%0 Book Section
%B ICASSP-95
%D 1995
%T Unsupervised adaptive image segmentation
%A Zoltan Kato
%A Josiane Zerubia
%A Marc Berthod
%A Wojciech Pieczynski
%E *IEEE Signal Pro *Society
%X This paper deals with the problem of unsupervised Bayesian segmentation of images modeled by Markov Random Fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (Simulated Annealing, ICM, etc...). However, when they are not known, the problem becomes more difficult. One has to estimate the hidden label field parameters from the available image only. Our approach consists of a recent iterative method of estimation, called Iterative Conditional Estimation (ICE), applied to a monogrid Markovian image segmentation model. The method has been tested on synthetic and real satellite images.
%B ICASSP-95
%I IEEE
%C Piscataway
%P 2399 - 2402
%8 1995///
%G eng
%0 Book Section
%B ICASSP-93
%D 1993
%T Parallel image classification using multiscale Markov random fields
%A Zoltan Kato
%A Marc Berthod
%A Josiane Zerubia
%E *IEEE Signal Pro *Society
%E *Institute of Electri *Engineers
%X In this paper, we are interested in massively parallel multiscale relaxation algorithms applied to image classification. First, we present a classical multiscale model applied to supervised image classification. The model consists of a label pyramid and a whole observation field. The potential functions of the coarse grid are derived by simple computations. Then, we propose another scheme introducing a local interaction between two neighbor grids in the label pyramid. This is a way to incorporate cliques with far apart sites for a reasonable price. Finally we present the results on noisy synthetic data and on a SPOT image obtained by different relaxation methods using these models.
%B ICASSP-93
%I IEEE
%C New York
%P 137 - 140
%8 1993///
%G eng