A novel algorithm for color constancy

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Color constancy is the skill by which it is possible to tell the color of an object even under a colored light. I interpret the color of an object as its color under a fixed canonical light, rather than as a surface reflectance function. This leads to an analysis that shows two distinct sets of circumstances under which color constancy is possible. In this framework, color constancy requires estimating the illuminant under which the image was taken. The estimate is then used to choose one of a set of linear maps, which is applied to the image to yield a color descriptor at each point. This set of maps is computed in advance. The illuminant can be estimated using image measurements alone, because, given a number of weak assumptions detailed in the text, the color of the illuminant is constrained by the colors observed in the image. This constraint arises from the fact that surfaces can reflect no more light than is cast on them. For example, if one observes a patch that excites the red receptor strongly, the illuminant cannot have been deep blue. Two algorithms are possible using this constraint, corresponding to different assumptions about the world. The first algorithm, Crule will work for any surface reflectance. Crule corresponds to a form of coefficient rule, but obtains the coefficients by using constraints on illuminant color. The set of illuminants for which Crule will be successful depends strongly on the choice of photoreceptors: for narrowband photoreceptors, Crule will work in an unrestricted world. The second algorithm, Mwext, requires that both surface reflectances and illuminants be chosen from finite dimensional spaces; but under these restrictive conditions it can recover a large number of parameters in the illuminant, and is not an attractive model of human color constancy. Crule has been tested on real images of Mondriaans, and works well. I show results for Crule and for the Retinex algorithm of Land (Land 1971; Land 1983; Land 1985) operating on a number of real images. The experimental work shows that for good constancy, a color constancy system will need to adjust the gain of the receptors it employs in a fashion analagous to adaptation in humans.

Original languageEnglish (US)
Pages (from-to)5-35
Number of pages31
JournalInternational Journal of Computer Vision
Issue number1
StatePublished - Aug 1990
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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