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Lecture 2

Information Visualization Intro – Recap

Foundation in Human Visual Perception– Sensory vs. Cultural– Attention – Searchlight Model– Stages of Visual Processing– Luminance & Color Channels– Pre-Attentive Processing– Mapping Data to Display Variables

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Goal of Information Visualization

Use human perceptual capabilities to gain insights into large data sets that are difficult to extract using standard query languages

Support Exploration– Look for structure, patterns, trends, anomalies, relationships – Provide a qualitative overview of large, complex data sets – Assist in identifying region(s) of interest and appropriate

parameters for more focussed quantitative analysis

Abstract and Large Data Sets– Symbolic – Tabular– Networked – Hierarchical– Textual information

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Information Visualization - Problem Statement

Scientific Visualization – Show abstractions, but based on physical space

Information Visualization– Information does not have any obvious spatial mapping

Fundamental ProblemHow to map non–spatial abstractions

into effective visual form?

GoalUse of computer-supported, interactive, visual 

representations of abstract data to amplify cognition

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Student Videos – Essence of Information Visualization

Copy the following URL into Browser window:

http://www.scils.rutgers.edu/~aspoerri/Teaching/InfoVisResources/student_videos/

and Right click on hyperlink for the name below and use “Save As …” download avi file to computer

Phil Brighthttp://www.scils.rutgers.edu/~aspoerri/Teaching/InfoVisResources/student_videos/bright.avi

Carlos Carrerohttp://www.scils.rutgers.edu/~aspoerri/Teaching/InfoVisResources/student_videos/carrero.avi

Daveia Thomashttp://www.scils.rutgers.edu/~aspoerri/Teaching/InfoVisResources/student_videos/thomas.avi

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Approach

1 Foundation in Human Visual PerceptionHow it relates to creating effective information visualizations

2 Understand Key Design Principles

for Creating Information Visualizations

3 Study Major Information Visualization Tools

4 Evaluate Information Visualizations Tools

5 Design New, Innovative Visualizations

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Human Visual System – Overview

Sensory vs. Cultural

Attention – Searchlight Model

Stages of Visual Processing

Luminance & Color Channels

Pre-Attentive Processing

Mapping Data to Display Variables

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Sources

Information VisualizationPerception for Design

Colin WareAcademic Press, 2000

As well as:

• Marti Hearst (Berkeley)

• Christopher Healey (North

Carolina)

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Sensory vs. Cultural

a

AB

C

D

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Sensory vs. Cultural (cont.)

Visualization = Learned Language ?– Meaning of Symbol = Created by Convention

– If true, choice of visual representation arbitrary

– Semiotics = Study of Symbols and how they convey Meaning

Choice of Visual Representation Matters– Outlines

Object outline and object itself excite similar neural processes

Visual cortex designed to detect continuous contours

– Similar perceptual illusions / blindness in humans and animals

– Not all diagrammatic notations are equal

Most visualizations are Hybrids– Learned conventions and hard-wired processing

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Physical World Structured

Well-Defined Surfaces

Objects have mostly smooth surfaces

Temporal Persistence

Objects don’t randomly appear/vanish

Light travels in Straight Lines

reflects off surfaces in certain ways

Law of Gravity

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Our Premise

Sensory RepresentationsTap into Perceptual Power of Brain Without Learning

Sensory Representations Effectivebecause well matched to early stages of neural processing

– Understanding without training

– Perceptual Illusions Persist Mueller-Lyon Illusion (off by 25-30%)

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Attention – Searchlight Model

a

VisualSearch orMonitoringStrategy

EyeMovementControl

Useful VisualField of View

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Attention – Searchlight Properties

Searchlight Size varies with – Data density– Stress level

Attention Operators work within searchlight beam

Attention = Tunable Filter

Eye movements 3/sec – series of saccades

Popout Effects (general attention)

Segmentation Effects (dividing up the visual field)

Guide Attention

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Stages of Visual Processing

1 Rapid Parallel Processing– Feature Extraction: orientation, color, texture, motion

– Transitory: briefly held in an iconic store

– Bottom-up, data-driven processing

2 Serial Goal-Directed Processing– Object recognition: visual attention & memory important.

– Slow and serial processing

– Uses both short-term memory and long-term memory

– More emphasis on arbitrary aspects of symbols

– Different pathways for object recognition & visually guided motion

– Top-down processing

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Parallel Processes Serial Processes

Parallel Processing• Orientation• Texture• Color• Motion

a

AB

C

D

Detection• Edges• Regions• 2D Patterns

Serial Processing• Object Identification• Short Term Memory 5 ± 2 = 3 to 7 Objects

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Visual Angle

dr

h

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Acuities

Vernier Super Acuity (10 sec)

Two Point acuity (0.5 min)

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Spatial Frequency Acuity

Contrast

Spatial Freq.

Need Sufficient Contrastfor Fine Details

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Acuity Distribution

10 30 50103050

Distance from Fovea (deg.)

100

80

60

40

20

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Scale Matters

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Luminance “channel”

Extracts Surface Information

Discounts Illumination Level

Discounts Color of Illumination

Mechanisms1 Adaptation2 Simultaneous Contrast

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Luminance is not Brightness

Luminance = physical measure

Brightness = perceived amount of light

Eye sensitive over 9 orders or magnitude– 5 orders of magnitude (room – sunlight)– Receptors bleach and less sensitive with more light– Takes up to half an hour to recover sensitivity

Eye is NOT a light meterDesigned to detect CHANGESNot good for detecting Absolute ValuesExtremely sensitive to Differences & Changes

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Simultaneous Contrast

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Edge Detection

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Luminance for Shape-from-Shading

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Color Trichromacy

a

G+B +R

a b

Three cones types in retina

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Cone Sensitivity Functions – Blue / Green / Red

400 500 600 700

Wavelength (nm)

20

100

80

60

40

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Color Implications

Color Perception is Relative

Sensitive to Small Differences

– hence need sixteen million colors

Not Sensitive to Absolute Values

– hence we can only use < 10 colors for coding

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Color = Classification

Rapid Visual Segmentation

Color helps us determine type

a

whiteblack

green yellow

green

blue brown

pinkpurpleorangegrey

red

yellow

Only about six categories

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Color Coding

Large areas = low saturation

Small areas = high saturation

12 Colors for labeling

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Channel Properties – Take Home Messages

Luminance Channel

Detail

Form

Shading

Motion

Stereo

Chromatic Channels

Surfaces of Things

Labels

Categories (about 6-10)

Red, green, yellow

and blue are special

(unique hues)

More Important

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Color - Take Home Messages

Use Luminance for Detail, Shape and Form

Use Color for Categorization - few colors

Minimize Contrast Effects

Strong colors for small areasContrast in luminance with background

Subtle colors for large areas

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Pre-Attentive Processing

Some Visual Properties Processed Pre-Attentively– No need to focus attention

Pre-Attentive Properties Important for Design of Visualizations– Can be perceived immediately– Can mislead viewer

< 200 - 250ms– Eye movements = at least 200ms– Some processing can be done very quickly

Implies low-level processing in parallel

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Segmentation by Primitive Features

How many areas ?

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Pre-Attentive Processing

How many 3s ?

08028085080830802809850-802808567847298872ty458202094757720021789843890r455790456099272188897594797902855892594573979209

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Color Pre-Attentive (Pops out)

How many 3s ?

08028085080830802809850-802808567847298872ty458202094757720021789843890r455790456099272188897594797902855892594573979209

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Orientation and Size - Gabor Primitives

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Pre-Attentive Experiment

3 6 12

Number of distractors

500

700

900Number of irrelevant items varies

Pre-attentive 10 msec per item or better.

Decision = Fixed Timeregardless of the number of distractors Preattentive

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Pre-Attentive Processing - Color

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Pre-Attentive Processing - Orientation

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Pre-Attentive Processing - Motion

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Pre-Attentive Processing - Size

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Pre-Attentive Processing - Simple shading

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Pre-Attentive – Summary

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Conjunction (does not pop out)

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Compound features (do not pop out)

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Example: Conjunction of Features

Viewer cannot rapidly and accurately determine if target (red circle) is present or absent when target has two or more features, each of which are present in the distractors. Viewer must search sequentially.

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Laws of Pre-Attentive Display

Must Stand Out in Simple Dimension

– Color

– Simple Shape = orientation, size

– Motion

– Depth

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Pre-Attentive Channels

Form orientation/size

Color

Simple Motion/Blinking

Spatial, Stereo Depth, Shading, Position

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Pre-Attentive Demo

Pre-Attentive Demo by Christopher Healey

Target = Red Circle

Distractors – blue circles (colour search)– red squares (shape search)– blue circles and red squares (conjunction search)

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Pre-Attentive Conjunctions

Position + Color

Position + Shape

Stereo + Color

Color + Motion

Spatial location + some aspect of form

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Pre-Attentive Lessons

Design Symbols

Based on simple visual attributes

Make symbols distinct

Support Rapid Visual Search (10 msec/item)

Use different channels for different types of information

Do not use large areas of strong color

Faces, etc are not pre-attentive

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Example

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Mapping Data to Display Variables

Position (2)

Orientation (1)

Size (spatial frequency)

Motion (2)++

Blinking?

Color (3)

Accuracy Ranking for Quantitative Perceptual Tasks

Angle Slope

Length

Position

Area

Volume

Color Density

More Accurate

Less Accurate

(Mackinlay 88 from Cleveland & McGill)

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Ranking of Visual Properties for Different Data Types

QUANTITATIVE

PositionLengthAngleSlopeAreaVolumeDensityColor SaturationColor Hue

ORDINAL

PositionDensityColor SaturationColor HueTextureConnectionContainmentLengthAngle

NOMINAL

PositionColor HueTextureConnectionContainmentDensityColor SaturationShapeLength