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Issue No.02 - February (2001 vol.23)
pp: 217-221
<p><b>Abstract</b>—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 <it>qualitative</it> description of the prior, avoiding the requirement for a <it>quantitative</it> specification of its parameters. This eliminates the need for trial-and-error strategies for the determination of these parameters and leads to better segmentations.</p>
Motion segmentation, layered representations, empirical Bayesian procedures, estimation of hyperparameters, statistical learning, expectation-maximization.
Nuno Vasconcelos, Andrew Lippman, "Empirical Bayesian Motion Segmentation", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.23, no. 2, pp. 217-221, February 2001, doi:10.1109/34.908972
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