1. 1 Color Kyongil Yoon

2. 2004-02-12 2VISA 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

3. 2004-02-12 3VISA The Physics of Color Per unit wavelength to yield spectral units Per unit wavelength to yield spectral units BRDF or albedo with wavelength BRDF or albedo with wavelength Spectral radiance Spectral radiance The color of source The color of source Black body radiators Black body radiators Spectral power distribution depends only on the temperature of the body Spectral power distribution depends only on the temperature of the body Color temperature of a light source Color temperature of a light source The sun and the sky The sun and the sky The sun: a point light source, daylight, yellow The sun: a point light source, daylight, yellow The sky: a source consisting of a hemisphere with constant existence, skylight (airlight), blue The sky: a source consisting of a hemisphere with constant existence, skylight (airlight), blue Artificial Illumination Artificial Illumination Incandescent light: roughly black-body model Incandescent light: roughly black-body model Fluorescent light: bluish tinge, mimic natural daylight Fluorescent light: bluish tinge, mimic natural daylight Others Others

4. 2004-02-12 4VISA The Physics of Color The color of surfaces The color of surfaces Result of various mechanisms: different absorbtion at different wavelengths, refraction, diffraction, bulk scattering Result of various mechanisms: different absorbtion at different wavelengths, refraction, diffraction, bulk scattering (Spectral) reflectance + (spectral) albedo (Spectral) reflectance + (spectral) albedo Specular reflection Specular reflection

5. 2004-02-12 5VISA Human Color Perception Color matching Color matching Let people match a given color using a certain number of primaries Let people match a given color using a certain number of primaries Subtractive matching Subtractive matching Trichromacy Trichromacy Three primaries are required Three primaries are required Subtractive matching, Independent Subtractive matching, Independent Implies three distinct types of color transducer in the eye Implies three distinct types of color transducer in the eye Grassman’s law Grassman’s law If we mix two test lights, then mixing the matches will match the result if T a = w a1 P 1 +w a2 P 2 +w a3 P 3 and T b = w b1 P 1 +w b2 P 2 +w b3 P 3 then (T a + T b ) = (w a1 +w b1 )P 1 +(w a2 +w b2 )P 2 +(w a3 +w b3 )P 3 If we mix two test lights, then mixing the matches will match the result if T a = w a1 P 1 +w a2 P 2 +w a3 P 3 and T b = w b1 P 1 +w b2 P 2 +w b3 P 3 then (T a + T b ) = (w a1 +w b1 )P 1 +(w a2 +w b2 )P 2 +(w a3 +w b3 )P 3 If two test lights can be matched with the same set of weights, then they will match each other if T a = w a1 P 1 +w a2 P 2 +w a3 P 3 and T b = w b1 P 1 +w b2 P 2 +w b3 P 3 then T a = T b If two test lights can be matched with the same set of weights, then they will match each other if T a = w a1 P 1 +w a2 P 2 +w a3 P 3 and T b = w b1 P 1 +w b2 P 2 +w b3 P 3 then T a = T b Matching is linear if T a = w a1 P 1 +w a2 P 2 +w a3 P 3 then kT a = (kw a1 )P 1 +(kw a2 )P 2 +(kw a3 )P 3 Matching is linear if T a = w a1 P 1 +w a2 P 2 +w a3 P 3 then kT a = (kw a1 )P 1 +(kw a2 )P 2 +(kw a3 )P 3 Some exceptions Some exceptions

6. 2004-02-12 6VISA Human Color Perception Color receptors Color receptors We can assume that there are three distinct types of receptor in the eye that mediate color perception We can assume that there are three distinct types of receptor in the eye that mediate color perception Turns incident light into neural signals Turns incident light into neural signals The principle of univariance The principle of univariance The activity of receptors is of one kind The activity of receptors is of one kind Rods and Cones Rods and Cones Cones dominate color vision Cones dominate color vision Three type of cones differentiated by their sensitivity Three type of cones differentiated by their sensitivity S, M, and L cones (not necessarily blue, green, and red) S, M, and L cones (not necessarily blue, green, and red)

7. 2004-02-12 7VISA Representing Color (Linear Color Spaces) Linear color space Linear color space Agree on a standard set of primaries Agree on a standard set of primaries Describe any color light by the three weights Describe any color light by the three weights Easy to use Easy to use Color matching functions Color matching functions Unit radiance source Unit radiance source Spectral radiance source Spectral radiance source How to deal with subtractive matching How to deal with subtractive matching Negative weight value Negative weight value Standardization by CIE Standardization by CIE Commission international d’eclairage Commission international d’eclairage

8. 2004-02-12 8VISA

9. 2004-02-12 9VISA Linear Color Spaces CIE XYZ Popular standard Popular standard Color matching functions were chosen to be everywhere positive Color matching functions were chosen to be everywhere positive Impossible to get primaries Impossible to get primaries

10. 2004-02-12 10VISA CIE xy The horseshoe line (spectral locus) is the spectral locus. The horseshoe line (spectral locus) is the spectral locus. Hue changes one moves around the spectral locus Hue changes one moves around the spectral locus Out-of-date? Out-of-date?

11. 2004-02-12 11VISA Linear Color Spaces RGB RGB Uses single wavelength primaries (645.16nm for R, 526.32nm for G, 444.44nm for B) Uses single wavelength primaries (645.16nm for R, 526.32nm for G, 444.44nm for B) CMY and black CMY and black Red, yellow, blue: primary colors in subtractive mixture Red, yellow, blue: primary colors in subtractive mixture Simplest color space for subtractive matching Simplest color space for subtractive matching Cyan (W-R), Magenta (W-G), Yellow (W-B) Cyan (W-R), Magenta (W-G), Yellow (W-B) C+M = (W-R) + (W-G) = R+G+B-R-G = B C+M = (W-R) + (W-G) = R+G+B-R-G = B Practical printer uses an additional black Practical printer uses an additional black Quality Quality Cost Cost

12. 2004-02-12 12VISA

13. 2004-02-12 13VISA Representing Color (Non-Linear Color Space) Disadvantage of linear space Disadvantage of linear space Does not encode common properties such hue, saturation Does not encode common properties such hue, saturation Not intuitive Not intuitive Hue, saturation, and value Hue, saturation, and value Hue: the property that varies in passing from red to green Hue: the property that varies in passing from red to green Saturation: the property that varies in passing from red to pink Saturation: the property that varies in passing from red to pink Value: brightness (lightness) Value: brightness (lightness)

14. 2004-02-12 14VISA

15. 2004-02-12 15VISA Non-Linear Color Space Uniform Color Space Uniform color space Uniform color space The distance in coordinate space is a fair guide to the significance of the difference The distance in coordinate space is a fair guide to the significance of the difference Just noticeable differences Just noticeable differences CIE u’v’ space CIE u’v’ space CIE LAB CIE LAB Most popular Most popular Good guide to understand how different two colors will look to a human observer Good guide to understand how different two colors will look to a human observer

16. 2004-02-12 16VISA

17. 2004-02-12 17VISA Spatial and Temporal Effects Chromatic adaptation Chromatic adaptation Assimilation Assimilation Contrast Contrast

18. 2004-02-12 18VISA Statistical Modeling of Colour Data Daniel C. Alexander, Bernard Buxton Daniel C. Alexander, Bernard Buxton Become standard to model Become standard to model Single mode distribution of color data by ignoring the intensity component and constructing a Gaussian model of the chromaticity Single mode distribution of color data by ignoring the intensity component and constructing a Gaussian model of the chromaticity