Title :
Bayesian color constancy revisited
Author :
Gehler, Peter Vincent ; Rother, Carsten ; Blake, Andrew ; Minka, Tom ; Sharp, Toby
Author_Institution :
Max Planck Inst., Tubingen
Abstract :
Computational color constancy is the task of estimating the true reflectances of visible surfaces in an image. In this paper we follow a line of research that assumes uniform illumination of a scene, and that the principal step in estimating reflectances is the estimation of the scene illuminant. We review recent approaches to illuminant estimation, firstly those based on formulae for normalisation of the reflectance distribution in an image - so-called grey-world algorithms, and those based on a Bayesian formulation of image formation. In evaluating these previous approaches we introduce a new tool in the form of a database of 568 high-quality, indoor and outdoor images, accurately labelled with illuminant, and preserved in their raw form, free of correction or normalisation. This has enabled us to establish several properties experimentally. Firstly automatic selection of grey-world algorithms according to image properties is not nearly so effective as has been thought. Secondly, it is shown that Bayesian illuminant estimation is significantly improved by the improved accuracy of priors for illuminant and reflectance that are obtained from the new dataset.
Keywords :
Bayes methods; image colour analysis; lighting; Bayesian color constancy; Bayesian illuminant estimation; computational color constancy; grey-world algorithm; illumination; image formation; scene illuminant; Bayesian methods; Cameras; Image databases; Image resolution; Layout; Lighting; Object recognition; Reflectivity; Testing; Yield estimation;
Conference_Titel :
Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4244-2242-5
Electronic_ISBN :
1063-6919
DOI :
10.1109/CVPR.2008.4587765