iat 355 introduction to visual analytics perception may 21, 2014iat 3551
TRANSCRIPT
IAT 355 1
IAT 355 Introduction to Visual Analytics
Perception
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Perceptual Processing
g Seek to better understand visual perception and visual information processing– Multiple theories or models exist– Need to understand physiology and
cognitive psychology
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A Simple Model
g Two stage process– Parallel extraction of low-level
properties of scene– Sequential goal-directed processing
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Eye
Stage 1Early, parallel detection of color, texture, shape, spatial attributes
Stage 2Serial processing of object identification (using memory) and spatial layout, action
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Stage 1 - Low-level, Parallel
g 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
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Stage 2 - Sequential, Goal-Directed
g Splits into subsystems for object recognition and for interacting with environment
g Increasing evidence supports independence of systems for symbolic object manipulation and for locomotion & action
g First subsystem then interfaces to verbal linguistic portion of brain, second interfaces to motor systems that control muscle movementsMay 21, 2014
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Stage 2 Attributes
g Slow serial processingg Involves working and long-term
memoryg Top-down processing
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Preattentive Processing
g How does human visual system analyze images?– Some things seem to be done
preattentively, without the need for focused attention
– Generally less than 200-250 msecs (eye movements take 200 msecs)
– Seems to be done in parallel by low-level vision system
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How Many 3’s?
1281768756138976546984506985604982826762980985845822450985645894509845098094358590910302099059595957725646750506789045678845789809821677654876364908560912949686
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How Many 3’s?
1281768756138976546984506985604982826762980985845822450985645894509845098094358590910302099059595957725646750506789045678845789809821677654876364908560912949686
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What Kinds of Tasks?
g Target detection– Is something there?
g Boundary detection– Can the elements be grouped?
g Counting– How many elements of a certain type
are present?
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Examples:
g Where is the red circle? Left or right?g Put your hand up as soon as you see
it.
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Pre-attentive Hue
g Can be done rapidly
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Examples:
g Where is the red circle? Left or right?g Put your hand up as soon as you see
it.
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Shape
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Examples:
g Where is the red circle? Left or right?g Put your hand up as soon as you see
it.
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Hue and Shape
g Cannot be done preattentivelyg Must perform a sequential searchg Conjuction of features (shape and hue)
causes it
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Examples
g Is there a boundary:– A connected chain of features that cross
the rectangleg Put your hand up as soon as you see
it.
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Fill and Shape
g Left can be done preattentively since each group contains one unique feature
g Right cannot (there is a boundary!) since the two features are mixed (fill and shape)
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Examples
g Is there a boundary?
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Hue versus Shape
g Left: Boundary detected preattentively based on hue regardless of shape
g Right: Cannot do mixed color shapes preattentively
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Hue vs. Brightness
g Left: Varying brightness seems to interfereg Right: Boundary based on brightness can
be done preattentively
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Preattentive Features
g Certain visual forms lend themselves to preattentive processing
g Variety of forms seem to work
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3-D Figures
g 3-D visual reality has an influence
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Emergent Features
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Potential PA Features
g lengthg widthg sizeg curvatureg numberg terminatorsg intersectiong closure
g hueg intensityg flickerg direction of motiong stereoscopic depthg 3-D depth cuesg lighting direction
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IAT 355 26May 21, 2014http://www.csc.ncsu.edu/faculty/healey/PP/
IAT 355 27May 21, 2014http://www.csc.ncsu.edu/faculty/healey/PP/
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Key Perceptual Properties
g Brightnessg Colorg Textureg Shape
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Luminance/Brightness
g Luminance– Measured amount of light coming from
some placeg Brightness
– Perceived amount of light coming from source
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Brightness
g Perceived brightness is non-linear function of amount of light emitted by sourceTypically a power functionS = aIn
S - sensationI - intensity
g Very different on screen versus paper
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Greyscale
g 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
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Greyscale
g White and Black are not fixed
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Greyscale
g White and Black are not fixed!
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Luminance
g Foreground must be distinct from background!
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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?
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Color Systems
g HSV: Hue, Saturation, Value– Hue: Color type– Saturation:
“Purity” of color– Value: Brightness
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CIE Space
g The perceivable set of colors
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Color for Categories
g 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
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Why 12 colors?
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Just-Noticeable Difference
g Which is brighter?
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Just-Noticeable Difference
g Which is brighter?
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(130, 130, 130) (140, 140, 140)
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Weber’s Law
g In the 1830’s, Weber made measurements of the just-noticeable differences (JNDs) in the perception of weight and other sensations.
g He found that for a range of stimuli, the ratio of the JND ΔS to the initial stimulus S was relatively constant:
ΔS / S = kMay 21, 2014
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Weber’s Law
g Ratios more important than magnitude in stimulus detection
g 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.
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Weber’s Law
g Most continuous variations in magnitude are perceived as discrete steps
g Examples: contour maps, font sizesMay 21, 2014
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Stevens’ Power Law
g Compare area of circles:
1 20
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Stevens’ Power Law
s(x) = axb
s is the sensationx is the intensity of the
attributea is a multiplicative constantb is the power
b > 1: overestimateb < 1: underestimate
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[graph from Wilkinson 99]
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Stevens’ Power LawSensation Exponent Brightness 0.33
Smell 0.55 (Coffee)
Loudness 0.6
Temperature 1.0 (Cold)
Taste 1.3 (Salt)
Heaviness 1.45
Electric Shock 3.5
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[Stevens 1961]
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Stevens’ Power Law
Experimental results for b:
Length .9 to 1.1Area .6 to .9Volume .5 to .8
Heuristic: b ~ 1/sqrt(dimensionality)
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Stevens’ Power Lawg Apparent magnitude scaling
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[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. 96-109, 1971] [slide from Pat Hanrahan]
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Relative Magnitude Estimation
Most accurate
Least accurate
Position (common) scalePosition (non-aligned)
scale
LengthSlopeAngle
AreaVolumeColor (hue/saturation/value)
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Color Sequence
g Can you order these?
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Possible Color Sequences
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Grey Scale
Full Spectral Scale
Partial Spectral Scale
Single Hue Scale
Double-ended Hue Scale
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HeatMap
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ColorBrewer
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Color Purposes
g Call attention to specific datag Increase appeal, memorabilityg Represent discrete categories
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Using Color
g Modesty! Less is moreg Use blue in large regions, not thin
linesg Use adjacent colors that vary in hue
& value
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Using Color
g For large regions, don’t use highly saturated colors (pastels a good choice)
g Do not use adjacent colors that vary in amount of blue
g Don’t use high saturation, spectrally extreme colors together (causes after images)
g Use color for grouping and searchMay 21, 2014
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Texture
g Appears to be combination of– orientation– scale– contrast
g Complex attribute to analyze
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Shape, Symbol
g Can you develop a set of unique symbols that can be placed on a display and be rapidly perceived and differentiated?
g Application for maps, military, etc.g Want to look at different preattentive
aspects
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Glyph Construction
g Suppose that we use two different visual properties to encode two different variables in a discrete data set– color, size, shape, lightness
g 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
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Integral-Separable
g Not one or other, but along an axis
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Integral vs. Separable Dimensions
Integral
SeparableMay 21, 2014
[Ware 2000]
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Shapes
g Superquadric surface can be used to display 2 dimensions
g Color could be added
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Change Blindness
g Is the viewer able to perceive changes between two scenes?– If so, may be distracting– Can do things to minimize noticing
changes
– http://www.psych.ubc.ca/~rensink/flicker/download/
– http://nivea.psycho.univ-paris5.fr/ECS/kayakflick.gif
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