Sampling, Resampling and Colour Constancy

Abstract

We formulate colour constancy as a problem of Bayesian inference, where one is trying to represent the posterior on possible interpretations given image data. We represent the posterior as a set of samples, drawn from that distribution using a Markov chain Monte Carlo method. We show how to build an efficient sampler. This approach has the advantage that it unifies the constraints on the problem, and represents possible ambiguities. In turn, a good description of possible ambiguities means that new information, instead of producing contradictions, is easily incorporated by resampling existing samples. The method is demonstrated on the case where surfaces seen in two distinct images are later discovered to be the same. We show examples using images of real scenes.