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Carnegie Mellon Robotics Institute

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Abstract

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.

Notes

Associated Lab(s) / Group(s): Robot Learning Lab

Text Reference

Chuck Rosenberg, Martial Hebert, and Sebastian Thrun, "Color constancy using KL-divergence," Proceedings of the Eighth IEEE International Conference on Computer Vision (ICCV '01), July, 2001, pp. 239 - 246.

BibTeX Reference

@inproceedings{Rosenberg_2001_3825,
   author = "Chuck Rosenberg and Martial Hebert and Sebastian Thrun",
   title = "Color constancy using KL-divergence",
   booktitle = "Proceedings of the Eighth IEEE International Conference on Computer Vision (ICCV '01)",
   pages = "239 - 246",
   month = "July",
   year = "2001",
   volume = "1",
}

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