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A Comparison of Computational Colour Constancy Algorithms; Part One: Methodology and Experiments with Synthesized Data

Kobus Barnard, Brian Funt, Vlad Cardei "A Comparison of Computational Color Constancy Algorithms; Part One: Methodology and Experiments with Synthesized Data," IEEE Transactions in Image Processing, Vol. 11, No. 9, pp. 972-984. [ Full text (pdf) ]


We introduce a context for testing computational color constancy, specify our approach to the implementation of a number of the leading algorithms, and report the results of three experiments using synthesized data. Experiments using synthesized data are important because the ground truth is known, possible confounds due to camera characterization and pre-processing are absent, and various factors affecting color constancy can be efficiently investigated because they can be manipulated individually and precisely.

The algorithms chosen for close study include two gray world methods, a version of the Retinex method, a number of variants of Forsyth's gamut-mapping method, Cardei et al.'s neural net method, and Finlayson et al.'s Color by Correlation method. We investigate the ability of these algorithms to make estimates of three different color constancy quantities: the chromaticity of the scene illuminant, the overall magnitude of that illuminant, and a corrected, illumination invariant, image. We consider algorithm performance as a function of the number of surfaces in scenes generated from reflectance spectra, the relative effect on the algorithms of added specularities, and the effect of subsequent clipping of the data. All data used in this study is available on-line at http://www.cs.sfu.ca/~colour/data.

Keywords: color, colour, color constancy, comparison, algorithm, computational, gamut constraint, color by correlation, neural network,

Full text (pdf)