Sampling, resampling and colour constancy

The colour constancy as a problem of Bayesian inference is formulated, where one is trying to represent the posterior on possible interpretations on the given image data. The posterior is represented as a set of samples, drawn from the distribution using a Markov chain Monte Carlo method. 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.