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
Multilevel GMRF-based segmentation of image sequences
Regazzoni, C. S.;
1992-01-01
Abstract
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
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