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Intro to Human Visual System and Displays
Fundamental Optics Fovea Perception
These slides were developed by Colin Ware, Univ. of New Hampshire
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Why Should We Be Interested In Visualization
Hi bandwidth to the brain (70% of all receptors ,40+% of cortex, 4 billion neurons)
Can see much more than we can mentally image
Can perceive patterns (what dimensionality?)
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Perceptual versus Cultural
a
AB
C
D
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Basic Pathways
aaa
7A MSTVIP LIP FST TEO
V3A V4
V3
V2
V1
VISUALLY GUIDEDMOTION PERCEPTIONTRANSIENT
OBJECT PERCEPTIONCOLOR CONSTANCYATTENTIONFACESMEMORYSUSTAINED
DORSAL PATHWAYS VENTRAL PATHWAYS
Dynamic form
Color andform withcolor,attention
PO MT
DP
PP
STS ITEye Movement Control
Faces,Attention,Short termMemory
Filtering for orientation,color, stereo depth
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The machinery
a
Parallel processingof orientation, texture,color and motionfeatures
Object Identification,Working Memory
Detection of 2D patterns,contours and regions
a
AB
C
D
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Human Visual Field
aa
100
80
60
LEFT RIGHT
40
20
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Visual Angle
dr
h
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Acuities
Vernier super acuity (10 sec)
Grating acuityTwo Point acuity (0.5 min)
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Human Spatial Acuity
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Cutoff at 50 cycles/deg.
Receptors: 20 sec of arc Pooled over larger and larger areas 100 million receptors 1 million fibers to brain A screen may have 30 pixels/cm – need
about 4 times as much. VR displays have 5 pixels/cm
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Acuity Distribution
aaa
10 30 50103050
Distance from Fovea (deg.)
100
80
60
40
20
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Brain Pixels
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Brain pixels=retinal ganglion cell receptive fields
Tartufieri
Field size = 0.006(e+1.0) - AndersonCharacters = 0.046e - Anstis
Ganglion cells
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aaa
10 30 50103050
Distance from Fovea (deg.)
100
80
60
40
20
Pixels and Brain Pixels
aaa
10 30 50103050
Distance from Fovea (deg.)
100
80
60
40
20
0.2 BP
1 bp
Small Screen
0.8 BP
Big Screen
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Perception
Many, many ways to trick the vision system.
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Intro to Color for Information Display
Color Theory Color Geometries Color applications Labeling Pseudo-color sequences
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Trichromacy
a
G+B +R
a b
Three cones types in retina
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Cone sensitivity functions
a
400 500 600 700
Wavelength (nm)
20
100
80
60
40
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Color measurement
Based on the “standard observer” CIE tristimulus values XYX Y is luminance. Assumes all humans are the same
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Short wavelength sensitive cones
Blue text on a dark backgroundis to be avoided. We have very fewshort-wavelength sensitive cones in the retina and they are not very sensitive
Blue text on dark backgroundis to be avoided. We have very fewshort-wavelength sensitive cones in the retina and they are not very sensitive
Blue text on a dark backgroundis to be avoided. We have very fewshort-wavelength sensitive cones in the retina and they are not very sensitive.Chromatic aberration in the eye is also a problem
Blue text on a dark backgroundis to be avoided. We have very fewshort-wavelength sensitive cones in the retina and they are not very sensitive
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Color Channels
a
Long (red)
Med (green)
Short (blue)
Luminance
Y-B
R-G
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Luminance “channel”
Visual system extracts surface information Discounts illumination level Discounts color of illumination Mechanisms 1) Adaptation 2) Simultaneous contrast
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Luminance is not Brightness
Eye sensitive over 9 orders or magnitude 5 orders of magnitude (room – sunlight) Receptors bleach and become less
sensitive with more light Takes up to half an hour to recover
sensitivity We are not light meters
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Luminance contrast
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Contrast for constancy
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Contrast for constancy
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Brightness Lightness and Luminance
Brightness refers to perception of lights
Brightness non linear Monitor Gamma
Lightness refers to perception of surfaces
Perceived lightness depends on a reference white
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Luminance for Shape-from-shading
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Channel Properties
Luminance Channel Detail Form Shading Motion Stereo
Chromatic Channels Surfaces of things Labels Berlin and Kay Categories (about 6-
10) Red, green, yellow
and blue are special (unique hues)
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Chromatic Channels have Low Spatial Resolution Luminance contrast
needed to see detail
a
Some Natural philosopherssuppose that these colors arisefrom accidental vapours diffusedin the air, which communicatetheir own hues to the shadows;so that the colours of theshadows are occasioned bythe reflection of any given skycolour: the above observationsfavour this opinion.
Text onanisoluminantbackgroundis hardto read
a
Some Natural philosopherssuppose that these colors arisefrom accidental vapours diffusedin the air, which communicatetheir own hues to the shadows;
3:1 recommended10:1 idea for small text
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Color phenomena
a
a
b
c
d
Small field tritanopiaChromatic contrast
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Color “blindness”
A 3D to a 2D space 8 % of males R-G color blindness
Can generate color blind acceptable palette
Yellow blue variation OK
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Implications
Color perception is relative We are 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 great for classification
Rapid visual segmentation
Color helps us determine type
Only about six categories
a
whiteblack
green yellow
green
blue brown
pinkpurpleorangegrey
red
yellow
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Applications
Color interfaces Color coding Color sequences Color for multi-dimensional discrete data
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Color Coding
Large areas: low saturation
Small areas high saturation
Break isoluminance with borders
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Color Coding
The same rules apply to color coding text and other similar information. Small areas should have high saturation colors,
Large areas should be coded with low saturation colors
Luminance contrast should be maintained
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Visual Principles
Sensory vs. Arbitrary Symbols Pre-attentive Properties Gestalt Properties Relative Expressiveness of Visual Cues
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Sensory vs. Arbitrary Symbols
Sensory: Understanding without training Resistance to instructional bias Sensory immediacy
Hard-wired and fast
Cross-cultural Validity Arbitrary
Hard to learn Easy to forget Embedded in culture and applications
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American Sign Language Primarily arbitrary, but partly
representational Signs sometimes based partly on
similarity But you couldn’t guess most of them They differ radically across
languages Sublanguages in ASL are more
representative Diectic terms Describing the layout of a room,
there is a way to indicate by pointing on a plane where different items sit.
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All Preattentive Processing figures from Healey 97http://www.csc.ncsu.edu/faculty/healey/PP/PP.html
Pre-attentive Processing
A limited set of visual properties are processed pre-attentively (without need for focusing attention).
This is important for design of visualizations
What can be perceived immediately? What properties are good discriminators? What can mislead viewers?
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Example: Color Selection
Viewer can rapidly and accurately determinewhether the target (red circle) is present or absent.Difference detected in color.
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Example: Shape Selection
Viewer can rapidly and accurately determinewhether the target (red circle) is present or absent.Difference detected in form (curvature)
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Pre-attentive Processing
< 200 - 250ms qualifies as pre-attentive eye movements take at least 200ms yet certain processing can be done very
quickly, implying low-level processing in parallel
If a decision takes a fixed amount of time regardless of the number of distracters, it is considered to be pre-attentive.
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Example: Conjunction of Features
Viewer cannot rapidly and accurately determinewhether the target (red circle) is present or absent when target has two or more features, each of which arepresent in the distractors. Viewer must search sequentially.
All Preattentive Processing figures from Healey 97http://www.csc.ncsu.edu/faculty/healey/PP/PP.html
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Example: Emergent Features
Target has a unique feature with respect to distractors (open sides) and so the groupcan be detected preattentively.
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Example: Emergent Features
Target does not have a unique feature with respect to distractors and so the group cannot be detected preattentively.
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Asymmetric and Graded Preattentive Properties
Some properties are asymmetric a sloped line among vertical lines is preattentive a vertical line among sloped ones is not
Some properties have a gradation some more easily discriminated among than
others
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Use Grouping of Well-Chosen Shapes for Displaying Multivariate Data
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SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXOCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOCGOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREMCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMGOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOCSUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXOCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC
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SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXOCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOCGOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREMCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMGOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOCSUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXOCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC
Text NOT PreattentiveText NOT Preattentive
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Preattentive Visual Properties(Healey 97)
length Triesman & Gormican [1988] width Julesz [1985] size Triesman & Gelade [1980] curvature Triesman & Gormican [1988] number Julesz [1985]; Trick & Pylyshyn [1994] terminators Julesz & Bergen [1983] intersection Julesz & Bergen [1983] closure Enns [1986]; Triesman & Souther [1985] colour (hue) Nagy & Sanchez [1990, 1992]; D'Zmura [1991]
Kawai et al. [1995]; Bauer et al. [1996] intensity Beck et al. [1983]; Triesman & Gormican [1988] flicker Julesz [1971] direction of motion Nakayama & Silverman [1986]; Driver & McLeod [1992] binocular lustre Wolfe & Franzel [1988] stereoscopic depth Nakayama & Silverman [1986] 3-D depth cues Enns [1990] lighting direction Enns [1990]
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Slide adapted from Tamara Munzner
59
Gestalt Principles
Idea: forms or patterns transcend the stimuli used to create them. Why do patterns emerge? Under what circumstances?
Principles of Pattern Recognition “gestalt” German for “pattern” or “form, configuration” Original proposed mechanisms turned out to be wrong Rules themselves are still useful
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Gestalt PropertiesProximity
Why perceive pairs vs. triplets?
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Slide adapted from Tamara Munzner
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Gestalt PropertiesSimilarity
Slide adapted from Tamara Munzner
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Slide adapted from Tamara Munzner
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Gestalt PropertiesContinuity
Slide adapted from Tamara Munzner
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Slide adapted from Tamara Munzner
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Gestalt PropertiesConnectedness
Slide adapted from Tamara Munzner
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Slide adapted from Tamara Munzner
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Gestalt PropertiesClosure
Slide adapted from Tamara Munzner
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Slide adapted from Tamara Munzner
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Gestalt PropertiesSymmetry
Slide adapted from Tamara Munzner
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Gestalt Laws of Perceptual Organization (Kaufman 74)
Figure and Ground Escher illustrations are good examples Vase/Face contrast
Subjective Contour
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More Gestalt Laws
Law of Common Fate like preattentive motion property
move a subset of objects among similar ones and they will be perceived as a group
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Pseudo-color sequences
Issues: How can we see forms (quality) How we read value (quantity)
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Pseudo-ColorSequences
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Gray scale
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Spectrum sequence
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Color Sequences for Maps
Color is poor for form and shape Color is naturally classified Luminance is good for form and shape Luminance results in contrast illusions A spiral sequence in color space - a good
solution
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Spiral Sequence
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Luminance to signal direction
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Take home messages
Use luminance for detail, shape and form Use color for coding - few colors Minimize contrast effects Strong colors for small areas - contrast in
luminance with background Subtle colors can be used to segment
large areas