Empirical Bayesian Motion Segmentation

We introduce an empirical Bayesian procedure for the simultaneous segmentation of an observed motion field and estimation of the hyper-parameters of a Markov random field prior. The new approach 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 trialand-error strategies for the determination of these parameters and leads to better segmentations.