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Color

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Color

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  1. Color Kyongil Yoon

  2. Color • Chapter 6, “Computer Vision: A Modern Approach” • The experience of colour • Caused by the vision system responding differently to different wavelengths of light. • Radiometric vocabulary to describe energy arriving in different quantities at different wavelengths • Human color perception • Different ways of describing colors VISA

  3. The Physics of Color • Per unit wavelength to yield spectral units • BRDF or albedo with wavelength • Spectral radiance • The color of source • Black body radiators • Spectral power distribution depends only on the temperature of the body • Color temperature of a light source • The sun and the sky • The sun: a point light source, daylight, yellow • The sky: a source consisting of a hemisphere with constant existence, skylight (airlight), blue • Artificial Illumination • Incandescent light: roughly black-body model • Fluorescent light: bluish tinge, mimic natural daylight • Others VISA

  4. The Physics of Color • The color of surfaces • Result of various mechanisms: different absorbtion at different wavelengths, refraction, diffraction, bulk scattering • (Spectral) reflectance + (spectral) albedo • Specular reflection VISA

  5. Human Color Perception • Color matching • Let people match a given color using a certain number of primaries • Subtractive matching • Trichromacy • Three primaries are required • Subtractive matching, Independent • Implies three distinct types of color transducer in the eye • Grassman’s law • If we mix two test lights, then mixing the matches will match the resultif Ta = wa1P1+wa2P2+wa3P3 and Tb = wb1P1+wb2P2+wb3P3then (Ta + Tb) = (wa1+wb1)P1+(wa2+wb2)P2+(wa3+wb3)P3 • If two test lights can be matched with the same set of weights, then they will match each otherif Ta = wa1P1+wa2P2+wa3P3 and Tb = wb1P1+wb2P2+wb3P3then Ta = Tb • Matching is linearif Ta = wa1P1+wa2P2+wa3P3 then kTa = (kwa1)P1+(kwa2)P2+(kwa3)P3 • Some exceptions VISA

  6. Human Color Perception • Color receptors • We can assume that there are three distinct types of receptor in the eye that mediate color perception • Turns incident light into neural signals • The principle of univariance • The activity of receptors is of one kind • Rods and Cones • Cones dominate color vision • Three type of cones differentiated by their sensitivity • S, M, and L cones (not necessarily blue, green, and red) VISA

  7. Representing Color (Linear Color Spaces) • Linear color space • Agree on a standard set of primaries • Describe any color light by the three weights • Easy to use • Color matching functions • Unit radiance source • Spectral radiance source • How to deal with subtractive matching • Negative weight value • Standardization by CIE • Commission international d’eclairage VISA

  8. VISA

  9. Linear Color SpacesCIE XYZ • Popular standard • Color matching functions were chosen to be everywhere positive • Impossible to get primaries VISA

  10. CIE xy • The horseshoe line (spectral locus) is the spectral locus. • Hue changes one moves around the spectral locus • Out-of-date? VISA

  11. Linear Color Spaces • RGB • Uses single wavelength primaries(645.16nm for R, 526.32nm for G, 444.44nm for B) • CMY and black • Red, yellow, blue: primary colors in subtractive mixture • Simplest color space for subtractive matching • Cyan (W-R), Magenta (W-G), Yellow (W-B) • C+M = (W-R) + (W-G) = R+G+B-R-G = B • Practical printer uses an additional black • Quality • Cost VISA

  12. VISA

  13. Representing Color (Non-Linear Color Space) • Disadvantage of linear space • Does not encode common properties such hue, saturation • Not intuitive • Hue, saturation, and value • Hue: the property that varies in passing from red to green • Saturation: the property that varies in passing from red to pink • Value: brightness (lightness) VISA

  14. VISA

  15. Non-Linear Color SpaceUniform Color Space • Uniform color space • The distance in coordinate space is a fair guide to the significance of the difference • Just noticeable differences • CIE u’v’ space • CIE LAB • Most popular • Good guide to understand howdifferent two colors will look to a human observer VISA

  16. VISA

  17. Spatial and Temporal Effects • Chromatic adaptation • Assimilation • Contrast VISA

  18. Statistical Modeling of Colour Data • Daniel C. Alexander, Bernard Buxton • Become standard to model • Single mode distribution of color data by ignoring the intensity component and constructing a Gaussian model of the chromaticity VISA