© anselm spoerri lecture 4 human visual system –recap –3d vs 2d debate –object recognition...
TRANSCRIPT
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Lecture 4
Human Visual System– Recap
– 3D vs 2D Debate– Object Recognition Theories
Tufte – Envisioning Information
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Human Visual System – Recap
Sensory Representations Effectivebecause well matched to early stages of neural processing
Physical World Structured
Stages of Visual Processing1 Rapid Parallel Processing2 Slow Serial Goal-Directed Processing
Visual System Detects CHANGES + PATTERNS
Luminance Channel More Important than Color
Pre-Attentive Features
Position
Color Simple Shape = orientation, size
Motion Depth
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Gestalt Laws – Recap
Proximity
Similarity
Continuity
Symmetry
Closure
Relative Size
Figure and Ground
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Space Perception – Recap
Depth Cues
Shape-from-Shading
Shape-from-Contour
Shape-from-Texture
Shape-from-Motion
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Simple Lighting Model – Recap
DiffuseLambertian
Specular AmbientShadows
Light from above and at infinity
Diffuse, Specular and Ambient Reflection Depth Cues
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Depth Cues – Relative Importance – Recap
Depth
Con
trast
Depth (meters)
0.001
0.01
0.1
1.0
1 10 100
Occlusion
Relative size
Convergenceaccommodation
Binoculardisparity
Motionparallax
Aerial
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3D vs 2D Debate - Display Abstract Data in 3D?
Depth Cue Theory– Depth cues are environmental information about space
Occlusion most important Depth Cue
Perspective may not add anything by itself
Stereo important for Close Interaction
Motion important for 3D layout
Surface Perception – Shape-from-Shading – Shape-from-Texture
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Relative Position Judgment
Fine Judgments - threading a needle – Stereo is important – Shadows– Occlusion
Large Scale Judgments– Perspective– Motion parallax– Stereo is not important
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Image + Object Recognition
Properties of Image Recognition – Remarkable image recognition memory– Up to 5 images per second– Applications in image searching interfaces– Easier to Recognize than to Recall
Image Based Theories– Template theories based on 2D image processing
Structural 3D Theories– Extract structure of a scene in terms of 3D primitives
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Template Theories
Template with simple morphing operations
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Template Theories – Scale Matters
Visual degrees = 4optimal for object perception
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Geon Theory
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Geon Theory (cont.)
3D Primitives “Geons”Structural skeleton
Shape from shading is also primitive
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Canonical Silhouettes
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Recognition – Processing Stages
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Pattern Finding & Recognition – 3D vs 2D
34% memory errors
21% errors
20% memory errors
11.4% errors
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Edward Tufte
Books
The Visual Display of Quantitative Information
Envisioning Information
Visual Explanations
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Tufte - Minard's Napoleon's March to Moscow
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Tufte - Escape Flatland: Napoleon's March
Enforce Visual ComparisonsWidth of tan and black lines gives you an immediate comparison of the size of Napoleon's army at different times during march.
Show CausalityMap shows temperature records and some geographic locations that shows that weather and terrain defeated Napoleon as much as his opponents.
Show Multivariate dataNapoleon's March shows six: army size, location (in 2 dimensions), direction, time, and temperature.
Use Direct LabelingIntegrate words, numbers & imagesDon't make user work to learn your "system.”
Legends or keys usually force the reader to learn a system instead of studying the information they need.
Design Content-Driven
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Tufte – Challenger Data: Launch?
Graph obscures important variables of interest: temperature is shown textually and graphically; degree of damage is not mapped onto a nominal scale
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Tufte – Challenger Data: Launch?
Diagrams can lead to great insight, but also to lack of it
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Cause of cholera epidemic in London in 1854?
Modified in Visual Explanations by Edward Tufte, Graphics Press, 1997
John Snow’s deduction that a cholera epidemic was caused by a bad water pump
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Tufte’s Measures
Maximize data density
Data density of graphic =Number entries in data matrix
Area of data graphic
Measuring Misrepresentation close to 1
Size of effect shown in graphic
Size of effect in dataLie factor =
Data ink ratio =Data ink
Total ink used in graphic
Maximize data-ink ratio
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Tufte - Graphical Displays Should
Show Data
Focus on Content instead of graphic production
Avoid Distorting what Data has to say
Make Large Data Sets Coherent
Encourage Eye to Compare Different Pieces of Data
Reveal Data at several Levels of Detail
Closely integrate Statistical and Verbal Descriptions
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Example
20022001200019991998
500
475
450
Stock market crash?
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Example
20022001200019991998
500
250
0
Show entire scale
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Example
20001990198019701960
500
250
0
Show in context
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Tufte - How to Exaggerate with Graphs
“Lie factor” = 2.8
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Tufte - How to Exaggerate with Graphs
“Lie factor” = 2.8
Error:Shrinking along both dimensions
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When to use which type?
Line Graph – x-axis requires quantitative variable– Variables have contiguous values– familiar/conventional ordering among ordinals
Bar Graph– comparison of relative point values
Scatter Plot– convey overall impression of relationship
between two variables
Pie Chart– Emphasizing differences in proportion
among a few numbers
R2 = 0.87
0%
20%
40%
60%
80%
100%
0.0 0.2 0.4
0
510
15
20
1 2 3 4 5 6 7 8
0
5
10
15
1 2 3 4 5 6 7 8
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Tufte - Graph & Chart Tips
Avoid Separate Legends and Keys
Make Grids, labeling, etc., Very Faint so that they recede into background
Graphical Integrity– Where’s baseline?– What’s scale?– What’s context?– Watch Size Coding: Height/width vs. area vs. volume
Using Color Effectively– To label– To measure– To represent or imitate reality– To enliven or decorate
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Tufte – Hierarchy of Visual Effects
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Tufte – Hierarchy of Visual Effects
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Tufte – Hierarchy of Visual Effects in Maps
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Tufte – Be aware of visual artifacts
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Tufte – Leverage Illusionary Contours
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Tufte – Narratives of Space & Time
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Tufte – Micro / Macro Readings - 2½ Displays
Axonometric Projection To Clarify, Add Detail
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Tufte – Micro / Macro Readings - 2½ Displays
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Tufte’s Principles – Summary
Good Information Design = Clear Thinking Made Visible
Greatest number of Ideas in Shortest Time with Least Ink in the Smallest Space
Principles– Enforce Visual Comparisons
Show Comparisons Adjacent in Space
– Show Causality
– Show Multivariate Data
– Use Direct Labeling
– Use Small Multiples
– Avoid “Chart Junk”: Not needed extras to be cute