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IAT 814 Introduction to Visual Analytics
Perception Sep 11, 2013 IAT 814
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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 IAT 814
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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 Sep 11, 2013 IAT 814
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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
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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 Sep 11, 2013 IAT 814
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Stage 2 Attributes Slow serial processing
Involves working and long-term memory Top-down processing Sep 11, 2013 IAT 814
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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 Sep 11, 2013 IAT 814
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How Many 3’s? Sep 11, 2013 IAT 814
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How Many 3’s? Sep 11, 2013 IAT 814
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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? Sep 11, 2013 IAT 814
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Examples: Where is the red circle? Left or right?
Put your hand up as soon as you see it. Sep 11, 2013 IAT 814
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Pre-attentive Hue Can be done rapidly Sep 11, 2013 IAT 814
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Examples: Where is the red circle? Left or right?
Put your hand up as soon as you see it. Sep 11, 2013 IAT 814
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Shape Sep 11, 2013 IAT 814
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Examples: Where is the red circle? Left or right?
Put your hand up as soon as you see it. Sep 11, 2013 IAT 814
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Hue and Shape Cannot be done preattentively
Must perform a sequential search Conjuction of features (shape and hue) causes it Sep 11, 2013 IAT 814
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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. Sep 11, 2013 IAT 814
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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) Sep 11, 2013 IAT 814
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Examples Is there a boundary? Sep 11, 2013 IAT 814
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Hue versus Shape Left: Boundary detected preattentively based on hue regardless of shape Right: Cannot do mixed color shapes preattentively Sep 11, 2013 IAT 814
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Hue vs. Brightness Left: Varying brightness seems to interfere
Right: Boundary based on brightness can be done preattentively Sep 11, 2013 IAT 814
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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 Sep 11, 2013 IAT 814
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3-D Figures 3-D visual reality has an influence Sep 11, 2013 IAT 814
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Emergent Features Sep 11, 2013 IAT 814
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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 Sep 11, 2013 IAT 814
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Key Perceptual Properties
Brightness Color Texture Shape Sep 11, 2013 IAT 814
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Luminance/Brightness
Measured amount of light coming from some place Brightness Perceived amount of light coming from source Sep 11, 2013 IAT 814
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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
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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
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Greyscale White and Black are not fixed Sep 11, 2013 IAT 814
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Greyscale White and Black are not fixed! Sep 11, 2013 IAT 814
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Color Systems HSV: Hue, Saturation, Value Hue: Color type
Saturation: “Purity” of color Value: Brightness Sep 11, 2013 IAT 814
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CIE Space The perceivable set of colors Sep 11, 2013 IAT 814
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CIE L*a*b* http://www.gamutvision.com/docs/printest.html Sep 11, 2013
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Color Categories Are there certain canonical colors?
Post & Greene ’86 had people name different colors on a monitor Pictured are ones with > 75% commonality Sep 11, 2013 IAT 814
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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
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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 IAT 814
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Why 12 colors? Sep 11, 2013 IAT 814
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Just-Noticeable Difference
Which is brighter? Sep 11, 2013 IAT 814
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Just-Noticeable Difference
Which is brighter? (130, 130, 130) (140, 140, 140) Sep 11, 2013 IAT 814
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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
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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 IAT 814
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Weber’s Law Most continuous variations in magnitude are perceived as discrete steps Examples: contour maps, font sizes Sep 11, 2013 IAT 814
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Weber’s Law Most continuous variations in magnitude are perceived as discrete steps Examples: contour maps, font sizes Sep 11, 2013 IAT 814
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Stevens’ Power Law Compare area of circles: Sep 11, 2013 IAT 814
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[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] Sep 11, 2013 IAT 814
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Stevens’ Power Law [Stevens 1961] Sep 11, 2013 IAT 814
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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 IAT 814
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[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 IAT 814
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Relative Magnitude Estimation
Most accurate Least accurate Position (common) scale Position (non-aligned) scale Length Slope Angle Area Volume Color (hue/saturation/value) Sep 11, 2013 IAT 814
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Color Sequence Can you order these? Sep 11, 2013 IAT 814
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Possible Color Sequences
Grey Scale Full Spectral Scale Partial Spectral Scale Single Hue Scale Double-ended Hue Scale Sep 11, 2013 IAT 814
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HeatMap Sep 11, 2013 IAT 814
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ColorBrewer Sep 11, 2013 IAT 814
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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
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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
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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
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Texture Appears to be combination of Complex attribute to analyze
orientation scale contrast Complex attribute to analyze Sep 11, 2013 IAT 814
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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
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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 IAT 814
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Integral-Separable Not one or other, but along an axis Sep 11, 2013
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Integral vs. Separable Dimensions
[Ware 2000] Sep 11, 2013 IAT 814
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Shapes Superquadric surface can be used to display 2 dimensions
Color could be added Sep 11, 2013 IAT 814
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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
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