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A Comparison of Computational Color Constancy Algorithms, Part One;
Theory and Experiments with Synthetic Data

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Kobus Barnard, Brian Funt, and Vlad Cardei,
"A Comparison of Computational Color Constancy Algorithms, Part One;
Theory and Experiments with Synthetic Data",
IEEE Transactions on Image Processing , Vol. 11, No. 9, pp. 972-984,
Sept. 2002

## Abstract:

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 limiting case of 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 is available on-line at http://www.cs.sfu.ca/~color/data,
and implementations for most of the algorithms are also available
(http://www.cs.sfu.ca/~color/code).

Full text (pdf)

Keywords: Algorithm, color by correlation, color constancy,
comparison, computational, gamut constraint, neural network.

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