# Color

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```					 Color
Kyongil Yoon

1
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

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The Physics of Color
   Per unit wavelength to yield spectral units
      BRDF or albedo with wavelength
   The color of source
   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

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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

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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 result
if    Ta = wa1P1+wa2P2+wa3P3 and Tb = wb1P1+wb2P2+wb3P3
then (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 other
if    Ta = wa1P1+wa2P2+wa3P3 and Tb = wb1P1+wb2P2+wb3P3
then Ta = Tb
   Matching is linear
if    Ta = wa1P1+wa2P2+wa3P3
then kTa = (kwa1)P1+(kwa2)P2+(kwa3)P3
   Some exceptions
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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)

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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           U ( ) f1 ( ) P  f 2 ( ) P2  f3 ( ) P3
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      Spectral radiance source       S ( ) w1 P  w2 P2  w3 P3
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   How to deal with subtractive matching
      Negative weight value
   Standardization by CIE
      Commission international d’eclairage

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Linear Color Spaces
CIE XYZ
   Popular standard
   Color matching functions were chosen to be
everywhere positive
   Impossible to
get primaries

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   Out-of-date?

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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

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Representing Color
(Non-Linear Color 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)

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Non-Linear Color Space
Uniform Color Space
   Uniform color space
      The distance in coordinate space is a fair guide to the
significance of the difference
      Just noticeable differences
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      CIE u’v’ space                                  Y 3
L*  116    16
      4X           9X                   Yn 
(u ', v ')               ,             
 X  15Y  3Z X  15Y  3Z                     1      1

 X 3  Y 3 
      CIE LAB                                      a*  500          
 X n   Yn  
   
   Most popular                                     
                 
   Good guide to understand how
      1       1

different two colors will look                      Y 3  Z 3 
to a human observer                     b*  200     
 Yn   Z n  

                

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Spatial and Temporal Effects
   Assimilation
   Contrast

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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

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