© anselm spoerri lecture 4 human visual system –recap –3d vs 2d debate –object recognition...

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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