%0 Journal Article
%J IEEE TRANSACTIONS ON IMAGE PROCESSING
%D 1995
%T DPA: a deterministic approach to the MAP problem
%A Marc Berthod
%A Zoltan Kato
%A Josiane Zerubia
%X Deterministic pseudo-annealing (DPA) is a new deterministic optimization method for finding the maximum a posteriori (MAP) labeling in a Markov random field, in which the probability of a tentative labeling is extended to a merit function on continuous labelings. This function is made convex by changing its definition domain. This unambiguous maximization problem is solved, and the solution is followed down to the original domain, yielding a good, if suboptimal, solution to the original labeling assignment problem. The performance of DPA is analyzed on randomly weighted graphs.
%B IEEE TRANSACTIONS ON IMAGE PROCESSING
%V 4
%P 1312 - 1314
%8 1995///
%@ 1057-7149
%G eng
%N 9
%! IEEE T IMAGE PROCESS
%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 Proceedings of the 5th International Conference on Computer Vision
%D 1995
%T Unsupervised parallel image classification using a hierarchical Markovian model
%A Zoltan Kato
%A Josiane Zerubia
%A Marc Berthod
%E IEEE Computer *Society
%X This paper deals with the problem of unsupervised classification 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 only observable image. Our approach consists of extending a recent iterative method of estimation, called Iterative Conditional Estimation (ICE) to a hierarchical markovian model. The idea resembles the Estimation-Maximization (EM) algorithm as we recursively look at the Maximum a Posteriori (MAP) estimate of the label field given the estimated parameters then we look at the Maximum Likelihood (ML) estimate of the parameters given a tentative labeling obtained at the previous step. We propose unsupervised image classification algorithms using a hierarchical model. The only parameter supposed to be known is the number of regions, all the other parameters are estimated. The presented algorithms have been implemented on a Connection Machine CM200. Comparative tests have been done on noisy synthetic and real images (remote sensing).
%B Proceedings of the 5th International Conference on Computer Vision
%I IEEE
%C Piscataway
%P 169 - 174
%8 1995///
%G eng
%0 Book Section
%B Fourth International Conference on Computer Vision, ICCV 1993, Berlin, Germany, 11-14 May, 1993, Proceedings
%D 1993
%T Multiscale Markov random field models for parallel image classification
%A Zoltan Kato
%A Marc Berthod
%A Josiane Zerubia
%E *IEEE Computer S *Analysis
%E *Machine *Intelligence
%X In this paper, we are interested in multiscale Markov Random Field (MRF) models. It is well known that multigrid methods can improve significantly the convergence rate and the quality of the final results of iterative relaxation techniques. Herein, we propose a new hierarchical model, which consists of a label pyramid and a whole observation field. The parameters of the coarse grid can be derived by simple computation from the finest grid. In the label pyramid, we have introduced a new local interaction between two neighbor grids. This model gives a relaxation algorithm which can be run in parallel on the entire pyramid. On the other hand, the new model allows to propagate local interactions more efficiently giving estimates closer to the global optimum for deterministic as well as for stochastic relaxation schemes. It can also be seen as a way to incorporate cliques with far apart sites for a reasonable price.
%B Fourth International Conference on Computer Vision, ICCV 1993, Berlin, Germany, 11-14 May, 1993, Proceedings
%I IEEE
%C Los Alamitos
%P 253 - 257
%8 1993///
%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