IAT 355 Visual Analytics

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IAT 355 Visual Analytics

• Perception

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SCHOOL
OF INTERACTIVE ARTS TECHNOLOGY SIAT
WWW.SIAT.SFU.CA
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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

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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
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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

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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

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Stage 2 Attributes

• Slow serial processing
• Involves working and long-term memory
• Top-down processing

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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 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 3s?

• 1281768756138976546984506985604982826762
• 9809858458224509856458945098450980943585
• 9091030209905959595772564675050678904567
• 8845789809821677654876364908560912949686

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How Many 3s?

• 1281768756138976546984506985604982826762
• 9809858458224509856458945098450980943585
• 9091030209905959595772564675050678904567
• 8845789809821677654876364908560912949686

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What Kinds of Tasks?

• Target detection
• Is something there?
• Boundary detection
• Can the elements be grouped?
• Counting
• How many elements of a certain type are present?

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Examples

• Is there a red circle?

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Pre-attentive Hue

• Can be done rapidly

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Examples

• Is there a red circle?

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Shape
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Examples

• Is there a red circle?

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Hue and Shape

• Cannot be done preattentively
• Must perform a sequential search
• Conjuction of features (shape and hue) causes it

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Examples

• Is there a boundary
• A connected chain of features that cross the
  rectangle

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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)

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Examples

• Is there a boundary?

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Hue versus Shape

• Left Boundary detected preattentively based on
  hue regardless of shape
• Right Cannot do mixed color shapes
  preattentively

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Hue vs. Brightness

• Left Varying brightness seems to interfere
• Right Boundary based on brightness can be done
  preattentively

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Preattentive Features

• Certain visual forms lend themselves to
  preattentive processing
• Variety of forms seem to work

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3-D Figures

• 3-D visual reality has an influence

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Emergent Features
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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

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Key Perceptual Properties

• Brightness
• Color
• Texture
• Shape

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Luminance/Brightness

• Luminance
• Measured amount of light coming from some place
• Brightness
• Perceived amount of light coming from source

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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

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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

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Greyscale

• White and Black are not fixed

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Greyscale

• White and Black are not fixed!

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Color Systems

• HSV Hue, Saturation, Value
• Hue Color type
• Saturation Purity of color
• Value Brightness

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CIE Space

• The perceivable set of colors

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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 gt 75 commonality

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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?
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Color for Categories

• Can different colors be used for categorical
  variables?
• Yes (with care)
• Colin Wares 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

• Which is brighter?

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Just-Noticeable Difference

• Which is brighter?

(130, 130, 130)
(140, 140, 140)
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Webers Law

• In the 1830s, 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 ?S to the initial stimulus S was
  relatively constant
• ?S / S k

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Webers 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.

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Webers Law

• Most continuous variations in magnitude are
  perceived as discrete steps
• Examples contour maps, font sizes

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Stevens Power Law

• Compare area of circles

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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 gt 1 overestimate
• b lt 1 underestimate

graph from Wilkinson 99
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Stevens Power Law

• Stevens 1961

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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)

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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. 96-109, 1971
slide from Pat Hanrahan
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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)

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Color Sequence

• Can you order these?

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Possible Color Sequences
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

• 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

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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

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Using Color

• For large regions, dont use highly saturated
  colors (pastels a good choice)
• Do not use adjacent colors that vary only in
  the amount of blue
• Dont use high saturation, spectrally extreme
  colors together (causes after images)
• Use color for grouping and search

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Texture

• Appears to be combination of
• orientation
• scale
• contrast
• Complex attribute to analyze

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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

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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

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Integral-Separable

• Not one or other, but along an axis

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Integral vs. Separable Dimensions

• Integral
• Separable

Ware 2000
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Shapes

• Superquadric surface can be used to display 2
  dimensions
• Color could be added

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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
• http//www.psych.ubc.ca/rensink/flicker/download/
  
• http//nivea.psycho.univ-paris5.fr/ECS/kayakflick.
  gif

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Thanks To

• John Stasko, Georgia Tech
• Chris Healey, NC State