title: Bayesian model of human color constancy.
abstract: Vision is difficult because images are ambiguous about the structure of the world. For object color, the ambiguity arises because the same object reflects a different spectrum to the eye under different illuminations. Human vision typically does a good job of resolving this ambiguity-an ability known as color constancy. The past 20 years have seen an explosion of work on color constancy, with advances in both experimental methods and computational algorithms. Here, we connect these two lines of research by developing a quantitative model of human color constancy. The model includes an explicit link between psychophysical data and illuminant estimates obtained via a Bayesian algorithm. The model is fit to the data through a parameterization of the prior distribution of illuminant spectral properties. The fit to the data is good, and the derived prior provides a succinct description of human performance.
authors.affiliation: Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA. brainard@psych.upenn.edu
authors.forename: David H
authors.initials: DH
authors.lastname: Brainard
authors.forename: Philippe
authors.initials: P
authors.lastname: Longère
authors.forename: Peter B
authors.initials: PB
authors.lastname: Delahunt
authors.forename: William T
authors.initials: WT
authors.lastname: Freeman
authors.forename: James M
authors.initials: JM
authors.lastname: Kraft
authors.forename: Bei
authors.initials: B
authors.lastname: Xiao
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003116:Color; D003118:Color Perception / Q000502:physiology*; D006801:Humans; D008027:Light; D008954:Models, Biological; D011601:Psychophysics