Abstract
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.
Original language | English |
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Pages | 148-153 |
Number of pages | 6 |
DOIs | |
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
Conference | IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) |
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Country/Territory | United States |
City | San Diego |
Period | 20/06/05 → 26/06/05 |