Color

1. Color Frank Dellaert Many slides by Jim Rehg Some slides by David Forsyth

2. Outline • Color and Radiometry • Human Color Perception • Color spaces • Lightness and Color Constancy • Physics-based Vision: Specularities

3. Color and Radiometry • What is color ?

4. What is Color? • A perceptual attribute of objects and scenes constructed by the visual system • A quantity related to the wavelength of light in the visible spectrum • A box of Crayola crayons • A significant industry with conferences, standards bodies, etc. • A challenge • “There are no second-rate brains in color vision” – Edwin Land

5. Why is Color Important? • In animal vision • food vs nonfood • identify predators and prey • Check health, fitness, etc. of other individuals. • In computer vision • Skin finder • Segment an image

6. Reflectance Model

7. Illumination Spectra Blue skylight Tungsten bulb

8. Reflectance Spectra

9. Human Color Perception • What is the retinal basis for color perception in humans?

10. Human Photoreceptors Fovea Periphery

11. Human Cone Sensitivities • Spectral sensitivity of L, M, S cones in human eye

12. Color Names for Cartoon Spectra

13. Additive Color Mixing

14. Subtractive Color Mixing

15. Color Matching Process Basis for industrial color standards

16. Color Matching Experiment 1 Image courtesy Bill Freeman

17. Color Matching Experiment 1 Image courtesy Bill Freeman

18. Color Matching Experiment 1 Image courtesy Bill Freeman

19. Color Matching Experiment 1 Image courtesy Bill Freeman

20. Color Matching Experiment 2 Image courtesy Bill Freeman

21. Color Matching Experiment 2 Image courtesy Bill Freeman

22. Color Matching Experiment 2 Image courtesy Bill Freeman

23. Color Matching Experiment 2 Image courtesy Bill Freeman

24. Principle of Trichromaticity

25. Conclusion from Color Matching • Three primaries are sufficient for most people to reproduce arbitrary colors. Caveats: • Some people use different weights, a consequence of a chromosomal disorder. • Elderly and neurologically-impaired may require fewer primaries • Random variation in sample population

26. Caveat: Context Matters ! Figure courtesy of D. Forsyth

27. Caveat: Context Matters ! Figure courtesy of D. Forsyth

28. Caveat: Context Matters ! Figure courtesy of D. Forsyth

29. Color Spaces

30. Principle of Univariance • Perceived color depends solely on cone responses From “Foundations of Vision” by B. Wandell

31. Color Spaces • Use color matching functions to define a coordinate system for color. • Each color can be assigned a triple of coordinates with respect to some color space (e.g. RGB). • Devices (monitors, printers, projectors) and computers can communicate colors precisely.

32. Grassman’s Laws

33. Choose primaries A, B, C Given energy function what amounts of primaries will match it? For each wavelength, determine how much of A, of B, and of C is needed to match that wavelength = color matching functions Color matching functions Then our match is:

34. RBG Color Matching • monochromatic • 645.2, 526.3, 444.4 nm. • negative parts -> some colors can be matched only subtractively. Figure courtesy of D. Forsyth

35. CIE XYZ Color Matching CIE XYZ: Color matching functions are positive everywhere, but primaries are imaginary. Usually draw x, y, where x=X/(X+Y+Z) y=Y/(X+Y+Z) Figure courtesy of D. Forsyth

36. Geometry of Color (CIE) • Perceptual color spaces are non-convex • Three primaries can span the space, but weights may be negative. • Curved outer edge consists of single wavelength primaries

37. RGB Color Space Many colors cannot be represented (phosphor limitations)

38. Uniform color spaces • McAdam ellipses (next slide) demonstrate that differences in x,y are a poor guide to differences in color • Construct color spaces so that differences in coordinates are a good guide to differences in color.

39. Figures courtesy of D. Forsyth McAdam ellipses

40. Lightness and Color Constancy

41. Color on Mars !

42. Human Color Constancy • Color constancy: hue and saturation • Lightness constancy: gray-level • Humans can perceive • Color a surface would have under white light (surface color) • Color of reflected light (separate surface color from measured color) • Color of illuminant (limited)

43. Land’s Mondrian Experiments • Squares of color with the same color radiance yield very different color perceptions Photometer: 1.0, 0.3, 0.3 Photometer: 1.0, 0.3, 0.3 Red Red Blue Blue Colored light White light Audience: “Red” Audience: “Blue”

44. Basic Model for Lightness Constancy • Assumptions: • Planar frontal scene • Lambertian reflectance • Linear camera response • Camera model: • Modeling assumptions for scene • Piecewise constant albedo • Slowly-varying Illumination

45. Algorithm Components • Goal: what surfaces look like in white light • Process I: relative brightness. • Process II: absolute reference

46. 1-D Lightness “Retinex” Threshold gradient image to find surface (patch) boundaries Figure courtesy of D. Forsyth

47. 1-D Lightness “Retinex” Figure courtesy of D. Forsyth Integration to recover surface lightness (unknown constant)

48. Extension to 2-D • Spatial issues • Integration becomes much harder • Recover of absolute reference • Brightest patch is white • Average reflectance across scene is known • Gamut is known • Known reference (color chart, skin color…)

49. Color Retinex Images courtesy John McCann

50. Physics-based Vision: Specularities