1. IAT 814 Introduction to Visual Analytics
Perception
Sep 11, 2013
IAT 814

2. Perceptual Processing
Seek to better understand visual perception and visual information processing
Multiple theories or models exist
Need to understand physiology and cognitive psychology
Sep 11, 2013
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3. A Simple Model Two stage process
Parallel extraction of low-level properties of scene
Sequential goal-directed processing
Stage 1
Early, parallel detection of color, texture, shape, spatial attributes
Stage 2
Serial processing of object identification (using memory) and spatial layout, action
Eye
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4. Stage 1 - Low-level, Parallel
Neurons in eye & brain responsible for different kinds of information
Orientation, color, texture, movement, etc.
Arrays of neurons work in parallel
Occurs “automatically”
Rapid
Information is transitory, briefly held in iconic store
Bottom-up data-driven model of processing
Often called “pre-attentive” processing
Sep 11, 2013
IAT 814

5. Stage 2 - Sequential, Goal-Directed
Splits into subsystems for object recognition and for interacting with environment
Increasing evidence supports independence of systems for symbolic object manipulation and for locomotion & action
First subsystem then interfaces to verbal linguistic portion of brain, second interfaces to motor systems that control muscle movements
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6. Stage 2 Attributes Slow serial processing
Involves working and long-term memory
Top-down processing
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7. Preattentive Processing
How does human visual system analyze images?
Some things seem to be done preattentively, without the need for focused attention
Generally less than msecs (eye movements take 200 msecs)
Seems to be done in parallel by low-level vision system
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IAT 814

8. How Many 3’s?
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9. How Many 3’s?
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10. What Kinds of Tasks? Target detection Boundary detection Counting
Is something there?
Boundary detection
Can the elements be grouped?
Counting
How many elements of a certain type are present?
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11. Examples: Where is the red circle? Left or right?
Put your hand up as soon as you see it.
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12. Pre-attentive Hue
Can be done rapidly
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13. Examples: Where is the red circle? Left or right?
Put your hand up as soon as you see it.
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14. Shape
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15. Examples: Where is the red circle? Left or right?
Put your hand up as soon as you see it.
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16. Hue and Shape Cannot be done preattentively
Must perform a sequential search
Conjuction of features (shape and hue) causes it
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17. Examples Is there a boundary: Put your hand up as soon as you see it.
A connected chain of features that cross the rectangle
Put your hand up as soon as you see it.
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18. Fill and Shape
Left can be done preattentively since each group contains one unique feature
Right cannot (there is a boundary!) since the two features are mixed (fill and shape)
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19. Examples
Is there a boundary?
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20. Hue versus Shape
Left: Boundary detected preattentively based on hue regardless of shape
Right: Cannot do mixed color shapes preattentively
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21. Hue vs. Brightness Left: Varying brightness seems to interfere
Right: Boundary based on brightness can be done preattentively
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22. Preattentive Features
Certain visual forms lend themselves to preattentive processing
Variety of forms seem to work
In the next slide, spot the region of different shapes, both left and right
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23. 3-D Figures
3-D visual reality has an influence
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24. Emergent Features
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25. Potential PA Features length width size curvature number terminators
intersection
closure
hue
intensity
flicker
direction of motion
stereoscopic depth
3-D depth cues
lighting direction
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IAT 814

26. Key Perceptual Properties
Brightness
Color
Texture
Shape
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27. Luminance/Brightness
Measured amount of light coming from some place
Brightness
Perceived amount of light coming from source
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28. Brightness
Perceived brightness is non-linear function of amount of light emitted by source
Typically a power function
S = aIn
S - sensation
I - intensity
Very different on screen versus paper
Sep 11, 2013
IAT 814

29. Greyscale
Probably not best way to encode data because of contrast issues
Surface orientation and surroundings matter a great deal
Luminance channel of visual system is so fundamental to so much of perception
We can get by without color discrimination, but not luminance
Sep 11, 2013
IAT 814

30. Greyscale
White and Black are not fixed
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31. Greyscale
White and Black are not fixed!
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32. Color Systems HSV: Hue, Saturation, Value Hue: Color type
Saturation: “Purity” of color
Value: Brightness
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33. CIE Space
The perceivable set of colors
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34. CIE L*a*b* http://www.gamutvision.com/docs/printest.html Sep 11, 2013
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35. Color Categories Are there certain canonical colors?
Post & Greene ’86 had people name different colors on a monitor
Pictured are ones with > 75% commonality
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36. Luminance Foreground must be distinct from background!
Can you read this text?
Can you read this text?
Can you read this text?
Can you read this text?
Can you read this text?
Can you read this text?
Sep 11, 2013
IAT 814

37. Color for Categories
Can different colors be used for categorical variables?
Yes (with care)
Colin Ware’s suggestion: 12 colors
red, green, yellow, blue, black, white, pink, cyan, gray, orange, brown, purple
Sep 11, 2013
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38. Why 12 colors?
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39. Just-Noticeable Difference
Which is brighter?
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40. Just-Noticeable Difference
Which is brighter?
(130, 130, 130)
(140, 140, 140)
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41. Weber’s Law
In the 1830’s, Weber made measurements of the just-noticeable differences (JNDs) in the perception of weight and other sensations.
He found that for a range of stimuli, the ratio of the JND ΔS to the initial stimulus S was relatively constant:
ΔS / S = k
Sep 11, 2013
IAT 814

42. Weber’s Law Ratios more important than magnitude in stimulus detection
For example: we detect the presence of a change from 100 cm to 101 cm with the same probability as we detect the presence of a change from 1 to 1.01 cm, even though the discrepancy is 1 cm in the first case and only .01 cm in the second.
Sep 11, 2013
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43. Weber’s Law
Most continuous variations in magnitude are perceived as discrete steps
Examples: contour maps, font sizes
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44. Weber’s Law
Most continuous variations in magnitude are perceived as discrete steps
Examples: contour maps, font sizes
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45. Stevens’ Power Law
Compare area of circles:
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46. [graph from Wilkinson 99]
Stevens’ Power Law
s(x) = axb
s is the sensation
x is the intensity of the attribute
a is a multiplicative constant
b is the power
b > 1: overestimate
b < 1: underestimate
[graph from Wilkinson 99]
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47. Stevens’ Power Law
[Stevens 1961]
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48. Stevens’ Power Law
Experimental results for b: Length .9 to 1.1 Area .6 to .9 Volume .5 to .8 Heuristic: b ~ 1/sqrt(dimensionality)
Sep 11, 2013
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49. [Cartography: Thematic Map Design, p. 170, Dent, 96]
Stevens’ Power Law
Apparent magnitude scaling
[Cartography: Thematic Map Design, p. 170, Dent, 96]
S = 0.98A0.87
[J. J. Flannery, The relative effectiveness of some graduated point symbols in the presentation of quantitative data, Canadian Geographer, 8(2), pp , 1971] [slide from Pat Hanrahan]
Sep 11, 2013
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50. Relative Magnitude Estimation
Most accurate Least accurate
Position (common) scale Position (non-aligned) scale Length Slope Angle Area Volume Color (hue/saturation/value)
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51. Color Sequence
Can you order these?
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52. Possible Color Sequences
Grey Scale
Full Spectral Scale
Partial Spectral Scale
Single Hue Scale
Double-ended Hue Scale
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53. HeatMap
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54. ColorBrewer
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55. Color Purposes Call attention to specific data
Increase appeal, memorability
Increase number of dimensions for encoding data
Example, Ware and Beatty ‘88
x,y - variables 1 & 2
amount of r,g,b - variables 3, 4, & 5
Sep 11, 2013
IAT 814

56. Using Color Modesty! Less is more
Use blue in large regions, not thin lines
Use red and green in the center of the field of view (edges of retina not sensitive to these)
Use black, white, yellow in periphery
Use adjacent colors that vary in hue & value
Sep 11, 2013
IAT 814

57. Using Color
For large regions, don’t use highly saturated colors (pastels a good choice)
Do not use adjacent colors that vary in amount of blue
Don’t use high saturation, spectrally extreme colors together (causes after images)
Use color for grouping and search
Sep 11, 2013
IAT 814

58. Texture Appears to be combination of Complex attribute to analyze
orientation
scale
contrast
Complex attribute to analyze
Sep 11, 2013
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59. Shape, Symbol
Can you develop a set of unique symbols that can be placed on a display and be rapidly perceived and differentiated?
Application for maps, military, etc.
Want to look at different preattentive aspects
Sep 11, 2013
IAT 814

60. Glyph Construction
Suppose that we use two different visual properties to encode two different variables in a discrete data set
color, size, shape, lightness
Will the two different properties interact so that they are more/less difficult to untangle?
Integral - two properties are viewed holistically
Separable - Judge each dimension independently
Sep 11, 2013
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61. Integral-Separable Not one or other, but along an axis Sep 11, 2013
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62. Integral vs. Separable Dimensions
[Ware 2000]
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63. Shapes Superquadric surface can be used to display 2 dimensions
Color could be added
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64. Change Blindness
Is the viewer able to perceive changes between two scenes?
If so, may be distracting
Can do things to minimize noticing changes
Sep 11, 2013
IAT 814