© Anselm Spoerri Lecture 2 Information Visualization Intro – Recap Foundation in Human Visual...
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![Page 1: © Anselm Spoerri Lecture 2 Information Visualization Intro – Recap Foundation in Human Visual Perception –Sensory vs. Cultural –Attention – Searchlight.](https://reader036.fdocuments.net/reader036/viewer/2022062320/56649d6a5503460f94a47eaa/html5/thumbnails/1.jpg)
© Anselm Spoerri
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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© Anselm Spoerri
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)
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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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© Anselm Spoerri
Ranking of Visual Properties for Different Data Types
QUANTITATIVE
PositionLengthAngleSlopeAreaVolumeDensityColor SaturationColor Hue
ORDINAL
PositionDensityColor SaturationColor HueTextureConnectionContainmentLengthAngle
NOMINAL
PositionColor HueTextureConnectionContainmentDensityColor SaturationShapeLength