Intro to Human Visual System and Displays

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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
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Basic Pathways
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The machinery
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Human Visual Field
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Visual Angle
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Acuities
Vernier super acuity (10 sec)
Grating acuity Two 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
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Brain Pixels
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Brain pixelsretinal ganglion cell receptive
fields
Field size 0.006(e1.0) - Anderson Characters
0.046e - Anstis
Ganglion cells
Tartufieri
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Pixels and Brain Pixels
0.8 BP
1 bp
0.2 BP
Small Screen
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
Three cones types in retina
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Cone sensitivity functions
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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 background is to be avoided.
We have very few short-wavelength sensitive cones
in the retina and they are not very sensitive
Blue text on a dark background is to be avoided.
We have very few short-wavelength sensitive cones
in the retina and they are not very
sensitive. Chromatic aberration in the eye is
also a problem
Blue text on dark background is to be avoided.
We have very few short-wavelength sensitive cones
in the retina and they are not very sensitive
Blue text on a dark background is to be avoided.
We have very few short-wavelength sensitive cones
in the retina and they are not very sensitive
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Color Channels
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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

31 recommended 101 idea for small text
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Color phenomena
Small field tritanopia
Chromatic 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 lt 10 colors for coding

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Color great for classification

• Rapid visual segmentation
• Color helps us determine type
• Only about six categories

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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 couldnt 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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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?

All Preattentive Processing figures from Healey
97http//www.csc.ncsu.edu/faculty/healey/PP/PP.ht
ml
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Example Color Selection
Viewer can rapidly and accurately
determine whether the target (red circle) is
present or absent. Difference detected in color.
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Example Shape Selection
Viewer can rapidly and accurately
determine whether the target (red circle) is
present or absent. Difference detected in form
(curvature)
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Pre-attentive Processing

• lt 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
determine whether the 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.
All Preattentive Processing figures from Healey
97http//www.csc.ncsu.edu/faculty/healey/PP/PP.ht
ml
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Example Emergent Features
Target has a unique feature with respect to
distractors (open sides) and so the group can 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 DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS
NIATREC YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH
RECORDS COLUMNS ECNEICS HSILGNE SDROCER
SNMULOC GOVERNS PRECISE EXAMPLE MERCURY SNREVOG
ESICERP ELPMAXE YRUCREM CERTAIN QUICKLY PUNCHED
METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM GOVERNS
PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE
YRUCREM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS
HSILGNE SDROCER SNMULOC SUBJECT PUNCHED QUICKLY
OXIDIZED TCEJBUS DEHCNUP YLKCIUQ
DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS NIATREC
YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH RECORDS
COLUMNS ECNEICS HSILGNE SDROCER SNMULOC
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Text NOT Preattentive
SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP
YLKCIUQ DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS
NIATREC YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH
RECORDS COLUMNS ECNEICS HSILGNE SDROCER
SNMULOC GOVERNS PRECISE EXAMPLE MERCURY SNREVOG
ESICERP ELPMAXE YRUCREM CERTAIN QUICKLY PUNCHED
METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM GOVERNS
PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE
YRUCREM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS
HSILGNE SDROCER SNMULOC SUBJECT PUNCHED QUICKLY
OXIDIZED TCEJBUS DEHCNUP YLKCIUQ
DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS NIATREC
YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH RECORDS
COLUMNS ECNEICS HSILGNE SDROCER SNMULOC
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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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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 Properties

• Proximity

Why perceive pairs vs. triplets?
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Gestalt Properties

• Similarity

Slide adapted from Tamara Munzner
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Gestalt Properties

• Continuity

Slide adapted from Tamara Munzner
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Gestalt Properties

• Connectedness

Slide adapted from Tamara Munzner
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Gestalt Properties

• Closure

Slide adapted from Tamara Munzner
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Gestalt Properties

• Symmetry

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