Computational colour constancy tries to recover the colour of the scene illuminant of an image. Colour constancy algorithms can, in general, be divided into two groups: statistics-based approaches that exploit statistical knowledge of common lights and surfaces, and physics-based algorithms which are based on an understanding of how physical processes such as highlights manifest themselves in images. A combined physical and statistical colour constancy algorithm that integrates the advantages of the statistics-based Colour by Correlation method with those of a physics-based technique based on the dichromatic reflectance model is introduced. In contrast to other approaches not only a single illuminant estimate is provided but a set of likelihoods for a given illumination set. Experimental results on the benchmark Simon Fraser image database show the combined method to clearly out-perform purely statistical and purely physical algorithms.
|Number of pages||6|
|Publication status||Published - Jun 2005|
|Event||IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - San Diego, United States|
Duration: 20 Jun 2005 → 26 Jun 2005
|Conference||IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)|
|Period||20/06/05 → 26/06/05|