A geometric-vision approach to solve bilinear problems in general, and the color constancy and illuminant estimation problem in particular, is presented in this paper. We show a general framework, based on ideas from the generalized (probabilistic) Hough transform, to estimate the unknown variables in the bilinear form. In the case of illuminant and reflectance estimation in natural images, each image pixel `votes' for possible illuminants (or reflectance), and the estimation is based on cumulative votes. In the general case, the voting is for the parameters of the bilinear model. The framework is natural for the introduction of physical constraints. For the case of illuminant estimation, we briefly show the relation of this work with previous algorithms for color constancy, and present examples.
Proceedings of the IEEE International Conference on Computer Vision
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