Multilevel GMRF-based segmentation of image sequences
A probabilistic method for obtaining a complete image representation on the basis of spatial-temporal knowledge is presented. The main goal of the algorithm is to obtain a consistent segmentation of a noisy image sequence. Consistent means that the same region must maintain the same label in all consequent images of the sequence where it appears. To this end, a processing scheme is presented which extends Bayesian networks of Gibbs-Markov random fields (GMRF) to segmentation of dynamic scenes.<<ETX>>