IAT 355 1

IAT 355 Introduction to Visual Analytics

Perception

May 21, 2014

IAT 355 2

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

May 21, 2014

IAT 355 3

A Simple Model

g Two stage process– Parallel extraction of low-level

properties of scene– Sequential goal-directed processing

May 21, 2014

Eye

Stage 1Early, parallel detection of color, texture, shape, spatial attributes

Stage 2Serial processing of object identification (using memory) and spatial layout, action

IAT 355 4

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

May 21, 2014

IAT 355 5

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

IAT 355 6

Stage 2 Attributes

g Slow serial processingg Involves working and long-term

memoryg Top-down processing

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IAT 355 7

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

May 21, 2014

IAT 355 8

How Many 3’s?

1281768756138976546984506985604982826762980985845822450985645894509845098094358590910302099059595957725646750506789045678845789809821677654876364908560912949686

May 21, 2014

IAT 355 9

How Many 3’s?

1281768756138976546984506985604982826762980985845822450985645894509845098094358590910302099059595957725646750506789045678845789809821677654876364908560912949686

May 21, 2014

IAT 355 10

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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IAT 355 11

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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IAT 355 12

Pre-attentive Hue

g Can be done rapidly

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IAT 355 13

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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IAT 355 14

Shape

May 21, 2014

IAT 355 15

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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IAT 355 16

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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IAT 355 17

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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IAT 355 18

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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IAT 355 19

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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IAT 355 21

Hue vs. Brightness

g Left: Varying brightness seems to interfereg Right: Boundary based on brightness can

be done preattentively

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IAT 355 22

Preattentive Features

g Certain visual forms lend themselves to preattentive processing

g Variety of forms seem to work

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IAT 355 23

3-D Figures

g 3-D visual reality has an influence

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IAT 355 24

Emergent Features

May 21, 2014

IAT 355 25

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

May 21, 2014

IAT 355 26May 21, 2014http://www.csc.ncsu.edu/faculty/healey/PP/

IAT 355 27May 21, 2014http://www.csc.ncsu.edu/faculty/healey/PP/

IAT 355 28

Key Perceptual Properties

g Brightnessg Colorg Textureg Shape

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IAT 355 29

Luminance/Brightness

g Luminance– Measured amount of light coming from

some placeg Brightness

– Perceived amount of light coming from source

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IAT 355 30

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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IAT 355 31

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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IAT 355 33

Greyscale

g White and Black are not fixed!

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Luminance

g Foreground must be distinct from background!

May 21, 2014

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?

IAT 355 35

Color Systems

g HSV: Hue, Saturation, Value– Hue: Color type– Saturation:

“Purity” of color– Value: Brightness

May 21, 2014

IAT 355 36

CIE Space

g The perceivable set of colors

May 21, 2014

IAT 355 37

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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IAT 355 38

Why 12 colors?

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IAT 355 39

Just-Noticeable Difference

g Which is brighter?

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IAT 355 40

Just-Noticeable Difference

g Which is brighter?

May 21, 2014

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

IAT 355 41

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

IAT 355 42

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.

May 21, 2014

IAT 355 43

Weber’s Law

g Most continuous variations in magnitude are perceived as discrete steps

g Examples: contour maps, font sizesMay 21, 2014

IAT 355 44

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

May 21, 2014

[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]

IAT 355 47

Stevens’ Power Law

Experimental results for b:

Length .9 to 1.1Area .6 to .9Volume .5 to .8

Heuristic: b ~ 1/sqrt(dimensionality)

May 21, 2014

IAT 355 48

Stevens’ Power Lawg Apparent magnitude scaling

May 21, 2014

[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]

IAT 355 49

Relative Magnitude Estimation

Most accurate

Least accurate

Position (common) scalePosition (non-aligned)

scale

LengthSlopeAngle

AreaVolumeColor (hue/saturation/value)

May 21, 2014

IAT 355 50

Color Sequence

g Can you order these?

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IAT 355 51

Possible Color Sequences

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Grey Scale

Full Spectral Scale

Partial Spectral Scale

Single Hue Scale

Double-ended Hue Scale

IAT 355 52

HeatMap

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IAT 355 53

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

IAT 355 57

Texture

g Appears to be combination of– orientation– scale– contrast

g Complex attribute to analyze

May 21, 2014

IAT 355 58

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

May 21, 2014

IAT 355 59

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

May 21, 2014

IAT 355 60

Integral-Separable

g Not one or other, but along an axis

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IAT 355 61

Integral vs. Separable Dimensions

Integral

SeparableMay 21, 2014

[Ware 2000]

IAT 355 62

Shapes

g Superquadric surface can be used to display 2 dimensions

g Color could be added

May 21, 2014

IAT 355 63

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

May 21, 2014