01097nas a2200169   4500008004100000020001400041245005300055210005200108260001200160300001600172490000600188520058900194100001800783700001700801700002100818856008800839       1995                        eng d  a1057-714900aDPA: a deterministic approach to the MAP problem0 aDPA a deterministic approach to the MAP problem  c1995///  a1312 - 13140 v43 aDeterministic 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.1 aBerthod, Marc1 aKato, Zoltan1 aZerubia, Josiane  uhttps://www.inf.szte.hu/publication/dpa-a-deterministic-approach-to-the-map-problem01192nas a2200169   4500008004100000245004500041210004500086260003000131300001600161520065100177100001700828700002100845700001800866700002500884700003200909856008100941       1995                        eng d00aUnsupervised adaptive image segmentation0 aUnsupervised adaptive image segmentation  aPiscatawaybIEEEc1995///  a2399 - 24023 aThis 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.1 aKato, Zoltan1 aZerubia, Josiane1 aBerthod, Marc1 aPieczynski, Wojciech1 a*Society, *IEEE, Signal Pro  uhttps://www.inf.szte.hu/publication/unsupervised-adaptive-image-segmentation01826nas a2200157   4500008004100000245008400041210006900125260003000194300001400224520122500238100001701463700002101480700001801501700002901519856012001548       1995                        eng d00aUnsupervised parallel image classification using a hierarchical Markovian model0 aUnsupervised parallel image classification using a hierarchical   aPiscatawaybIEEEc1995///  a169 - 1743 aThis 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).1 aKato, Zoltan1 aZerubia, Josiane1 aBerthod, Marc1 a*Society, IEEE, Computer  uhttps://www.inf.szte.hu/publication/unsupervised-parallel-image-classification-using-a-hierarchical-markovian-model01560nas a2200169   4500008004100000245007600041210006900117260003200186300001400218520092900232100001701161700001801178700002101196700003301217700002801250856011201278       1993                        eng d00aMultiscale Markov random field models for parallel image classification0 aMultiscale Markov random field models for parallel image classif  aLos AlamitosbIEEEc1993///  a253 - 2573 aIn 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.1 aKato, Zoltan1 aBerthod, Marc1 aZerubia, Josiane1 a*Analysis, *IEEE, Computer S1 a*Intelligence, *Machine  uhttps://www.inf.szte.hu/publication/multiscale-markov-random-field-models-for-parallel-image-classification01324nas a2200169   4500008004100000245007200041210006900113260002800182300001400210520069500224100001700919700001800936700002100954700003200975700003901007856010801046       1993                        eng d00aParallel image classification using multiscale Markov random fields0 aParallel image classification using multiscale Markov random fie  aNew YorkbIEEEc1993///  a137 - 1403 aIn 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.1 aKato, Zoltan1 aBerthod, Marc1 aZerubia, Josiane1 a*Society, *IEEE, Signal Pro1 a*Engineers, *Institute, of Electri  uhttps://www.inf.szte.hu/publication/parallel-image-classification-using-multiscale-markov-random-fields