In this paper we consider the problem of colour constancy; how given an image of a scene under an unknown illuminant can we recover an estimate of that light? Rather than recovering a single estimate of the illuminant as many previous authors have done, in the first instance we recover a measure of the likelihood that each possible illuminant was the scene illuminant. We do this by correlating image colours with the colours that can occur under each of a set of possible lights. We then recover an estimate of the scene illuminant based on these likelihoods. Computation is expressed and performed in a generic correlation framework which we develop in this paper. We develop a new probabilistic instantiation of this framework which delivers very good colour constancy on synthetic and real images. We show that the proposed framework is rich enough to allow many existing algorithms to be expressed within it; e.g. the grey-world and gamut mapping algorithms. We explore too the relationship of these algorithms to other probabilistic and neural network approaches.
|Number of pages||8|
|Publication status||Published - Sep 1999|
|Event||7th IEEE International Conference on Computer Vision - Kerkyra, Greece|
Duration: 20 Sep 1999 → 27 Sep 1999
|Conference||7th IEEE International Conference on Computer Vision|
|Period||20/09/99 → 27/09/99|