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Color in Information Display, Vis06 10/31/2006
Maureen Stone, StoneSoup Consulting 1
Color in Information Display
Maureen StoneStoneSoup Consulting
Course Evaluations
IEEE CS will send out an email invitation• Web-based questionaires• About 1-2 weeks after the conference
Value• Help define courses for the future • Chance to win an iPod
Information Display
Graphical presentation of information• Charts, graphs, diagrams, maps, illustrations• Originally hand-crafted, static• Now computer-generated, dynamic
Color is a key component• Color labels and groups• Color scales (colormaps)• Multi-variate color encoding• Color shading and textures• And more…
www.nps.gov
“Color” includes Gray
Maps courtesy of the National Park Service (www.nps.gov)
Effective Color
Materials
Aesthetics
Perception
Overview
Color Models (8:30-9:30)• From trichromacy to appearance (4 steps) • With application examples
Principles for Visualization (9:30-10:15)• Tufte• Design practice
Break (10:15-10:45)Value & Contrast (10:45-11:15)
• Legibility• Layering
Craft of RGB Color (11:15-12:15)• RGB specification• Color management (demo)
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What is Color?
Physical World Visual System Mental Models
Lights, surfaces, objects
Eye, optic nerve, visual
cortex
Red, green, brown
Bright, light, dark, vivid, colorful, dull
Warm, cool, bold, blah, attractive, ugly,
pleasant, jarring
Perception and Cognition
Color Models
Physical World Visual System Mental Models
Opponent Encoding
Separate lightness, chroma
(A,R-G,Y-B)
LightEnergy
Spectral distribution functions
F(λ)
ConeResponse
Encode as three values
(L,M,S)
CIE (X,Y,Z)
Appearance Models
Color in Context
AdaptationBackground
Size …
CIECAM02
Perceptual Models
Color “Space”
Hue lightnesssaturation
CIELABMunsell(HVC)
Physical World
Spectral Distribution• Visible light• Power vs. wavelength
Any source• Direct• Transmitted• Reflected• Refracted
From A Field Guide to Digital Color, © A.K. Peters, 2003
Visual System
Light path• Cornea, pupil, lens, retina• Optic nerve, brain
Retinal cells• Rods and cones• Unevenly distributed
Cones• Three “color receptors”• Concentrated in fovea
From Gray’s Anatomy
Cone Response
Encode spectra as three values• Long, medium and short (LMS)• Trichromacy: only LMS is “seen”• Different spectra can “look the same”
Sort of like a digital camera*
From A Field Guide to Digital Color, © A.K. Peters, 2003
Non-uniform Distribution
From Fairchild, Color Appearance Models
Illustration of the retinal photoreceptor mosaic
S = blueM = greenL = red
No blue cones
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Effects of Retinal Encoding
All spectra that stimulate the same cone responseare indistinguishable
Metameric match
Color Measurement
CIE Standard ObserverCIE tristimulus values (XYZ)All spectra that stimulate the same tristimulus
(XYZ) response are indistinguishable
From A Field Guide to Digital Color, © A.K. Peters, 2003
Project X,Y,Z on a plane to separate colorfulness from brightness
x = X/(X+Y+Z)y = Y/(X+Y+Z)z = Z/(X+Y+Z)
1 = x+y+z
Chromaticity Diagram
XYZ = xyY
Characterize Displays
R,G,B are points (varying lightness)Sum of two colors lies on lineGamut is a triangle
• White/gray/blacknear center
• Saturated colorson edges
Display Gamuts
From A Field Guide to Digital Color, © A.K. Peters, 2003
Projector Gamuts
From A Field Guide to Digital Color, © A.K. Peters, 2003
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Pixel to Intensity Mapping(“gamma curve”)
Same image, different mappings
Pixels to Perception
Measure luminance or intensity• For a given spectrum• Normalized luminance = normalized intensity• Y = kI (Y = luminance, I= intensity, k is a scalar < 1.0)
Measure R, G and B intensity ramps• Steps from min to max (0 to 255)• Chromaticity should be constant
Affected by• Brightness, contrast controls• White point controls• Display controller settings• Black level
Luminance from RGB
Y = rYR+gYG+bYB
YR,YG,YB• Maximum luminance of RGB primaries• Different for different displays
r,g,b• Linear wrt luminance (intensity) • See previous slide
Not a fixed equation!
Color Models
Physical World Visual System Mental Models
Opponent Encoding
Separate lightness, chroma
(A,R-G,Y-B)
LightEnergy
Spectral distribution functions
F(λ)
ConeResponse
Encode as three values
(L,M,S)
CIE (X,Y,Z)
Appearance Models
Color in Context
Adaptation,Background,
Size, …
CIECAM02
Perceptual Models
Color “Space”
Hue, lightnesssaturation
CIELABMunsell(HVC)
TrichromacyMetamerism
Color matchingColor measurement
Opponent Color
Definition• Achromatic axis• R-G and Y-B axis• Separate lightness from chroma channels
First level encoding• Linear combination of LMS• Before optic nerve• Basis for perception• Defines “color blindness”
Vischeck
Simulates color vision deficiencies• Web service or Photoshop plug-in• Robert Dougherty and Alex Wade
www.vischeck.com
Deuteranope Protanope Tritanope
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2D Color Space Similar Colors
protanope deuteranope luminance
Genes in Vischeck Red-Green Color Encoding
Rule 1: Don’t do itRule 2: I said, don’t do itRule 3: If you must do it, provide an alternative
Smart Money
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Maureen Stone, StoneSoup Consulting 6
Blue is Good
Real blue (not 0,0,1) is very nice• Add green• Bright blue (0,0.5,1)
Sequential• Blue-cyan• Blue-white
Diverging• Blue-gray-orange (blue-gray-yellow)• Blue-gray-black
Image encoding
Achromatic (L*)B-Y (b*)
R-G (a*)
Original
Image encoding
Original Reconstructed
Color Models
Physical World Visual System Mental Models
Opponent Encoding
Separate lightness, chroma
(A,R-G,Y-B)
LightEnergy
Spectral distribution functions
F(λ)
ConeResponse
Encode as three values
(L,M,S)
CIE (X,Y,Z)
Appearance Models
Color in Context
Adaptation,Background,
Size, …
CIECAM02
Perceptual Models
Color “Space”
Hue, lightnesssaturation
CIELABMunsell(HVC)
Separate lightness, chroma
Color blindness
Image encoding
Perceptual Color Spaces
Lightness
Hue
Colorfulness
Unique black and whiteUniform differences
Perception & design
Munsell Atlas
Courtesy Gretag-Macbeth
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CIELAB and CIELUV
Lightness (L*) plus two color axis (a*, b*)Non-linear function of CIE XYZDefined for computing color differences (reflective)
CIELABCIELUV
From Principles of Digital Image Synthesis by Andrew Glassner. SF: Morgan Kaufmann Publishers, Fig. 2.4 & 2.5, Page 63 & 64© 1995 by Morgan Kaufmann Publishers. Used with permission.
CIELAB Equations
Ratio with reference white
Offset cube root (linear near zero)
Polar coordinatescoordinatesfor hue and chroma
Lightness Scales
Lightness, brightness, luminance, and L*• Lightness is relative, brightness absolute• Absolute intensity is light power
Luminance is perceived intensity• Luminance varies with wavelength• Variation defined by luminous efficiency function• Equivalent to CIE Y
L* is perceptually uniform lightness
Luminance & Intensity
Intensity • Integral of spectral distribution (power)
Luminance • Intensity modulated by wavelength sensitivity• Integral of spectrum × luminous efficiency function
Green and blue lights of equal intensityhave different luminance values
Luminance & L*
L* is a function of normalized luminanceRange 0 to100
L* = 116(Y/Yn)1/3-16
The L* Function
Combination power + linear functions• Not 1/3 power• Best fit is 1/2.43
L* ~ 100(Y/Yn)1/2.43
Similar issue in other non-linear lightness specifications
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Luminance vs. L*
Gray values are proportional to luminance (Y) and L*
Luminance
Color
L*
Design Color Models
Hue (color wheel)• Red, yellow, blue equally spaced• Other spacing also used
Chroma • Intensity or purity• Distance from gray
Value (lightness)
See www.handprint.com for examples
Colortool in CIELAB
NASA Color Usage Research Lab, Larry Arend
Psuedo-Perceptual Models
HLS, HSV (HSB)NOT perceptual modelsSimple renotation of RGB
• View along gray axis• See a hue hexagon• L or V is grayscale pixel value
L & V do NOT predict perceived lightness
Luminance vs. HLS & HSV
Luminance
HSV
Color
HLS
Color Models
Physical World Visual System Mental Models
Opponent Encoding
Separate lightness, chroma
(A,R-G,Y-B)
LightEnergy
Spectral distribution functions
F(λ)
ConeResponse
Encode as three values
(L,M,S)
CIE (X,Y,Z)
Appearance Models
Color in Context
Adaptation,Background,
Size, …
CIECAM02
Perceptual Models
Color “Space”
Hue, lightnesssaturation
CIELABMunsell(HVC)
“Intuitive” colorColor differences
Color design
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Color Appearance
Image courtesy of John MCann
Image courtesy of John MCannImage courtesy of John MCann
Color Appearance
More than a single color• Adjacent colors (background)• Viewing environment (surround)
Appearance effects• Adaptation• Simultaneous contrast• Spatial effects
Color in context
Color Appearance ModelsColor Appearance ModelsMark FairchildMark Fairchild
surround
background
stimulus
Adaptation
Change in illuminationLight/Dark adaptation
• Pupil response• Photopigment sensitivity• Sunlight to starlight
Chromatic adaptation• Cones “white balance”• Scale cone sensitivities• Creates unique white
Daylight
Tungsten
From Color Appearance Models, fig 8-1
Scale Cone Response
La = kLL
Ma = kMM
Sa = kSS
0
0.25
0.5
0.75
1
1.25
Rel
ativ
e C
one
Res
pons
ivity
400 450 500 550 600 650 700
Wavelength (nm)
kL= 1/Lwhite, etc.
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von Kries Adaptation
Adapt to new illuminationL2M2S2 from L1M1S1
Ratio of new/old white (white2/white1)Assumes full adaptation to new illumination
L2 = (Lwhite2/Lwhite1)L1
M2 = (Mwhite2/Mwhite1)M1
S2 = (Swhite2/Swhite1)S1
LMS from XYZ
LMS describes cone responseBetter for appearance modeling than XYZLinear transformation
• Various similar matrices in use
L
M
S
0.7328 0.4296 -0.1624
-0.7036 1.6975 0.0061
0.0030 0.0136 0.9834
X
Y
Z
=
XYZ2 from XYZ1
Lwhite2/Lwhite1 0.0 0.0 X1
Y1
Z1
=
Where M is the transformation from XYZ to LMS
MM-1 0.0 Mwhite2/Mwhite1 0.0
0.0 0.0 Swhite2/Swhite1
X2
Y2
Z2
Measure XYZ1 when adapted to white1
Adapt to white2
Predict XYZ that will “look the same” (XYZ2)
Not change in object color, change in stimulus (e.g. pixels)
Simultaneous Contrast
Add Opponent Color• Dark adds light• Red adds green• Blue adds yellow
These samples will have both light/dark and hue contrast
Affects Lightness Scale Bezold Effect
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CrispeningPerceived difference depends on background
From Fairchild, Color Appearance Models
Spreading
Spatial frequency• The paint chip problem• Small text, lines, glyphs• Image colors
Adjacent colors blend
Redrawn from Foundations of Vision© Brian Wandell, Stanford University
Color Appearance Models
Take measurements• Tristimulus values
Compute appearance• Lightness & brightness• Chroma, hue & colorfulness• Saturation
Models• CIELAB, RLAB, LLAB• S-CIELAB• CIECAM97s, CIECAM02
Measure physical stimuli
Stimulus, background, surround, etc.
Calculate tristimulus values XYZ (LMS)
Stimulus, background, surround, etc.
Calculate correlates of perceptual attributes
Lightness, brightness, chroma, hue, colorfulness, saturation
Requirements
Minimum requirements• Extension of CIE colorimetry• Predict lightness, chroma and hue• Chromatic adaptation transform (CAT)
Also in CIECAM97s, CIECAM02• Absolute illumination• Background parameters• Surround (dark, dim or average)• Degree of adaptation (none to full)
CIE TC1-34, Testing Color Appearance Models
Applications of CAMs
Color reproduction• Model adaptation across media• Aid in mapping out-of-gamut colors
Model simultaneous contrast• Predict confusing color symbols (Brewer)• Compensate to give equal appearance on different
backgrounds (DiCarlo & Sabataitis)
Model color image perception (S-CIELAB)
S-CIELAB (images)
http://white.stanford.edu/~brian/scielab/scielab.html ©Xuemei Zhang, Stanford University
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Application to Visualization
Adaptable Color• Same color, different sizes• Same color, different backgrounds
Cross-media rendering• Maintain encoding• Names and relationships?
Interactive Color• Does it appear the same?• User has controls: Zoom, tool tips, etc.
Color Models
Physical World Visual System Mental Models
Opponent Encoding
Separate lightness, chroma
(A,R-G,Y-B)
LightEnergy
Spectral distribution functions
F(λ)
ConeResponse
Encode as three values
(L,M,S)
CIE (X,Y,Z)
Appearance Models
Color in Context
Adaptation,Background,
Size, …
CIECAM02
Perceptual Models
Color “Space”
Hue, lightnesssaturation
CIELABMunsell(HVC) Adaptation
Contrast effectsImage appearanceComplex matching
Visualization
Effective Color: Aesthetics
Materials
Aesthetics
Perception
What makes color effective?
“Good ideas executed with superb craft”—E.R. Tufte
Effective color needs a context• Immediate vs. studied• Anyone vs. specialist• Critical vs. contextual• Culture and expectations• Time and money
Why Should You Care?
Poorly designed color is confusing• Creates visual clutter• Misdirects attention
Poor design devalues the information• Visual sophistication• Evolution of document and web design
“Attractive things work better”—Don Norman
Color Design
Goals• Highlight, emphasize• Create regions, group• Illustrate depth, shape• Evoke nature• Decorate, make beautiful
Color harmony“…successful color combinations, whether these please the eye by using analogous colors, or excite the eye with contrasts.”
–Principles of Color Design, by Wucius Wong
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Color Design Terminology
Hue (color wheel)• Red, yellow, blue (primary)• Orange, green, purple (secondary)• Opposites complement (contrast)• Adjacent are analogous
Chroma (saturation) • Intensity or purity• Distance from gray
Value (lightness)• Dark to light• Applies to all colors, not just gray
Tints and Tones
Tone or shade• Hue + black• Decrease saturation• Decrease lightness
Tint• Hue + white• Decrease saturation• Increase lightness
Gradations Color Design Principles
Control value (lightness)• Ensure legibility• Avoid unwanted emphasis
Use a limited hue palette• Control color “pop out”• Define color grouping• Avoid clutter from too many competing colors
Use neutral backgrounds• Control impact of color• Minimize simultaneous contrast
Modeling Color Design
Design spaces are perceptual spaces• Munsell, OSA, Ostwald• Created also as design spaces
Geometric interpretation of color design• Color schemes based on hue circle• Contrast and analogy as distance• Smooth paths for tints, tones and gradations
Calibrated display• RGB to XYZ• Subject to gamut limitations
B.J. Meier, A.M. Spalter, D. Karelitz, Interactive Color Palette Tools
Envisioning Information
“… avoiding catastrophe becomes the first principle in bringing color to information:Above all, do no harm.”
—E. R. Tufte
www.edwardtufte.com
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Fundamental Uses
To labelTo measureTo represent or to imitate realityTo enliven or decorate
To Label
Identify by Color
Created by Tableau - Visual Analysis for DatabasesTM
Cluster Calendar
Jarke van Wijk, Edward van Selow Cluster and Calendar based Visualization of Time Series Data
Color Weaving
Positive vorticity (red), Negative vorticity (blue), Strongly negative Reynolds shear stress (green)High swirl strength (orange or magenta, depending on direction)
Effectively Visualizing Multi-Valued Flow Data using Color and TextureT. Urness, V. Interrante, I. Marusic, E. Longmire, and B. Ganapathisubramani
Grouping, Highlighting
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Considerations for Labels
How critical is the color encoding?• Unique specification or is it a “hint”?• Quick response, or time for inspection?• Is there a legend, or need it be memorized?
Contextual issues• Are there established semantics?• Grouping or ordering relationships?• Surrounding shapes and colors?
Shape and structural issues• How big are the objects? • How many objects, and could they overlap?• Need they be readable, or only visible?
Labeling Scenarios
Aircraft cockpit design• Quick response• Critical information and conditions• Memorized• 5-7 unique colors, easily distinguishable
Highway signs• Quick response• Critical but redundant information• 10-15 colors?
Typical color desktop• Aid to search• Redundant information• Personal and decorative• How many colors?
Psychophysics of Labeling
13579345978274055249379164782541372387659727710386619874367259047362956372836491056763254378795483675456840378465485690
Time proportional to the number of digits
13579345978274055249379164782541372387659727710386619874367259047362956372836491056763254378795483675456840378465785690
Time proportional to the number of 7’s
13579345978274055249379164782541372387659727710386619874367259047362956372836491056763254378795483675456840378465785690
Both 3’s and 7’s“Pop out”
Preattentive, “pop out”
Contrast Creates Pop-out
Hue and lightness Lightness only
Pop-out vs. Distinguishable
Pop-out• Typically, 5-6 distinct values simultaneously• Up to 9 under controlled conditions
Distinguishable• 20 easily for reasonable sized stimuli• More if in a controlled context• Usually need a legend
Radio Spectrum Map (33 colors)
http://www.cybergeography.org/atlas/us_spectrum_map.pdf
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Distinguishable on Inspection Color Names
Basic names (Berlin & Kay)• Linguistic study of names across 20 languages• Found 11 basic names, similar linguistic evolution
– Black, white, gray– Red, green, blue, yellow– Orange, purple, brown, pink
Hypothesis: ‘‘a total universal inventory of exactly 11 basic color categories exists from which the 11 or fewer basic color terms of any given language are always drawn.’’ —Berlin & Kay, 1969
Subsequent work
Controversy about original hypothesis• Not objective, reflects experimenter• No real support for perceptual/cognitive basis
Recent work by Kay et. al.• Redefines as constraints, modulated by language• Focus on Hering (opponent) primaries• Black, white, red, green, yellow, blue
There exist universal constraints on cross-language color naming related to these 11 basic color percepts, particularly to the six Hering opponent primaries, black, white, red, yellow, green, and blue (14–17). —Kay & Regier, 2003
World Color SurveyWCS Database
• 330 Munsell chips• 24 native speakers of 110 languages • Asked to name each chip
http://www.icsi.berkeley.edu/wcs/
“grue”
Ber
inm
oEnglis
h
Lang
uage
, thou
ght a
nd co
lor: r
ecen
t dev
elopm
ents.
Paul
Kay a
nd T
erry
Regie
r.
Implications for Vis
How universal are names?• Perceptual ties to opponent color, but “grue”• Some evidence of innate “categorical cognition”• But, language influence undeniable
Names are important• Link to cognition• Handle for discussion
Basic names good for English (with caveats)• Red-green color blindness• Indistinct blue-green boundary• Sensitive to background (brown, gray)• Sensitive to media (purple-blue, yellow-green)
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Other Color Names Research
Selection by name• Berk, Brownston & Kaufman, 1982• Meier, et. al. 2003
Image recoloring• Saito, et. al.
Labels in visualization• D’Zmura, Cowan (pop out conditions)• Healey & Booth (automatic selection)
Web experiment• Moroney, et. al. 2003• Some clustering, not basic names
Tableau Color Example
Color palettes• How many? Algorithmic?• Basic colors (regular and pastel)• Extensible? Customizable?
Color appearance• As a function of size• As a function of background
Robust and reliable color names
Distinct, but hard to name Tableau Colors
www.tableausoftware.com
Maximum hue separation Analogous, yet distinct
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Sequential
To Measure
Data to Color
Types of data values• Nominal, ordinal, numeric• Qualitative, sequential, diverging
Types of color scales• Hue scale
– Nominal (labels)– Cyclic (learned order)
• Lightness or saturation scales– Ordered scales– Lightness best for high frequency– More = darker (or more saturated)– Most accurate if quantized
Color Scales
Long history in graphics and visualization• Ware, Robertson et. al• Levkowitz et. al• Rheingans
PRAVDA Color• Rogowitz and Treinish• IBM Research
Cartography• Cynthia Brewer • ColorBrewer
Different Scales
Rogowitz & Treinish, “How not to lie with visualization”
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Density Map
Lightness scale
Lightness scalewith hue and
chroma variationHue scale with
lightness variation
Phase Diagrams (hue scale)
The optical singularities of bianisotropic crystals, by M. V. Berry
Singularities occur where all colors meet
Phases of the Tides
Figure 1.9. Cotidal chart. Tide phases relative to Greenwich are plotted for all the world’s oceans. Phase progresses from red to orange to yellow to green to blue to purple. The lines converge on anphidromic points, singularities on the earth’s surface where there is no defined tide. [Winfree, 1987 #1195 , p. 17].
Brewer Scales
Nominal scales• Distinct hues, but similar emphasis
Sequential scale• Vary in lightness and saturation• Vary slightly in hue
Diverging scale• Complementary sequential scales• Neutral at “zero”
Brewer’s Categories
Cynthia Brewer, Pennsylvania State University
Thematic MapsUS Census Map
Mapping Census 2000: The Geography of U.S. Diversity
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Color Brewer
www.colorbrewer.org
Tableau Color Example
Color scales for encoding data• Displayed as charts and graphs• Quantized or continuous
Issues• Color ramps based on Brewer’s principles• Not single hue/chroma varying in lightness• Create a ramp of the “same color”• Legible different than distinguishable• Center, balance of diverging ramps
Data to color• Data distribution, outliers, quantization• Much harder than colorizing an image
Heat Map (default ramp)
Skewed Data
www.tableausoftware.com
Slightly negative
Full Range
Skewed Data
www.tableausoftware.com
Stepped
Skewed Data
www.tableausoftware.com
Threshold
Skewed Data
www.tableausoftware.com
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Copyright StoneSoup Consulting, 2005
Common GIS Project
http://commongis.jrc.it/
Multivariate Color Sequences
Multi-dimensional Scatter plot
Variable 1, 2 → X, YVariable 3, 4, 5 → R, G, B
Using Color Dimensions to Display Data Dimensions
Beatty and Ware
Color Weaves6 variables = 6 hues, which vary in brightness
Additive mixture (blend) Spatial texture (weave)
Weaving versus Blending (APGV06 and SIGGRAPH poster)Haleh Hagh-Shenas, Victoria Interrante, Christopher Healey and Sunghee Kim
Brewer System
http://www.colorbrewer.org
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How well does it work?
Brewer color scheme
From The Economist
Color and Shading
Shape is defined by lightness (shading)“Color” (hue, saturation) labels
Image courtesy of Siemens
CT image (defines shape) PET color highlights tumor
Color Overlay (Temperature)3D line integral convolution to visualize 3D flow (LIC).Color varies from red to yellow with increasing temperature
http://www-users.cs.umn.edu/~interran/3Dflow.html
Victoria Interrante and Chester Grosch, U. Minnesota
To Represent orImitate Reality
Illustrative Color
www.bartleby.com/107/illus520.htmlGray’s Anatomy of the Human Body Map of Point Reyes
www.nps.gov
But the diagramatic maps of muscles in our illustrated anatomies are not “transcripts” of things seen, but the work of trained observers who build up the pictures of a specimen that has been revealed to them in years of patient study.
From Art and Illusion, E.H. Gombrich
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Simulated Stars
Image courtesy of the American Museum of Natural History and the San Diego Super-computing Center
(SDSC website)
To Enliven or Decorate
Visualization of isoelectron density surfaces around molecules
Marc Levoy (1988)
Which has more information?Which would you rather look at?
Courtesy of the National Park Service
To labelTo measureTo represent or imitate realityTo enliven or decorate
Additional ResourcesCourse notes
• References• Early copy of slides
My website• http://www.stonesc.com/Vis06• Final copy of slides, references
A Field Guide to Digital Color• A.K. Peters Booth• Discount for attending this course
Break until 10:45