Intro to Human Visual System Why Should We Be Interested In and - - PowerPoint PPT Presentation

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

A B C D

Basic Pathways

7A MST VIP LIP FST TEO V3A V4 V3 V2 V1 VISUALL Y GUIDED MOTION PERCEPTION TRANSIENT OBJECT PERCEPTION COLOR CONST ANCY ATTENTION FACES MEMORY SUST AINED DORSAL PATHWAYS VENTRAL PATHWAYS Dynamic form Color and form with color, attention PO MT DP PP STS IT Eye Movement Control Faces, Attention, Short term Memory Filtering for orientation, color, stereo depth

The machinery

Parallel processing

• f orientation, texture,

color and motion features Object Identification, W

• rking Memory

Detection of 2D patterns, contours and regions

A B C D

Human Visual Field

100 80 60

LEFT RIGHT

40 20

Visual Angle

θ

d r h

Acuities

Vernier super acuity (10 sec) Grating acuity Two 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

10 30 50 10 30 50 Distance from Fovea (deg.) 100 80 60 40 20

Brain Pixels

Brain pixels=retinal ganglion cell receptive fields

Tartufieri Field size = 0.006(e+1.0) - Anderson Characters = 0.046e - Anstis Ganglion cells

10 30 50 10 30 50 Distance from Fovea (deg.) 100 80 60 40 20

Pixels and Brain Pixels

10 30 50 10 30 50 Distance from Fovea (deg.) 100 80 60 40 20

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

G+B +R a b

Three cones types in retina

Cone sensitivity functions

400 500 600 700 W avelength (nm) 20 100 80 60 40

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 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 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 a dark background is to be avoided. We have very few short-wavelength sensitive cones in the retina and they are not very sensitive

Color Channels

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

Some Natural philosophers suppose that these colors arise from accidental vapours diffused in the air, which communicate their own hues to the shadows; so that the colours of the shadows are occasioned by the reflection of any given sky colour: the above observations favour this opinion.

T ext on an isoluminant background is hard to read

Some Natural philosophers suppose that these colors arise from accidental vapours diffused in the air, which communicate their own hues to the shadows;

3:1 recommended 10:1 idea for small text

Color phenomena

a b c d

Small field tritanopia Chromatic 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

white black green yellow green blue brown pink purple

• range

grey 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 97 http: / / 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 determine whether the target (red circle) is present or absent. Difference detected in color.

Example: Shape Selection

Viewer can rapidly and accurately determine whether 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 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 97 http://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 group can 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

• thers

Use Grouping of Well-Chosen Shapes for Displaying Multivariate Data

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

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

Text NOT Text NOT Preattentive 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

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

Gestalt Properties

Proximity

Why perceive pairs vs. triplets?

Slide adapted from Tamara Munzner

61

Gestalt Properties

Similarity

Slide adapted from Tamara Munzner Slide adapted from Tamara Munzner

62

Gestalt Properties

Continuity

Slide adapted from Tamara Munzner Slide adapted from Tamara Munzner

63

Gestalt Properties

Connectedness

Slide adapted from Tamara Munzner Slide adapted from Tamara Munzner

64

Gestalt Properties

Closure

Slide adapted from Tamara Munzner

Slide adapted from Tamara Munzner

65

Gestalt Properties

Symmetry

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-Color Sequences 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