Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 23, Issue 2, Pages 217-221Publisher
IEEE COMPUTER SOC
DOI: 10.1109/34.908972
Keywords
motion segmentation; layered representations; empirical Bayesian procedures; estimation of hyperparameters; statistical learning; expectation-maximization
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We introduce an empirical Bayesian procedure for the simultaneous segmentation of an observed motion field and estimation of the hyperparameters of a Markov random field prior. The new approach exhibits the Bayesian appeal of incorporating prior beliefs, but requires only a qualitative description of the prior, avoiding the requirement for a quantitative specification of its parameters. This eliminates the need for trial-and-error strategies for the determination of these parameters and leads to better segmentations.
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