Title :
Hidden Markov fields and unsupervised segmentation of images
Author :
Allagnat, Olivier ; Boucher, Jean-Marc ; He, Dong-Chen ; Pieczynski, Wojciech
Author_Institution :
Ecole Nat. Superieure des Telecommun. de Bretagne, France
fDate :
30 Aug-3 Sep 1992
Abstract :
Deals with unsupervised Bayesian segmentation of images. The authors introduce a new algorithm based on a recent general method of estimation in the case of incomplete data (iterative conditional estimation). The efficiency of the method is compared with a recent algorithm based on the stochastic gradient by L. Younes (1989). Results of numerous simulations are given and an application to a real radar image is also derived
Keywords :
Bayes methods; Markov processes; image segmentation; parameter estimation; hidden Markov fields; iterative conditional estimation; parameter estimation; radar image; stochastic gradient; unsupervised Bayesian segmentation; unsupervised image segmentation; Bayesian methods; Helium; Hidden Markov models; Ice; Image segmentation; Iterative algorithms; Iterative methods; Random variables; Simulated annealing; Stochastic processes;
Conference_Titel :
Pattern Recognition, 1992. Vol.III. Conference C: Image, Speech and Signal Analysis, Proceedings., 11th IAPR International Conference on
Conference_Location :
The Hague
Print_ISBN :
0-8186-2920-7
DOI :
10.1109/ICPR.1992.201936
