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Conference Paper

Bayesian Color Constancy Revisited


Gehler,  PV
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Gehler, P., Rother, C., Blake, A., Minka, T., & Sharp, T. (2008). Bayesian Color Constancy Revisited. In 2008 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-8). Piscataway, NJ, USA: IEEE.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C8F1-3
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.