Lecture 2
Information Visualization Intro – Recap
Foundation in Human Visual Perception
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Sensory vs. Cultural
Attention – Searchlight Model
Stages of Visual Processing
Luminance & Color Channels
Pre-Attentive Processing
Mapping Data to Display Variables
© Anselm Spoerri
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
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Symbolic
Tabular
Networked
Hierarchical
Textual information
© Anselm Spoerri
Information Visualization - Problem Statement
Scientific Visualization
– Show abstractions, but based on physical space
Information Visualization
– Information does not have any obvious spatial mapping
Fundamental Problem
How to map non–spatial abstractions
into effective visual form?
Goal
Use of computer-supported, interactive, visual
representations of abstract data to amplify cognition
© Anselm Spoerri
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 Bright
http://www.scils.rutgers.edu/~aspoerri/Teaching/InfoVisResources/student_videos/bright.avi
Carlos Carrero
http://www.scils.rutgers.edu/~aspoerri/Teaching/InfoVisResources/student_videos/carrero.avi
Daveia Thomas
http://www.scils.rutgers.edu/~aspoerri/Teaching/InfoVisResources/student_videos/thomas.avi
© Anselm Spoerri
Approach
1 Foundation in Human Visual Perception
How 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
© Anselm Spoerri
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
© Anselm Spoerri
Sources
Information Visualization
Perception for Design
Colin Ware
Academic Press, 2000
As well as:
• Marti Hearst (Berkeley)
• Christopher Healey (North Carolina)
© Anselm Spoerri
Sensory vs. Cultural
A
B
C
D
© Anselm Spoerri
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
© Anselm Spoerri
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
© Anselm Spoerri
Our Premise
Sensory Representations
Tap into Perceptual Power of Brain Without Learning
Sensory Representations Effective
because well matched to early stages of neural processing
– Understanding without training
– Perceptual Illusions Persist
Mueller-Lyon Illusion (off by 25-30%)
© Anselm Spoerri
Attention – Searchlight Model
Useful Visual
Field of View
Visual
Search or
Monitoring
Strategy
Eye
Movement
Control
© Anselm Spoerri
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
© Anselm Spoerri
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
© Anselm Spoerri
Parallel Processes  Serial Processes
Parallel Processing
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Orientation
Texture
Color
Motion
Detection
• Edges
• Regions
• 2D Patterns
A
Serial Processing
• Object Identification
• Short Term Memory
5 ± 2 = 3 to 7 Objects
B
C
D
© Anselm Spoerri
Visual Angle
© Anselm Spoerri
Acuities
Vernier Super Acuity (10 sec)
Two Point acuity (0.5 min)
© Anselm Spoerri
Spatial Frequency Acuity
Contrast
Sufficient Contrast
for Fine Details
Need
Spatial Freq.
© Anselm Spoerri
Acuity Distribution
100
80
60
40
20
50
30
10
10
30
50
Distance fromFovea (deg.)
© Anselm Spoerri
Scale Matters
© Anselm Spoerri
Luminance “channel”
Extracts Surface Information
Discounts Illumination Level
Discounts Color of Illumination
Mechanisms
1 Adaptation
2 Simultaneous Contrast
© Anselm Spoerri
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 meter
Designed to detect CHANGES
Not good for detecting Absolute Values
Extremely sensitive to Differences & Changes
© Anselm Spoerri
Simultaneous Contrast
© Anselm Spoerri
Edge Detection
© Anselm Spoerri
Luminance for Shape-from-Shading
© Anselm Spoerri
Color Trichromacy
Three cones types in retina
a
b
G+B
+R
© Anselm Spoerri
Cone Sensitivity Functions – Blue / Green / Red
100
80
60
40
20
400
500
600
700
Wavelength (nm)
© Anselm Spoerri
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
© Anselm Spoerri
Color = Classification
Rapid Visual Segmentation
Color helps us determine type
Only about six categories
green
white
black
yellow
red
blue
yellow
green
brown
pink
purple
orange
grey
© Anselm Spoerri
Color Coding
Large areas = low saturation
Small areas = high saturation
12 Colors
for labeling
© Anselm Spoerri
Channel Properties – Take Home Messages
Luminance Channel
Chromatic Channels
Detail
Surfaces of Things
Form
Labels
Shading
Categories
Motion
Red, green, yellow
Stereo
(about 6-10)
and blue are special
(unique hues)
 More Important
© Anselm Spoerri
Color - Take Home Messages
Use Luminance for Detail, Shape and Form
Use Color for Categorization - few colors
Minimize Contrast Effects
Strong colors for small areas
Contrast in luminance with background
Subtle colors for large areas
© Anselm Spoerri
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
© Anselm Spoerri
Segmentation by Primitive Features
How many areas ?
© Anselm Spoerri
Pre-Attentive Processing
How many 3s ?
08028085080830802809850-802808
567847298872ty4582020947577200
21789843890r455790456099272188
897594797902855892594573979209
© Anselm Spoerri
Color  Pre-Attentive (Pops out)
How many 3s ?
08028085080830802809850-802808
567847298872ty4582020947577200
21789843890r455790456099272188
897594797902855892594573979209
© Anselm Spoerri
Orientation and Size - Gabor Primitives
© Anselm Spoerri
Pre-Attentive Experiment
900
Number of irrelevant items varies
Pre-attentive 10 msec per item or better.
700
Decision = Fixed Time
500
regardless of the number of distractors
 Preattentive
3
6
12
Number of distractors
© Anselm Spoerri
Pre-Attentive Processing
- Color
© Anselm Spoerri
Pre-Attentive Processing -
Orientation
© Anselm Spoerri
Pre-Attentive Processing -
Motion
© Anselm Spoerri
Pre-Attentive Processing -
Size
© Anselm Spoerri
Pre-Attentive Processing -
Simple shading
© Anselm Spoerri
Pre-Attentive – Summary
© Anselm Spoerri
Conjunction (does not pop out)
© Anselm Spoerri
Compound features (do not pop out)
© Anselm Spoerri
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.
© Anselm Spoerri
Laws of Pre-Attentive Display
Must Stand Out in Simple Dimension
– Color
– Simple Shape
= orientation, size
– Motion
– Depth
© Anselm Spoerri
Pre-Attentive Channels
Form
orientation/size
Color
Simple Motion/Blinking
Spatial, Stereo Depth, Shading, Position
© Anselm Spoerri
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)
© Anselm Spoerri
Pre-Attentive Conjunctions
Position + Color
Position + Shape
Stereo + Color
Color + Motion
 Spatial location + some aspect of form
© Anselm Spoerri
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
© Anselm Spoerri
Example
© Anselm Spoerri
Mapping Data to Display Variables
Position (2)
Orientation (1)
Size
(spatial frequency)
Motion (2)++
Blinking?
Color (3)
© Anselm Spoerri
Accuracy Ranking for Quantitative Perceptual Tasks
Position
More
Accurate
Length
Angle
Slope
Area
Volume
Less
Accurate
Color
Density
(Mackinlay 88 from Cleveland & McGill)
Ranking of Visual Properties for Different Data Types
QUANTITATIVE
ORDINAL
NOMINAL
Position
Length
Angle
Slope
Area
Volume
Density
Color Saturation
Color Hue
Position
Density
Color Saturation
Color Hue
Texture
Connection
Containment
Length
Angle
Position
Color Hue
Texture
Connection
Containment
Density
Color Saturation
Shape
Length
© Anselm Spoerri