allowed to move (fairlysmall movement). Assume that the lines of the grid never end up crossing another line. Based on thevertex movements from one frame to the next, determine which parts (pixels) of the image will bemodified and rerender only these pixels. Note, it might be easier or more efficient to update slightlymore pixels that actually need to be updated (i.e. some neigboring pixels that are not modified may bererendered).


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The Surface Crawler (Yair Kurzion)

Given a volume with density values, generate a triangulation of some iso-surface. Given three pointson the surface, repeat the following stages: 1. For each point, calculate a repression force from itsneighbor points. 2. move the point (following the iso-surface) in the given direction. 3. If three pointsare too far away, spawn three new points and subdivide the triangle into four triangles like this: /\ /\ / \===>> /__\ / \ /\ /\ /______\ /__\/__\ Repeat until all the points on the surface are close enough to eachother (below some threshold). One problem if of course: how do you know that the triangulation hititself on the far side of the surface. If you maintain a list of the vertices on the boundary of thetriangulation, you can check that two vertices on the boundary are close , and maybe connect them byan edge. Or, maybe, you can use a different sub-division scheme. Similar to the Delauneytriangulation - adding a point in the middle of a triangle involves possible flipping of the edges of thattriangle. This may turn out to be easier on the boundary. The resulting triangulation is a roughestimation of the surface by triangles and a rough subdivision of the surface into equal-size triangles.


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Parallel Volume Rendering (Asish Law)

Write a GUI for our parallel renderer running on the T3D, and display the images in real time.Improve our parallel renderer by emoloying shmem routines instead of pvm sends and recvs. Thereare also some algorithmic optimizations that needs to be done. What the student will gain and learn:(S)he will have a hands-on experience working on a parallel machine, along with considerablelearning of message-passsing libraries. The person will also learn about our active-ray and ray-frontalgorithms, and work on them.


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Project-Evaluation

List of Labs and Weights

There will be only one lab project (30%)

Evaluation/Grading Criteria:

Grading of the lab project will be based on the following:

70%: Correctness and adherence to assignment specification. This includes any error checking specified in the labhandout. If no error checking is specified, then you don’t have to do it

10%: Readability and structure of code, use of comments, indentation, etc.

10%: Efficiency and speed (only an issue if its very inefficient)

10%: Adherence to lab procedures (submitting, naming conventions, etc.)

Grading will be done by the instructor at the demo time. If you can’t resolve the dispute with the grader, then see me.However, in order to maintain consistent grading for everyone in the class, I am not very inclined to alter grades that areassigned by the grader.


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Contributors:

Voluntary contributors to the content of this web site are:

Polly Baker (University of Illinois at Champaign) [email protected]

Gitta Domik (University of Paderborn) [email protected]

Georges Grinstein (University of Massachussetts Lowell) [email protected]

Thomas T. Hewett (Drexel University) [email protected]

Mike McGrath (School of Mines) [email protected]

Scott Owen (Georgia State University) [email protected]

These volunteers have agreed to referee the web site:

Ken Brodlie (University of Leeds) [email protected]

Marie-Theresa Rhyne,Lockheed Martin

(US Environmental Protection AgencyScientific Visualization Center)

[email protected]

Bill Hibbard (University of Wisconsin) [email protected]

BarabaraMones-Hattal

(University of Washington) [email protected]

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