Bayesian Decision Theory, the Maximum Local Mass Estimate, and Color Constancy (1995)
| Venue: | IN PROCEEDINGS: FIFTH INTERNATIONAL CONFERENCE ON COMPUTER VISION, PP 210-217, (IEEE COMPUTER |
| Citations: | 16 - 3 self |
BibTeX
@INPROCEEDINGS{Freeman95bayesiandecision,
author = {W. T. Freeman and D. H. Brainard},
title = {Bayesian Decision Theory, the Maximum Local Mass Estimate, and Color Constancy},
booktitle = {IN PROCEEDINGS: FIFTH INTERNATIONAL CONFERENCE ON COMPUTER VISION, PP 210-217, (IEEE COMPUTER},
year = {1995},
pages = {210--217},
publisher = {Society Press}
}
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Abstract
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 describe a new
