Color Constancy Using KL-Divergence

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Color Constancy Using KL-Divergence
Color is a useful feature for machine vision tasks. However, its effectiveness is often limited by the fact that the measured pixel values in a scene are influenced by both object surface reflectance properties and incident illumination. Color constancy algorithms attempt to compute color features which are invariant of the incident illumination by estimating the parameters of the global scene illumination and factoring out its effect. A number of recently developed algorithms utilize statistical methods to estimate the maximum likelihood values of the illumination parameters. This paper details the use of KL-divergence as a means of selecting estimated illumination parameter values. We provide experimental results demonstrating the usefulness of the KL-divergence technique for accurately estimating the global illumination parameters of real world images.
Charles R. Rosenberg, Martial Hebert, Sebastian Th
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2001
Where ICCV
Authors Charles R. Rosenberg, Martial Hebert, Sebastian Thrun
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