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Empirical Bayesian Motion Segmentation
February 2001 (vol. 23 no. 2)
pp. 217-221

Abstract—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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Index Terms:
Motion segmentation, layered representations, empirical Bayesian procedures, estimation of hyperparameters, statistical learning, expectation-maximization.
Citation:
Nuno Vasconcelos, Andrew Lippman, "Empirical Bayesian Motion Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 217-221, Feb. 2001, doi:10.1109/34.908972
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