Color by correlation: a simple, unifying framework for colorconstancy
Finlayson, G.D.; Hordley, S.D.; HubeL, P.M.
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Volume 23, Issue 11, Nov 2001 Page(s):1209 - 1221
Digital Object Identifier 10.1109/34.969113
Summary:The paper considers the problem of illuminant estimation: how,
given an image of a scene, recorded under an unknown light, we can
recover an estimate of that light. Obtaining such an estimate is a
central part of solving the color constancy problem. Thus, the work
presented will have applications in fields such as color-based object
recognition and digital photography. Rather than attempting to recover a
single estimate of the illuminant, we instead set out to recover a
measure of the likelihood that each of a set of possible illuminants was
the scene illuminant. We begin by determining which image colors can
occur (and how these colors are distributed) under each of a set of
possible lights. We discuss how, for a given camera, we can obtain this
knowledge. We then correlate this information with the colors in a
particular image to obtain a measure of the likelihood that each of the
possible lights was the scene illuminant. Finally, we use this
likelihood information to choose a single light as an estimate of the
scene illuminant. Computation is expressed and performed in a generic
correlation framework which we develop. We propose a new probabilistic
instantiation of this correlation framework and show that it delivers
very good color constancy on both synthetic and real images. We further
show that the proposed framework is rich enough to allow many existing
algorithms to be expressed within it: the gray-world and gamut-mapping
algorithms are presented in this framework and we also explore the
relationship of these algorithms to other probabilistic and neural
network approaches to color constancy
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