Bayesian Decision Theory, the Maximum Local Mass Estimate, and Color Constancy
|MERL Report: ||TR94-23: William T. Freeman, David H. Brainard
IEEE Intl. Conf. on Computer Vision, Cambridge, MA, June, 1995
Computational vision algorithms are often developed in a Bayesian framework. Two estimators are commonly used: maximum a posteriori (MAP), and minimum mean squared error (MMSE). We argue that neither is appropriate for perception problems. The MAP estimator makes insufficient use of structure in the posterior probability. The squared error penalty of the MMSE estimator does not reflect typical penalties. We apply this new estimator to color constancy. An unknown illuminant falls on surfaces of unknown colors. We seek to estimate both the illuminant spectrum and the surface spectra from photosensor responses which depend on the product of these unknown spectra. In simulations, we show that the MLM method performs better than the MAP estimator, and better than two standard color constancy algorithms. The MLM estimate may prove useful in other vision problems as well.