Comp/Phys/Apsc 715 Lecture 5: Trichromacy, Color Spaces, Properties - - PDF document

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Comp/Phys/Apsc 715 Lecture 5: Trichromacy, Color Spaces, Properties - - PDF document

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1/23/2014 Comp/Phys/Apsc 715 Lecture 5: Trichromacy, Color Spaces, Properties of Color 1/23/2014 Color Comp/Phys/Apsc 715 Taylor 1 Example Videos Segmentation and visualization of neurons Astro Visualization (the Millennium Run)

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Comp/Phys/Apsc 715

Lecture 5: Trichromacy, Color Spaces, Properties of Color

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Example Videos

  • Segmentation and visualization of neurons
  • Astro Visualization (the Millennium Run)
  • Dragonfly Flight Analysis

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Administrative

Homework to post by next Thursday At least a week ahead of when it is due

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How Important is Color (Hue)?

  • Color is Irrelevant
  • Color is Critical

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Color is Irrelevant…

  • To determine object shapes
  • To determine layout of objects in space
  • To determine how objects are moving
  • Therefore, to much of modern life

– Laboratory assistant went 21 years without realizing he was color-blind

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Color is Critical…

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Color is Critical…

  • To help us break camouflage
  • To judge the condition of objects (food)

– Ripe or rotten? – Poisonous?

  • To determine material types

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  • Good for labeling and categorizing

–Show classification (labeling) –Mimic reality –Draw attention –Show grouping

  • Poor for displaying shape, detail, or space

–Use luminance

Uses of Color

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Show Classification (Labeling)

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Mimic reality

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Draw attention

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Show grouping

  • Ware, p. 142

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SLIDE 5

1/23/2014 5 Show Value (Don’t use only Hue)

Luminance variation required for detail

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Color Models

  • Device-derived

– convenient for describing display device levels – RGB, CMY(K)

  • Intuitive

– based in familiar color description terms – HSV, HSB, HLS

  • Perceptually uniform

– device independent, perceptually “uniform” – CIELUV, CIELAB, Munsell

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R(ed) G(reen) B(lue)

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H(ue) S(aturation) V(alue) and Kindred

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  • Hill et al. ‘97, pg. 136

Perceptually Uniform

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Opponent Process Theory

  • Cone signals transformed into new channels

– Black/White (Luminance; ignores blue!) – Red/Green – Yellow/Blue

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Color Naming

  • Never “Reddish green” or “Yellowish blue”
  • Across cultures, looking at the appearance of

color names

– If only two, they are black and white – If three, red is next – Fourth and fifth are {yellow, green} (in either order) – Sixth comes blue

  • This supports the opponent-color theory
  • Next comes brown
  • Then {pink, purple, orange, gray}

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Color Categories

  • Task: Name the colors
  • Regions same > 75%
  • Nonuniform sizes
  • Only 8 hues named

small number of labels

  • Why “rainbow scale” is so nonuniform

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Hue vs. Luminance

  • Spatial Sensitivity

– Red/Green and Yellow/Blue each about 1/3 detail of Black/White

  • Stereoscopic Depth

– Pretty much can’t do it with hue alone

  • Temporal Sensitivity

– Moving hue-change patterns seem to move slowly

  • Form

– Shape-from-shading works well – Shape-from-hue doesn’t

  • Category: Hue works well!

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Color Spatial Sensitivity

It is very difficult to read text that is isoluminant with its background color. If clear text material is to be presented it is essential that there be substantial luminance contrast with the background color. Color contrast is not enough. This particular example is especially difficult because the chromatic difference is in the yellow blue direction. The

  • nly exception to the requirement for

luminance contrast is when the purpose is artistic effect and not clarity

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Color Temporal Sensitivity

  • http://visionlab.harvard.edu/Members/

Patrick/Demos/index.html

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Color Temporal Sensitivity

  • http://visionlab.harvard.edu/Members/

Patrick/Demos/index.html

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Application: Color for Labeling

  • Color is comparatively effective for Nominal

Information Coding

– Only about four gray values can code – Can leave luminance channel free for shape perception

  • Issues to consider

– Distinctness, unique hues, number of labels – Contrast with background – Color blindness – Field size – Conventions

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Number of Labels

  • Distinctness (Rapid)
  • Number of Labels

– 5-10 (Healey)

  • Unique Hues
  • Contrast with Background

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Other Issues (1/2)

  • Color Blindness

– Most red/green color blind (10% of males, 1% females)

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Other Issues (2/2)

  • Field Size

– Avoid small spots, especially in yellow/blue – Small areas: strong, highly-saturated colors – Large areas: low saturation with slight differences

  • Conventions

– U.S.: Red = danger, Green = life – Some parts of China: Red = life, Green/white = death – Some scientific domains have color conventions

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Trumbo’s Univariate Principles

  • Univariate

– Order: ordered values should be represented by perceptually-ordered colors – Separation: significantly different levels should be represented by distinguishable colors

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Ordered (and double-ended)

  • Tufte ‘97, pg. 76.

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1/23/2014 12 Not ordered (red in both halves)

  • Tufte ‘97, pg. 77.

Not Perceptually Ordered

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Not ordered (red at both ends!)

Not Perceptually Ordered

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(non-perceptually) Ordered, choppy separation

Non-uniform change

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Ordered?

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Ordered, Separation?

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Ordered, More separation?

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Trumbo’s Bivariate Principles

  • Bivariate

– Rows and columns: to preserve univariate information, display parameters should not obscure one another – Diagonal: to show positive association, displayed colors should group into three perceptual classes: diagonal, above, below

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Rows & Columns, Diagonal

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Not Rows & Columns or Diagonal

  • Tufte ‘83, pg. 153.

Mixes two dimensions

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Hue vs. Saturation (Hmm…)

Just plain bad

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Some Univariate Color Scales

  • Color model component
  • Redundant scales
  • Double-ended

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1/23/2014 16 Color Model Component Scales

  • Change a single color model component with
  • ther components held constant
  • Examples

– Grey scale – Saturation scale – Spectrum (hue, rainbow) scale (BOO, HISS!)

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Luminance (Gray) Scale

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Saturation Scale

Sudden change

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Hue Scale

No luminance change, choppy separation, not perceptually ordered

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Redundant Color Scales

  • Two or more color components varied

together

  • Examples

– Hue with luminance – Heated object scale (black body radiation)

  • Characteristics

– Reinforces signal – Combines characteristics of simpler scales

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Hue+Luminance

Blue loses Luminance Perceptually ordered?

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Hue+Luminance

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Approximates Black Body Radiation

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All Together Now

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SLIDE 19

1/23/2014 19 Double-ended Scale

  • Two distinct scales joined at neutral middle
  • Characteristics

– segments values into two groups – can emphasize both extremes of data range

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Double-Ended Income

  • Olson ‘97, fig. 11-8.

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1/23/2014 20 Consider Data

  • Interesting values?

– Position striking colors at interesting values

  • Zero in range?

– Double-ended scale

  • High spatial frequency?

– Vary lightness in addition to hue

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Consider Audience

  • Color deficient viewers?

– Don’t depend on red-green differentiation – Use redundant scales

  • Application area conventions?

– Use familiar scales (or at least know when you’re not)

  • Color associations with variables?

– Use associated color

  • Color associations with data ranges?

– Use red for bad range (in U.S.) – Use red for hot

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Size and Background Effects

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Saturation-size Illusion

Cleveland and McGill ‘83.

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Chromatic Contrast

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Brown…

  • Brown is dark yellow…

– But not when it is alone in a dark room

  • Must be surrounded by brighter patches

– Otherwise some shade of yellow

  • Be aware that it may not be seen as belonging to the family of

yellows. "I cannot pretend to feel impartial about colours. I rejoice with the brilliant ones and am genuinely sorry for the poor browns." - Sir Winston Churchill

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Web Pointers

  • Color Brewer

– Colorbrewer2.org

  • Color FAQ

– http://www.poynton.com/ColorFAQ.html

  • Penny Rheingans’ Color Perception and Apps

– http://www.cs.umbc.edu/~rheingan/SIGGRAPH/color.i ntro.pdf

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References:

  • Uses of Color and the four examples, Color Models

and the three examples, Univariate, Color Model component (and examples), Redundant (and examples), Color-size illusion, Double-ended (and examples), Multivariate scales (and examples), Evaluating color scales (and examples), Consider Data, Consider Audience: Penny Rheingans

  • The remainder are from Colin Ware’s book

“Information Visualization.”

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