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Computer Science 631 Lecture 6: Color

Computer Science 631 Lecture 6: Color. Ramin Zabih Computer Science Department CORNELL UNIVERSITY. Outline. The visible spectrum and human color perception Color cameras How color is encoded in images. The visible spectrum. Evolution’s camera. Human color perception.

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Computer Science 631 Lecture 6: Color

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  1. Computer Science 631Lecture 6: Color Ramin Zabih Computer Science Department CORNELL UNIVERSITY

  2. Outline • The visible spectrum and human color perception • Color cameras • How color is encoded in images

  3. The visible spectrum

  4. Evolution’s camera

  5. Human color perception • There are two kinds of cells in the retina • Rods and cones • What kind of cells are they? • Most retinal cells are in the fovea (center) • Rods sense luminance (black and white) • Concentrated in the fovea, but not exclusively • Cones sense color

  6. Spatial distribution (cross-section)

  7. Rods versus cones • Rods are more tolerant in terms of handling low light conditions • You don’t see color when it’s night • Cones give you better spatial acuity

  8. rods cones Different overall light sensitivity Results in the Purkinje shift: What appears brightest changes as the sun sets!

  9. Green Blue Red Cones come in three flavors

  10. How we see color • It all depends on how much the different cones are stimulated • It is possible to have two different spectra that stimulate cones the same way • Called a metamer • To a person, these colors look the same, but they are (in some sense) completely different

  11. Some colors do not come from a single wavelength • There will never be a purple laser • Purple comes from blue (short wavelength) and red (long wavelength) light • More precisely, the sensation that we call purple comes from the blue and red cones being stimulated • And no others!

  12. Blue cones are “odd”

  13. Non-uniform distribution • Blue cones are least dense in the fovea • 3-5%, versus about 8% elsewhere • Red cones are about 33%, fairly evenly distributed • Green are 64% in the fovea, about 55% elsewhere

  14. Another way to see this

  15. Color constancy • As the spectrum of the illuminating light changes, so does the pattern of cone stimulus • Yet your red coat looks the same as you walk outside! • No one has a good (computational) understanding of this problem

  16. How many colors can we see? • Humans can discriminate about • 200 hues • 20 saturation values • 500 brightness steps • The NBS lists 267 color names • What about across languages? • Seem to be about 11 basic ones • white, black, red, green, yellow, blue, brown, purple, pink, orange, gray

  17. Just noticeable difference These results are for adjacent colors! With a several-second pause, answer is about 12

  18. Additive versus subtractive colors • Paint is colored because of the spectrum it absorbs (subtracts from the incident light) • Red paint absorbs non-red photons • Color filters are another example • Lights have colors because of the spectrum they emit • Televisions and monitors work this way • The two obey different rules!

  19. Subtractive colors

  20. Additive colors Yellow light plus blue light = what?

  21. Cheap versus expensive cameras • Cheap color (video) cameras have a single CCD • Mask in front of the imaging array • Reduces spatial resolution • More expensive cameras have 3 different video cameras • Color output really is 3 different (independent) signals

  22. Different wavelengths, different focal lengths Note: expensive (achromatic) lenses don’t do this

  23. Consequences of different focal lengths • On a single-CCD system, only one color is really in focus • Typically, it’s the green channel • What about the human visual system?

  24. Colorspace • The colorspace is obviously 3-dimensional • Different ways to represent this space • One goal: distance in color space corresponds to human notion of “similar” colors • Perceptually uniform colorspaces are hard! • The obvious solution is to have one dimension per cone type • Additive, using red, green and blue

  25. RGB color space

  26. How to represent a pure color in RGB There’s a BIG problem here…

  27. Another way to think about color • RGB maps nicely onto the way monitors phosphors are designed • Cameras naturally provide something like RGB • 3 different wavelengths • But there is a more natural way to think about color • Hue, saturation, brightness

  28. Hue, saturation and brightness H dominant wavelength S purity % white B luminance

  29. Color wheel (constant brightness) In this view of color, there is a color cone (this is a cross-section)

  30. CIE colorspace

  31. CIE color chart • X+Y+Z is more or less luminosity • Let’s look at the plane X+Y+Z = 1

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