NDLI: Bayesian decision theory, the maximum local mass estimate, and color constancy

Content Provider	IEEE Xplore Digital Library
Author	Freeman, W.T. Brainard, D.H.
Copyright Year	1995
Description	Author affiliation: Mitsubishi Electr. Res. Lab., Cambridge, MA, USA (Freeman, W.T.)
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 estimator, which we call maximum local mass (MLM) [10, 26, 65], which integrates the local probability density. The MLM method is sensitive to local structure of the posterior probability, which MAP is not. The new method uses an optimality criterion that is appropriate for perception tasks: it finds the most probable approximately correct answer. For the case of low observation noise, we provide an efficient approximation. 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 the 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 method may prove useful in other vision problems as well.<>
Starting Page	210
Ending Page	217
File Size	992690
Page Count	8
File Format	PDF
ISBN	0818670428
DOI	10.1109/ICCV.1995.466784
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-06-20
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Decision theory Layout Computer vision Shape Probability distribution Laboratories Colored noise Parameter estimation Psychology
Content Type	Text
Resource Type	Article

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