DocumentCode :
1593918
Title :
Hidden evidential Markov trees and image segmentation
Author :
Pieczynski, Wojciech
Author_Institution :
Dept. Signal et Image, Inst. Nat. des Telecommun., Evry, France
Volume :
1
fYear :
1999
fDate :
6/21/1905 12:00:00 AM
Firstpage :
338
Abstract :
The problem addressed in this paper is that of statistical segmentation of images using hidden Markov models. The problem is to introduce a prior evidential knowledge, defined by a mass function, or equivalently, by a belief function. We notice that the result of the Dempster-Shafer fusion of an evidential Markov field with a probability provided by the observations is not necessarily a Markov field. Thus using classical Bayesian segmentation as MPM or MAP is not tractable. In order to solve this problem, we show that the use of Markov trees, which is another way of modelling the spatial dependence of the class random process, leads to tractable segmentation methods. In fact, the Dempster-Shafer fusion does not destroy the Markovianity in the a posteriori distribution and thus the classical Bayesian segmentation methods like as MPM or MAP may used. Furthermore, some ways of the model parameter estimation are indicated
Keywords :
Bayes methods; case-based reasoning; hidden Markov models; image segmentation; parameter estimation; trees (mathematics); Dempster-Shafer fusion; a posteriori distribution; a prior evidential knowledge; belief function; class random process; classical Bayesian segmentation; hidden evidential Markov trees; image segmentation; mass function; model parameter estimation; spatial dependence; statistical segmentation; Bayesian methods; Context modeling; Density measurement; Hidden Markov models; Image segmentation; Parameter estimation; Probability distribution; Random processes; Satellites;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on
Conference_Location :
Kobe
Print_ISBN :
0-7803-5467-2
Type :
conf
DOI :
10.1109/ICIP.1999.821626
Filename :
821626
Link To Document :
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