Intro to Human Visual System and Displays

Fundamental Optics Fovea Perception

These slides were developed by Colin Ware, Univ. of New Hampshire

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?)

Perceptual versus Cultural

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Basic Pathways

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7A MSTVIP LIP FST TEO

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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

The machinery

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Parallel processingof orientation, texture,color and motionfeatures

Object Identification,Working Memory

Detection of 2D patterns,contours and regions

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Human Visual Field

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LEFT RIGHT

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Visual Angle

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Acuities

Vernier super acuity (10 sec)

Grating acuityTwo Point acuity (0.5 min)

Human Spatial Acuity

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

Acuity Distribution

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10 30 50103050

Distance from Fovea (deg.)

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Brain Pixels

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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Distance from Fovea (deg.)

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Pixels and Brain Pixels

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Distance from Fovea (deg.)

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0.2 BP

1 bp

Small Screen

0.8 BP

Big Screen

Perception

Many, many ways to trick the vision system.

Intro to Color for Information Display

Color Theory Color Geometries Color applications Labeling Pseudo-color sequences

Trichromacy

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G+B +R

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Three cones types in retina

Cone sensitivity functions

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400 500 600 700

Wavelength (nm)

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Color measurement

Based on the “standard observer” CIE tristimulus values XYX Y is luminance. Assumes all humans are the same

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

Color Channels

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Long (red)

Med (green)

Short (blue)

Luminance

Y-B

R-G

Luminance “channel”

Visual system extracts surface information Discounts illumination level Discounts color of illumination Mechanisms 1) Adaptation 2) Simultaneous contrast

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

Luminance contrast

Contrast for constancy

Contrast for constancy

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

Luminance for Shape-from-shading

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)

Chromatic Channels have Low Spatial Resolution Luminance contrast

needed to see detail

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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

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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

Color phenomena

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Small field tritanopiaChromatic contrast

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

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

Color great for classification

Rapid visual segmentation

Color helps us determine type

Only about six categories

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whiteblack

green yellow

green

blue brown

pinkpurpleorangegrey

red

yellow

Applications

Color interfaces Color coding Color sequences Color for multi-dimensional discrete data

Color Coding

Large areas: low saturation

Small areas high saturation

Break isoluminance with borders

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

Visual Principles

Sensory vs. Arbitrary Symbols Pre-attentive Properties Gestalt Properties Relative Expressiveness of Visual Cues

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

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.

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?

Example: Color Selection

Viewer can rapidly and accurately determinewhether the target (red circle) is present or absent.Difference detected in color.

Example: Shape Selection

Viewer can rapidly and accurately determinewhether the target (red circle) is present or absent.Difference detected in form (curvature)

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.

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

Example: Emergent Features

Target has a unique feature with respect to distractors (open sides) and so the groupcan be detected preattentively.

Example: Emergent Features

Target does not have a unique feature with respect to distractors and so the group cannot be detected preattentively.

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

Use Grouping of Well-Chosen Shapes for Displaying Multivariate Data

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

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

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]

Slide adapted from Tamara Munzner

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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

Gestalt PropertiesProximity

Why perceive pairs vs. triplets?

Slide adapted from Tamara Munzner

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Gestalt PropertiesSimilarity

Slide adapted from Tamara Munzner

Slide adapted from Tamara Munzner

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Gestalt PropertiesContinuity

Slide adapted from Tamara Munzner

Slide adapted from Tamara Munzner

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Gestalt PropertiesConnectedness

Slide adapted from Tamara Munzner

Slide adapted from Tamara Munzner

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Gestalt PropertiesClosure

Slide adapted from Tamara Munzner

Slide adapted from Tamara Munzner

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Gestalt PropertiesSymmetry

Slide adapted from Tamara Munzner

Gestalt Laws of Perceptual Organization (Kaufman 74)

Figure and Ground Escher illustrations are good examples Vase/Face contrast

Subjective Contour

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

Pseudo-color sequences

Issues: How can we see forms (quality) How we read value (quantity)

Pseudo-ColorSequences

Gray scale

Spectrum sequence

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

Spiral Sequence

Luminance to signal direction

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