agora center, p.o. box 35 finland

Rendering recognizably unique textures

Janne V. Kujala and Tuomas J. LukkaHyperstructure Group

Agora Center, P.O. Box 35FIN-40014 University of Jyvaskyla

[email protected], [email protected]

Abstract

We present a perceptually designed hardware-accelerated algorithm for generating unique backgroundtextures for distinguishing documents. To be recogniz-able, the texture should produce a random feature vectorin the brain after visual feature extraction.

Our motivating example is a hypertext user inter-face which shows a fragment of a link’s target in themargin. Upon traversing the link, the fragment expandsto fill the screen. Our goal is to avoid user disorien-tation by texturing each document with a unique back-ground so that a document can easily be recognized froma fragment. The user should be able to learn the tex-tures of the most often visited documents, as per Zipf ’slaw.

The results of an initial experiment show that thegenerated textures are indeed recognizable. We discussa method for enhancing text readability by both provid-ing fast, interactive zooming and unnoticeably bleachingthe background around text.

1. Introduction

In this article, we introduce the use of procedurallygenerated unique backgrounds as a visualization of doc-ument identity. In our approach, each document hasa different, easily distinguishable background texture.The user can thus identify an item at a glance, even ifonly a fragment of the item is shown, without readingthe title (which the fragment may not even show).

фООFooFooIn the following sections, we first review relatedwork on texturing. Next, we discuss an example userinterface to a hypertext structure. Then, we formu-late general principles for designing recognizable back-grounds and present our hardware-accelerated imple-mentation. Following this, we discuss enhancing textreadability on such backgrounds and practical experi-ences and conclude.

2. Related work

The texture of a surface, taken literally, is itstranslation-invariant statistical microstructure. Incomputer graphics, the word texturing is used in a

somewhat looser sense[6, 16]: it refers to mapping 2Darrays of numerical values onto graphics primitives suchas polygons or Bezier patches, modifying their renderedappearance in some way (coloring[6], bump mapping[3],etc.). Textures have been synthesized in several ways:procedurally[8, 22, 26, 27, 29], using other textures as astarting point[17], perceptually, for visualizing surfaceorientation[19, 34, 35] and scalar or vector fields[37],and statistically, as samples from a probability distri-bution on a random field[9, 12, 36]. As a particularexample, the Starfish[32] program uses procedural tex-turing for generating random wallpaper textures.

Psychophysical studies on texture perception havemostly concentrated on pre-attentive visual texture

discrimination[1, 20], the ability of human observersto effortlessly discriminate pairs of certain textures.Julesz[21] proposed that texture discrimination couldbe explained by the densities of textons, fundamentaltexture elements, such as elongated blobs, line termi-nators, and line crossings. However, the textons arehard to define formally.

Simpler, filtering-based models can explain texturediscrimination equally well[2]. There is also physiolog-ical evidence of the filtering processes: in the visualcortex, there are cells sensitive to different frequencies,orientations, and locations in the visual field[5].

On a higher level, the correlations between localfeatures are combined by forming contours and possi-bly other higher-level constructions[31]. These higherlevels are not yet thoroughly understood; some theo-ries[18] assume certain primitive shapes whose struc-ture facilitates recognition.

There have also been studies on mapping textureappearance to an Euclidian texture space[14]: in theseexperiments, three dimensions have been sufficient toexplain most of the perceived differences for artificialtextures. However, the texture stimuli have been some-what simple (no color, lack of frequency-band interac-tion, etc.). For some natural texture sets, three dimen-sions have also been sufficient[28], but semantic con-nections can make it hard to assess the dimensionality.

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лОРЕМ ИПСУМ ДОЛОР СИТ АМЕТ ЦОНСЕЦТЕТУРАДИПИСИЦИНГ ЕЛИТ СЕД ДО ЕИУСМОД ТЕМПОРИНЦИДИДУНТ УТ ЛАБОРЕ ЕТ ДОЛОРЕ МАГНА АЛИЯУА уТ ЕНИМ АД МИНИМ ЖЕНИАМ ЯУИС НОСТРУД ЕЬЕРЦИТАТИОН УЛЛАМЦО ЛАБОРИС НИСИ УТАЛИЯУИП ЕЬ ЕА ЦОММОДО ЦОНСЕЯУАТ

дУИС АУТЕ ИРУРЕ ДОЛОР ИН РЕПРЕХЕНДЕРИТИН ЖОЛУПТАТЕ ЖЕЛИТ ЕССЕ ЦИЛЛУМ ДОЛОРЕ ЕУФУГИАТ НУЛЛА ПАРИАТУР

еЬЦЕПТЕУР СИНТ ОЦЦАЕЦАТ ЦУПИДАТАТ НОНПРОИДЕНТ СУНТ ИН ЦУЛПА ЯУИ ОФФИЦИА ДЕСЕРУНТ МОЛЛИТ АНИМ ИД ЕСТ ЛАБОРУМ лОРЕМИПСУМ ДОЛОР СИТ АМЕТ ЦОНСЕЦТЕТУР АДИПИСИЦИНГ ЕЛИТ СЕД ДО ЕИУСМОД ТЕМПОР ИНЦИДИДУНТ УТ ЛАБОРЕ ЕТ ДОЛОРЕ МАГНА АЛИЯУА уТЕНИМ АД МИНИМЖЕНИАМ ЯУИС НОСТРУД ЕЬЕРЦИТАТИОН УЛЛАМЦО ЛАБОРИС НИСИ УТ АЛИЯУИП ЕЬЕА ЦОММОДО ЦОНСЕЯУАТ

дУИС АУТЕ ИРУРЕ ДОЛОР ИН РЕПРЕХЕНДЕРИТИН ЖОЛУПТАТЕ ЖЕЛИТ ЕССЕ ЦИЛЛУМ ДОЛОРЕ ЕУФУГИАТ НУЛЛА ПАРИАТУР

еЬЦЕПТЕУР СИНТ ОЦЦАЕЦАТ ЦУПИДАТАТ НОНПРОИДЕНТ СУНТ ИН ЦУЛПА ЯУИ ОФФИЦИА ДЕСЕРУНТ МОЛЛИТ АНИМ ИД ЕСТ ЛАБОРУМ

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ù ü ó é ù ô ç è ç ð óä ë ì ù í ë õè ä ë ó í ô ô ç

ò ç å ó í ò ëí ä é ó ù çí ë ô ç èù ä å äë ì ë äí ñ






лОРЕМ ИПСУМ ДОЛОР СИТ АМЕТ ЦОНСЕЦТЕТУР

АДИПИСИЦИНГ ЕЛИТ СЕД ДО ЕИУСМОД ТЕМПОР

ИНЦИДИДУНТ УТ ЛАБОРЕ ЕТ ДОЛОРЕ МАГНА АЛИ

ЯУАуТ ЕНИМ АД МИНИМ ЖЕНИАМ ЯУИС НОС

ТРУД ЕЬЕРЦИТАТИОН УЛЛАМЦО ЛАБОРИС НИСИ УТ

АЛИЯУИП ЕЬ ЕА ЦОММОДО ЦОНСЕЯУАТ

дУИС АУТЕ ИРУРЕ ДОЛОР ИН РЕПРЕХЕНДЕРИТ

ИН ЖОЛУПТАТЕ ЖЕЛИТ ЕССЕ ЦИЛЛУМ ДОЛОРЕ ЕУ

ФУГИАТ НУЛЛА ПАРИАТУРеЬЦЕПТЕУР СИНТ ОЦЦАЕЦАТ ЦУПИДАТАТ НОН

ПРОИДЕНТ СУНТ ИН ЦУЛПА ЯУИ ОФФИЦИА ДЕ

СЕРУНТ МОЛЛИТ АНИМ ИД ЕСТ ЛАБОРУМ лОРЕМ

ИПСУМ ДОЛОР СИТ АМЕТ ЦОНСЕЦТЕТУР АДИП

ИСИЦИНГ ЕЛИТ СЕД ДО ЕИУСМОД ТЕМПОР ИНЦИ

ДИДУНТ УТ ЛАБОРЕ ЕТ ДОЛОРЕ МАГНА АЛИЯУА уТ

ЕНИМ АД МИНИМЖЕНИАМ ЯУИС НОСТРУД ЕЬЕРЦИ

ТАТИОН УЛЛАМЦО ЛАБОРИС НИСИ УТ АЛИЯУИП ЕЬ

ЕА ЦОММОДО ЦОНСЕЯУАТдУИС АУТЕ ИРУРЕ ДОЛОР ИН РЕПРЕХЕНДЕРИТ




СЕРУНТ МОЛЛИТ АНИМ ИД ЕСТ ЛАБОРУМ

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B�C D E E FG F H I J K I E I E L J M L M N O P F H DD M E F N F H D Q O R J O O N I S L O T U M D J I Q O EQ I J I Q�V D J I O Q B S L I C J F W H L E D X D H K I YM O M I F J L N N O Q K F N O P F H I C J I C I L M O N I S L I GD X D O K F Q Q F E F K F J C D S L O M TZ L I C O L M D I H L H D E F N F H I J H D G H D Y[ D J E D H I M I J V F N L G M O M D V D N I M D \ D K I N N L QE F N F H D D L ] L R I O M J L N N O G O H I O M L H T^ X K D G M D L H C I J M F K K O D K O M K L G I E O M O MJ F J G H F I E D J M B C L J M I J K L N G O S L I F _ I YK I O E D C D H L J M Q F N N I M O J I Q I E D W N O P F YH L Q T` F H D Q I G C

D X[ D JE F N F^J F J GK I O E D CH L Q TK F J C D K M D MD I L C Q F ED M E F N F H DQ I J I Q�V DM O M I F J L N N O

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Figure 1. The motivating example for unique backgrounds: the BuoyOING focus+context interface forbrowsing bidirectionally hyperlinked documents. The interface shows the relevant fragments of the other endsof the links and animates them fluidly to the focus upon traversing the link. a) shows a small documentnetwork. b) and c) show what a user sees while browsing the network, b) without and c) with backgroundtexture. There are three keyframes where the animation stops. Two frames of each animation between thekeyframes are shown. The unique backgrounds help the user notice that the upper right buoy in the lastkeyframe is actually a part of the same document (1) which was in the focus in the first keyframe. Our (asyet untested) hypothesis is that this will aid user orientation.

3. The motivation for Unique Back-grounds: the BuoyOING user inter-face

Focus+Context, or, fisheye views[10] are aparadigm for viewing large, structured information setsby showing the current area of interest (focus) magni-fied and the structurally connected but further-awayelements peripherally, with less magnification. Muchof the work on focus+context views has concentratedon tree structures or flat 2D images or maps.

The motivating example for unique backgrounds isthe BuoyOING (Buoy-Oriented Interface, Next Gen-eration) user interface, a focus+context interface fornavigating hypertext. BuoyOING is a logical step fromthe earlier work on Fluid Links[39], hypercept anima-tions[23], and transpointing windows[24]. BuoyOINGhas been designed from the ground up around the fol-lowing three principles:1. the user should always see all link targets (”you

should see where you can go”)2. the link transition should be fluidly animated (”you

should see where you do go”)3. the link transition and resulting view should make

it obvious to the user how to go back, without an ex-plicit back button (”once you get there, you shouldsee how you can get back”). This implies bidirectionallinks.

The links are between specific areas of the 2D doc-uments, and the relevant fragments of the target doc-ument is shown floating in the margin when the linkanchor is close to the focus. Figure 1 shows a sampledocument structure and a traversal, with and withoutunique backgrounds.

4. Generating Unique Background Tex-tures

In this section, we discuss the general principles wehave used to derive our algorithm to generate uniquebackground textures. The goals are that the uniquebackgrounds should be easily distinguishable and rec-ognizable, and that they should not significantly impairthe reading of black text drawn on top.

Our approach is an extension of the type of in-version approach that has been used by Ware andKnight[37] for inverting the earliest stage of the visualsystem in order to place a particular vector or scalarfield of data in the texture “channel”.

The ability to distinguish a particular texture froma large set depends on the distribution of textures inthe set. For instance, it is intuitively clear that textureswith independently random texel values would be avery bad choice: all such textures would look alike,being just noise. In order to design a distinguishable

Feature extraction

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Figure 2. The qualitative model of visual perceptionused to create the algorithm. The visual input istransformed into a feature vector, which containsnumbers (activation levels) corresponding to, e.g.,colors, edges, curves and small patterns. The featurevector is matched against the memorized textures.In order to generate recognizable textures, randomseed values should produce a distribution of featurevectors with maximum entropy.

distribution of textures, we have to take into accountthe properties of the human visual system.

The simple model of texture perception we use as-sumes that at some point, the results from the differentpre-attentive feature detectors, such as different shapesand colors, are combined to form an abstract feature

vector (see Fig. 2). As seen for example in [25], onlya limited number of different features detected can begrouped into objects, indicating that the spatial resolu-tion of the feature vector is quite low — as a well-knownexample, conjunction coding is not preattentive — redsquares are hard to find among green squares and redand green circles.

The feature vector is then used to compute whichconcept the particular input corresponds to by compar-ing it to memorized models in a simple perceptron-likefashion[30, 38]. This configuration is commonly usedin neural computation.

This rough, qualitative model is able to explainwhy uniformly random texels do not make easilydistinguishable background textures: after the “pre-processing”, different instances of noise would all yieldalmost exactly the same feature vector in the brain.Noise has no shape because there is no correlation be-tween the pixels.

From the model we can see that to be distinguish-able, a feature vector for a given texture should alwaysbe the same. Fragments of a non-repeating texture willbe slightly different, resulting in slightly different vec-tors even if the local structure is the same. A repeatingtexture should thus be easier to recognize. Our anec-dotal observations confirm this.

Additionally, the entropy of the feature vectors

over the distribution of textures should be maximized.The distribution should contain occurrences of as manydifferent features as possible, and the features should bedistributed independently from each other. However,the results cited above[25] also indicate that in any sin-

gle texture, only a limited range of features should beused.

In a sense, the model of perception should be in-

verted in order to produce a unique background from arandom vector. Features that are orthogonal for humanperception (e.g., color and the direction of the fastestluminance change) should be independently random,and features not orthogonal (e.g., colors of neighbour-ing pixels) should be correlated so as to maximize theentropy.

An important point in generating the backgroundsis that the texture appearance should have no corre-

lation with any attribute or content of the documentso that the textures of any hyperlinked documents aresimilar only by chance.

5. Hardware-accelerated implementation

In this section, we discuss our hardware-accelerated implementation (libpaper) of unique back-grounds (papers).

A. Parameter hierarchy

We have found that setting the parameters hier-archically produces the best results: the parametersfor different passes should depend on hyperparametersrandomly selected for the entire paper. This is in accor-dance with the discussion about [25] above: the hyper-parameters limit the number of different features thatare rendered onto one texture.

Our current parameter hierarchy is shown in Fig. 3.The individual parameters are explained in the subsec-tions below.

B. Rationale for a Fragment-based imple-mentation

One major goal for the implementation is to sup-port complicated mappings between paper and screencoordinates, such as fisheye distortion. To make thissimple, all processing when rendering the backgroundtexture must be done on the fragment level after thetexture accesses, i.e., we cannot use procedural geome-try except if pre-rendering the background into a tex-ture.

However, pre-rendering each texture is likely to betoo time- and memory-consuming if there are dozensof different documents visible at the same time, so weshall limit ourselves to pure fragment-based renderingin this article. However, if a single background covers

Global

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Repeat unit

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CIELAB

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Basis textures

Basis textureprobabilities

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Combinerprograms

Combinerparameters

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Figure 3. The parameter hierarchy for unique tex-tures in our implementation. At the highest level,global hyperparameters define the distribution of thetextures. For reasons explained in text, the param-eters used by different rendering passes of the samebackground texture need to be correlated; this isachieved by having them depend on hyperparametersselected for each paper from the global distribution.

large areas of the screen, pre-rendering may increaseperformance considerably, and therefore our implemen-tation does support two rendering modes with differ-ent tradeoffs. In the direct mode, small, static basistextures are used, which requires 2-3 passes with alltexture units enabled. In the pre-rendered mode, ren-dering requires 1 pass with only one texture unit, butthe repeating unit of the texture has to be pre-renderedinto a texture of its own. To achieve a satisfactoryimage quality in zooming with pre-rendering, a rela-tively large texture has to be used for each background;512x512 is not really sufficient.

Plain OpenGL 1.3 does not by itself provideenough flexibility in the fragment pipeline to allow forgenerating features nonlinearly from the basis textures.Because of this, and the availability of stable Linuxdrivers, our main platforms are NV10, i.e., OpenGL1.3 + GL NV register combiners, and NV25, i.e., NV10+ GL NV texture shader3. We are working on an im-plementation based on GL ARB fragment program oncesuitable hardware and Linux drivers emerge.

C. Colors

Color is the most dominant visual attribute of atexture. Therefore, it is essential that the overall col-ors of the backgrounds are maximally diverse with re-spect to color perception. However, we come againto the number-of-different-features arguments of Sec-tion 4: too many different colors in a single backgroundare perceived just as a mix of many colors, making all

Figure 4. The complete set of 2D basis texturesused by our implementation. All textures shown inthis article are built from these textures and the cor-responding HILO textures for offsetting.

such backgrounds look the same.

To maintain recognizability, we use a small paletteof colors for each paper, selected randomly from aheuristic distribution. The final image contains con-vex combinations of the palette colors.

Even though the CIE L∗a∗b∗ [7] color space is fairlyperceptually uniform, it is not good for selecting ran-dom colors (cf. [15]): using a uniformly chosen hueangle produces too much blue and too little yellow forhigh L∗ (luminance). Because of this, we use a dif-ferent color circle with seven evenly spaced “primary”hues (RGB, CMY and orange).

Hues are chosen from a distribution which usuallyclusters them fairly close together around a uniformlychosen mean but also allows far-away hues in the samepalette occasionally. Because the shape of the RGBcolor gamut irregularly limits the saturations of lightcolors and linear interpolation can further reduce thesaturation, the saturations of the palette colors are cho-sen from a distribution emphasizing saturated colors:unsaturated colors can easily cause a too multicoloredpalette because the adaptive effects of the eye shiftthem towards the complementary colors of the moresaturated colors in the palette.

For readability, we only use colors with the CIE L∗

value over 80. Note that the display gamma interactswith the lightness computation: we have adjusted thecomputations for a display gamma of 2.2, which is typ-ically the default in PC systems. For each background,we use colors from both the dark and light end of the80–100 range in order to create some contrast. Thismakes the shapes in the texture apparent, and avoidsthe unpleasant, blurry appearance of images with onlychroma changes in colors.

D. Basis textures

The shapes of the final background texture are gen-erated entirely from a small set of static basis textures.

Even though the basis textures are RGB textures, theycontain no color information: they are simply treatedas 3- or 4-vectors to be used in various ways to createshapes, and color is added by the register combinersusing the palette selected as described above.

We have obtained good results with the basis tex-tures in Fig. 4: a mix of small textures with uniformlyrandom texels, larger textures with noise or turbu-lence[27], and simple geometric images (e.g., checker-board).

As for the selection of basis textures for each pa-per, the principle of limiting the number of differentfeatures in each texture applies here as well: we usehyperparameters for each background texture to con-trol the probabilities of selecting different basis texturesso that each paper will mostly have only basis texturestwo or three types.

E. Texture coordinates

The choice of the geometry of the repeating unit(a parallelogram) fixes an absolute scale for the paper.The repeating unit should be fairly isotropic to avoidthe degeneration of textures to diagonal lines, and theunits for different textures should be relatively similarin size. The repeating unit is chosen from a heuristicdistribution satisfying these criteria.

After a repeating unit is fixed, there is still freedomin choosing texture coordinates for each basis texture:any mapping of the texture is fine, as long as it re-peats with the selected repeating unit. For example, atexture can repeat multiple times inside the repeatingunit, or can be skewed with respect to the repeatingunit. Again, a heuristic distribution is used which doesnot skew or scale the basis texture too much too often.

F. Texture shading

On the NV25 architecture, the texture accesses canbe customized further by the use of texture shading:the texture coordinates used by a texture unit can bemade to depend on the result of a previous texture unit.This can be used to create a large variety of shapes[27].So far, we have only used offset textures with randomoffset matrices, but even they do improve the qualityof the output.

G. Register combiners

The NVIDIA register combiners extension is usedto combine the the 3- and 4-vectors obtained from thebasis textures and the palette colors into the final frag-ment color. Our need for the combiners is rather uncon-ventional: we want to lose most of the original shapesof the basis textures in order to create new, differentshapes from the interaction of the basis texture values

Figure 5. How the limited register combiners of theNV10 architecture can be used to generate shapes.Top: the two basis textures. Bottom left: dot prod-uct of the basis textures: 2(2a − 1) · (2b − 1) + 1/2,where a and b are the texture RGB values. Bot-tom right: dot product of the basis textures squared:

32 ((2a − 1) · (2b − 1))2. This term can then be used

to modulate between two colors.

and combiner parameters chosen randomly from theseed number. For this, we use dot products of texturevalues with each other and with random constant vec-tors, and scale up with the register combiner outputmappings to sharpen the result (see Fig. 5). The re-sulting values are used for interpolating between thepalette colors. Because some basis textures have blur-rier edges than others, the output scalings need to beadjusted depending on the basis textures selected.

6. Experiences, evaluation

A. Overall appearance of the resulting tex-tures

As seen in Fig. 7, the overall appearance of thetextures is somewhat natural and the textures clearlydo have unique distinguishing features.

They can be zoomed quite well, even though wehave not tried to attain infinite zoomability[11] withthe current implementation, but only the more modestgoal of zooming within a range that would be reason-able for a single PDF document, i.e., approximately 20-fold difference between minimum and maximum zoom.

Although texture perception is scale-independentto some extent, the display resolution and certain spa-tial interactions in color perception limit the scale atwhich different features can be pre-attentively per-ceived. Therefore, we have chosen the texture coor-dinate mappings for different texture units so that theresulting textures have separate distinguishing featureson both small and large scales.

It could be possible[11] to make the unique back-

ground look similar at different scales, but this wouldremove the use of the texture as a cue of scale.

Our nonlinear use of the register combiners doeshave some ill effects when zooming the texture out to avery small scale: mipmapping will not give the correctaverage color value. It may be possible to alleviate thisby modeling the texture mathematically and calculat-ing the correct average and placing corrective terms tothe equations. However, in the intended zooming rangethe current system is quite satisfactory.

B. Text readability

The most common criticism against this work con-cerns text readability. Indeed, one of the most difficultaspects of this work was tuning the random color se-lection to produce acceptable results. However, in thecurrent version, even relatively small text is quite legi-ble on the backgrounds.

Experiments in [33] indicate that a backgroundtexture only affects readability when the text contrastis low. Also, as discussed there, subjective assessmentsof readability often have low correlation with objectivemeasurements.

Additionally, text readability on the generatedbackgrounds depends strongly on the text scale; mak-ing it easy for the user to zoom fluidly in and out helps.

It is also possible to enhance the text readabilityexplicitly, by lightening (“bleaching”) the backgroundslightly around the area where the text is drawn. Thebleaching can be made almost unnoticeable on a com-puter screen and works well because we start from afairly light background.

C. An Initial Recognizability Experiment

Experiments on black-and-white ink blots andsnow crystals [13] have shown that complex, unfamiliarpictures can be remembered and recognized and thatrecognition performance decreases very little over time.

In order to evaluate the recognizability of our pro-cedurally generated textures, we need to have an ap-propriate comparison point. Pictures of natural objectswould not be appropriate, because they cannot be gen-erated in infinite amounts from seed numbers and theywould easily yield undesirable semantic associations.Lacking a better example, we shall use plain solid colorbackgrounds as a baseline even though the colors ofeven a small set of randomly chosen colors would mostlikely not be discriminable.

Another question is how many textures would theuser have to remember for it to be useful. Studies ofweb cache statistics (see, e.g., [4]) have shown that filepopularity approximately follows Zipf’s law so that asmall number of documents accounts for most of the

Figure 6. Two different screenshots of a structure of PDF documents viewed in a focus+context view. Theuser interface shows relationships between specific points in the documents. Each document has an uniquebackground, which makes it easy to see that the fragment of a document on the right side of the bottomview is the document fully seen in the top view; without unique backgrounds, this would be relatively difficultand would require traversing the link.

Figure 7. A number of unique backgrounds generated by our system. This view can be rendered, withoutpre-rendering the textures, in 20ms on a GeForce4 Ti 4200 in a 1024x768 window (fill-rate/bandwidth limited).

use, see Fig. 9.

Thus, we chose to measure the recognition of only15 target textures or colors in the experiment. Ourhypothesis is that the texture backgrounds are morerecognizable than the solid colors.

C.1 Method

Participants. Five participants naıve to the hy-pothesis and our texturing work performed the exper-iment in both conditions.

Stimuli. 15 target backgrounds and 15 distractorbackgrounds were randomly chosen for both the tex-ture and solid color conditions. The distribution ofthe solid colors was the same that is used for the tex-ture colors except that the highest lightness tail wasde-emphasized to increase the otherwise low discrim-inability of very light, unsaturated colors.

Procedure. Each participant performed the test in-dividually in both conditions in a random order. First,the 15 target backgrounds were shown sequentially, ina random order, 5 seconds each. Then, recognition wastested by showing the 15 target backgrounds and the 15distractor backgrounds in a random order and havingthe participant answer “seen” or “not seen” for eachone. The time for answers was not limited.

Table 1. Results of a recognition experiment with15 previously seen textures and light colors to bepicked out from 15 not previously seen instances.The numbers give the percentage of trials and theaverage reaction time in seconds for the five subjects.This shows that the textures are quite recognizableeven after just one previous viewing.

textures colors

correct incorrect correct incorrectseen 72 (2.6s) 28 (5.5s) 71 (2.8s) 29 (4.1s)not seen 84 (3.0s) 16 (5.6s) 43 (3.4s) 57 (4.0s)total 78 (2.8s) 22 (5.5s) 57 (3.1s) 43 (4.1s)

C.2 Results

The results are summarized in Table 1. An anal-ysis of variance indicates that the average recognitionperformance is significantly better for the textures thanfor the solid colors [F(1,4) = 19.0, p = .012]. The largenumber of false positives shows that solid colors do nothave enough variation for unambiguous recognition.

Of course, solid colors could facilitate recognitionin the presence of other cues, such as the text of thedocument. However, the recognition of similar-lookingfragments of documents would still depend on con-scious effort on the user’s part — exactly what theunique background textures were designed to avoid.

a)

b)

c)

d)

Figure 8. Enhancing text readability on coloredbackgrounds: a) Original, b) Zooming, c) Bleach-ing, d) The bleached version without drawing theblack text, showing the blurred lightening.

7. Conclusions

We have presented a perceptually designedhardware-accelerated algorithm for generating recog-nizably unique backgrounds. The motivating example,the BuoyOING user interface demonstrates that themethod is at its most useful when the same documentcan be reached through several ways and fragments ofdocuments are seen.

Of course, we cannot hope to match in qualitya unique graphical appearance designed by a humandesigner; magazines and web sites have long usedskillfully designed graphical elements to make them-selves recognizable. However, our algorithm is able togenerate an unlimited amount of unique backgroundscheaply, making it possible, e.g., for all academic arti-cles with similar typography to have their own back-ground.

So far, we have concentrated mostly on low-endhardware, and have not even tapped the full potential

Figure 9. Zipf’s law concretized: why distinguish-ing 15 textures from a large number of others helps.In real life, some documents get accessed far moreoften than most. The diagram shows 2000 docu-ments weighted with Zipf’s law with exponent 1.1.Each square represents a document, and the area ofeach square is scaled to its rate of accesses. Underthese conditions, the 15 most important documentsaccount for approximately half of the accesses. Ofcourse, it is impossible to know which documents willbe important and that will also change with time soall documents should be textured stably from thefirst viewing onwards.

of the NV25 architecture.We have conducted an initial recognition experi-

ment showing that the textures generated by our al-gorithm are indeed recognizable. Carrying out moreusability tests is necessary, both to measure how welltextures can be remembered and to make the ad hocdistributions more experimentally based.

It can of course be argued that the backgroundsclutter the display visually, making the user interfacemore confusing, and may not even be helpful. Indeed,many aspects of the system are a compromise betweenrecognizability and other goals such as readability, ren-dering performance, or user comfortability.

Overall, however, we feel that the backgrounds cangreatly improve user orientation, enabling more effi-cient views to hyperstructured content, and thereforethat the benefits can be made to outweigh the costs.

8. Acknowledgments

The authors would like to thank MarkettaNiemela for help with ANOVA, and John Canny, BenjaFallenstein, Marketta Niemela and Pertti Saariluomafor discussions. Benja Fallenstein, Matti Katila, andAsko Soukka have contributed to the development ofthe BuoyOING interface in aspects not related to theunique background textures.

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