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CS5600 Computer Graphics by Rich Riesenfeld Spring 2006

Color. CS5600 Computer Graphics by Rich Riesenfeld Spring 2006. Lecture Set 11. Color Issues. Physical nature of color Eye mechanism of color Rods, cones, tri-stimulus model Brain mechanism of color Color spaces Aesthetic and physiological. Color. Color is complicated!

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CS5600 Computer Graphics by Rich Riesenfeld Spring 2006

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  1. Color CS5600 Computer Graphics by Rich Riesenfeld Spring 2006 Lecture Set 11

  2. Color Issues • Physical nature of color • Eye mechanism of color • Rods, cones, tri-stimulus model • Brain mechanism of color • Color spaces • Aesthetic and physiological Utah School of Computing

  3. Color • Color is complicated! • Highly nonlinear • No single model to explain all • Many simplistic models, explanations • Many myths • Much new knowledge Utah School of Computing

  4. Color • Many phenomena to explain • High light / low light • Illusions • Color blindness • Metamers Utah School of Computing

  5. Additive Primaries: (r,g,b) (1,1,0) yellow red green (1,0,0) (0,1,0) white (1,1,1) cyan magenta (0,1,1) (1,0,1) blue (0,0,1 ) Utah School of Computing

  6. Additive Primaries: (r,g,b) …………...<www.jgiesen.de/ColorTheory/RGBColorApplet/rgbcolorapplet.html> Utah School of Computing

  7. Traditional, Artistic: rgb cmy cmyk hsv hsl Perceptually Based: XYZ (Tristimulus) Xyz Hunter-Lab CIE-L*ab CIE-L*CH° CIE-L*CH° CIE-L*ab CIE-L*uv Color Spaces Utah School of Computing

  8. Additive Primaries: (r,g,b) red green (1,0,0) (0,1,0) yellow (1,1,0) Utah School of Computing

  9. Additive Primaries: (r,g,b) red (1,0,0) magenta (1,0,1) blue (0,0,1 ) Utah School of Computing

  10. Subtractive Primaries: (c,m,y) red (1,0,0) yellow magenta (1,1,0) (1,0,1) black (0,0,0) green blue black (0,1,0) (0,0,1 ) cyan (0,1,1) Utah School of Computing

  11. Additive Primaries: (c,m,y) green (0,1,0) cyan (0,1,1) blue (0,0,1 ) Utah School of Computing

  12. Subtractive Primaries: (c,m,y) yellow magenta (1,1,0) (1,0,1) black red (1,0,0) Utah School of Computing

  13. Subtractive Primaries: (c,m,y) yellow (1,1,0) green (0,1,0) cyan (0,1,1) Utah School of Computing

  14. Subtractive Primaries: (c,m,y) magenta (1,0,1) blue (0,0,1 ) cyan (0,1,1) Utah School of Computing

  15. Wavelength Spectrum ultraviolet light infrared light • Seen in physics, physical phenomena (rainbows, prisms, etc) • 1 Dimensional color space 700 600 500 400 Wavelength (nm) Utah School of Computing

  16. Wavelength Spectrum Note that the rainbow does not contain any magenta. It is nonspectral. Utah School of Computing

  17. Color Space • “Navigating,” moving around in a color space, is tricky • Many color representations (spaces) • Can you get to a nearby color? • Can you predictably adjust a color? Utah School of Computing

  18. gray Color Cube: (r,g,b) is RHS cyan blue (0,1,1) (0,0,1 ) magenta white (1,0,1) (1,1,1) green black (0,0,0) (0,1,0) red (1,1,0) (1,0,0) yellow Utah School of Computing

  19. Color Cube cyan (0,0,1 ) blue (0,1,1) magenta white (1,0,1) (1,1,1) green (0,1,0) red yellow (1,0,0) (1,1,0) Utah School of Computing

  20. Complementary Colors Add to Gray cyan blue (0,0,1 ) (0,1,1) magenta white (1,0,1) (1,1,1) green (0,1,0) red yellow (1,0,0) (1,1,0) Utah School of Computing

  21. Complementary Colors Looking at color cube along major diagonal Utah School of Computing

  22. James Clerk Maxwell’s Color green unsaturated cyan cyan yellow  white blue red magenta Utah School of Computing

  23. Newton’s Color Wheel Replaced Aristotle’s color model based on light and darkness. Utah School of Computing

  24. Color Applets www.cs.brown.edu/exploratories/freeSoftware/catalogs/repositoryApplets.html Utah School of Computing

  25. (H,S,V) Color Space • Introduced by Albet Munsell, late 1800s • He was an artist and scientist • Hue: Color • Saturation/Chroma: Strength of a color • Neutral gray has 0 saturation • Brightness/Value: Intensity of light emanating from image Utah School of Computing

  26. (Hue, Saturation, Value/Intensity) (H,S,V) Color Space Thehueof an object may be blue, but the termslightanddarkdistinguish the brightness of one object from another. Utah School of Computing

  27. Saturation Utah School of Computing

  28. Other HSX Color Spaces (Cones) V 120˚ yellow green 0˚ cyan 1.0 red blue magenta 240˚ H S black 0.0 Utah School of Computing

  29. Another HSX Color Space(double cone) L white 1.0 red 0˚ H S black 0.0 29

  30. Tristimulus Color Theory • Any color can be matched by a mixture of three fixed base colors (primaries) • Eye has three kinds of color receptors called cones • Eye also has rods (low light receptors) Utah School of Computing

  31. (Red, Green,Blue) (Long, Medium, Short) 400 440 480 520 560 600 640 680 Color Receptors in Eye Fraction of light absorbed by each type of cone Utah School of Computing Wavelength λ (nm)

  32. Color Receptorsin Eye 1.0 Relative sensitivity 0.0 400 450 500 550 600 650 700 Wavelength λ (nm) Utah School of Computing

  33. Color Response • Why are runway lights blue? • Why are console lights green? • What color is the Kodak box? • Why are green lasers directed toward pilots for destructive purposes? • Whydo soldiers read maps in the dark using dim red light? Utah School of Computing

  34. Color MatchingExperiments • Given a reference color, try to match it identically • What does “negative red,” or “negative color” mean?? Utah School of Computing

  35. CIE* Color Space ( X, Y, Z) represents an imaginary basis that does not correspond to what we see Define the normalized coordinates: x = X / ( X + Y +Z)y = Y / ( X + Y+ Z )z = Z / ( X+ Y + Z ) * Commission Internationale de l'Êclairage Utah School of Computing

  36. CIE Color Space of Visible Colors y x = X / ( X + Y +Z)y = Y / ( X + Y+ Z )z = Z / ( X+ Y + Z ) x x + y + z = 1 z The projection of the plane of the triangle onto the (X,Y) plane forms the chromaticity diagram that follows. Utah School of Computing

  37. Color Gamuts:CIE Color Chart green yellow cyan white red blue magenta Utah School of Computing

  38. 520 nm Color Gamuts: CIE Color Chart ideal green 540 nm 510 nm 560 nm green 500 nm 580 nm yellow 600 nm cyan white red 490 nm 700 nm blue ideal blue ideal red 400 nm Utah School of Computing

  39. Color Gamuts: CIE Color Chart www.cs.rit.edu/~ncs/color/a_chroma.html Utah School of Computing

  40. Color Gamuts: CIE Color Chart 520 nm 540 nm 510 nm 560 nm 500 nm 580 nm 600 nm 700 nm 490 nm 400 nm www.cs.rit.edu/~ncs/color/a_chroma.html Utah School of Computing

  41. Color Gamuts: CIE Color Chart The additive colors C1and C2 combine to form C3on the line connecting C1 and C2. C2 C3 C1 www.cs.rit.edu/~ncs/color/a_chroma.html Utah School of Computing

  42. Color Gamuts: CIE Color Chart The Color Gamuts of different displays and printers are not likely to match. Printers usually have smaller gamuts. B R G www.cs.rit.edu/~ncs/color/a_chroma.html Utah School of Computing

  43. Color Applets www.cs.brown.edu/exploratories/freeSoftware/repository/edu/brown/cs/exploratories/applets/combinedColorMixing/combined_color_mixing_java_browser.html Utah School of Computing

  44. CIE L*a*b* Color Space L*=1 white yellow +b* green lightness -a* red Equal distances represent approximately equal color difference. +a* blue -b* L*=0 black Utah School of Computing

  45. Important Concepts • Adaptation • Slow process • Constancy • Immediate process Utah School of Computing

  46. Output to the Brain fromLateral Geniculate Body G - R- G + R R + Y - G + Y B - B + B Color processing unit: lateral geniculate body Utah School of Computing

  47. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Eye’s Mechanism + + + + + + + + + + + + + + + + + + Utah School of Computing

  48. End Color Lecture Set 11 48

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