IAT 814 1

IAT 814 Introduction to Visual Analytics

Perception

Sep 11, 2013

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Perceptual Processingg 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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A Simple Modelg Two stage process

– Parallel extraction of low-level properties of scene

– Sequential goal-directed processing

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EyeStage 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 movementsSep 11, 2013

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Stage 2 Attributesg Slow serial processingg Involves working and long-term

memoryg Top-down processing

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Preattentive Processingg 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 itSep 11, 2013

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Examplesg Is there a boundary:

– A connected chain of features that cross the rectangle

g 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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Examplesg 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 Featuresg Certain visual forms lend themselves

to preattentive processingg Variety of forms seem to work

g In the next slide, spot the region of different shapes, both left and right

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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 Featuresg 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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Key Perceptual Propertiesg Brightnessg Colorg Textureg Shape

Sep 11, 2013

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Luminance/Brightnessg Luminance

– Measured amount of light coming from some place

g Brightness– Perceived amount of light coming

from source

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Brightnessg 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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Greyscaleg 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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Greyscaleg White and Black are not fixed

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Greyscaleg White and Black are not fixed!

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Color Systemsg HSV: Hue,

Saturation, Value– Hue: Color type– Saturation:

“Purity” of color– Value: Brightness

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CIE Spaceg The perceivable set of colors

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CIE L*a*b*

g http://www.gamutvision.com/docs/printest.htmlSep 11, 2013

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Color Categoriesg 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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Luminanceg Foreground must be distinct from

background!

Sep 11, 2013

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 Categoriesg 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 Differenceg Which is brighter?

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Just-Noticeable Differenceg Which is brighter?

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(130, 130, 130) (140, 140, 140)

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Weber’s Lawg 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 = kSep 11, 2013

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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 Lawg Most continuous variations in

magnitude are perceived as discrete steps

g Examples: contour maps, font sizesSep 11, 2013

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Weber’s Lawg Most continuous variations in

magnitude are perceived as discrete steps

g Examples: contour maps, font sizesSep 11, 2013

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Stevens’ Power Lawg Compare area of circles:

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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

Sep 11, 2013

[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

Sep 11, 2013

[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

Sep 11, 2013

[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 Sequenceg 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 Purposesg Call attention to specific datag Increase appeal, memorabilityg 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 Colorg Modesty! Less is moreg Use blue in large regions, not thin

linesg Use red and green in the center of

the field of view (edges of retina not sensitive to these)

g Use black, white, yellow in peripheryg Use adjacent colors that vary in hue

& valueSep 11, 2013

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Using Colorg 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 searchSep 11, 2013

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Textureg Appears to be combination of

– orientation– scale– contrast

g Complex attribute to analyze

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Shape, Symbolg 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 Constructiong 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

independentlySep 11, 2013

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Integral-Separableg Not one or other, but along an axis

Sep 11, 2013

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Integral vs. Separable Dimensions

Integral

SeparableSep 11, 2013

[Ware 2000]

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Shapesg Superquadric surface

can be used to display 2 dimensions

g Color could be added

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Change Blindnessg 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.gifSep 11, 2013