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Proceedings Paper

Bayesian method for recovering surface and illuminant properties from photosensor responses
Author(s): David H. Brainard; William T. Freeman
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Paper Abstract

The goal of computational color constancy is to recover the physical properties of illuminants and surfaces from photosensor responses. We formulate computational color constancy as a statistical estimation problem. We assume that the likelihood that any particular illuminant or surface will occur in a scene is governed by a prior probability distribution. In particular, we assume that illuminant spectral power distributions are drawn according to multivariate normal distribution over the weights of a finite dimensional linear model, and similarly for surface reflectance functions. Given a set of photosensor responses, Bayes rule may be applied to derive the posterior distribution for illuminants and surfaces. We discuss how to use the posterior to estimate the illuminant. We use simulation to compare the performance of a Bayesian algorithm to that of two previously reported color constancy algorithms. For our simulation conditions, the Bayesian algorithm results in the smallest expected estimation error.

Paper Details

Date Published: 1 May 1994
PDF: 13 pages
Proc. SPIE 2179, Human Vision, Visual Processing, and Digital Display V, (1 May 1994); doi: 10.1117/12.172687
Show Author Affiliations
David H. Brainard, Univ. of California/Santa Barbara (United States)
William T. Freeman, Mitsubishi Electric Research Labs., Inc. (United States)


Published in SPIE Proceedings Vol. 2179:
Human Vision, Visual Processing, and Digital Display V
Bernice E. Rogowitz; Jan P. Allebach, Editor(s)

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