Title :
Multilevel GMRF-based segmentation of image sequences
Author :
Regazzoni, Carlo S. ; Murino, Vittorio
Author_Institution :
Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy
fDate :
30 Aug-3 Sep 1992
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
Keywords :
Bayes methods; image segmentation; knowledge representation; probability; Bayesian networks; Gibbs-Markov random fields; dynamic scenes; image representation; image sequences; multilevel image segmentation; probabilistic method; processing scheme; spatial-temporal knowledge; Bayesian methods; Image motion analysis; Image representation; Image restoration; Image segmentation; Image sequences; Knowledge engineering; Layout; Optical sensors; Random variables;
Conference_Titel :
Pattern Recognition, 1992. Vol.II. Conference B: Pattern Recognition Methodology and Systems, Proceedings., 11th IAPR International Conference on
Conference_Location :
The Hague
Print_ISBN :
0-8186-2915-0
DOI :
10.1109/ICPR.1992.201876
