Wojciech Pieczynski

Learn More
This paper attacks the problem of generalized multisensor mixture estimation. A distribution mixture is said to be generalized when the exact nature of components is not known, but each of them belongs to a finite known set of families of distributions. Estimating such a mixture entails a supplementary difficulty: One must label, for each class and each(More)
Hidden Markov fields (HMF) are widely used in image processing. In such models, the hidden random field of interest S s s X X ∈ = ) ( is a Markov field, and the distribution of the observed random field S s s Y Y ∈ = ) ( (conditional on X ) is given by ∏ ∈ = S s s s x y p x y p ) ( ) ( . The posterior distribution ) ( y x p is then a Markov distribution,(More)
tion problem is to decide, from the observed image, in This paper proposes a new unsupervised fuzzy Bayesian which class each pixel lies. In the first case we speak of image segmentation method using a recent model using hidden fuzzy segmentation, and in the second case of hard segmenfuzzy Markov fields. The originality of this model is to use tation. As we(More)
This work deals with the unsupervised Bayesian hidden Markov chain restoration extended to the non stationary case. Unsupervised restoration based on “ExpectationMaximization” (EM) or “Stochastic EM” (SEM) estimates considering the “Hidden Markov Chain” (HMC) model is quite efficient when the hidden chain is stationary. However, when the latter is not(More)
Due to the enormous quantity of radar images acquired by satellites and through shuttle missions, there is an evident need for efficient automatic analysis tools. This paper describes unsupervised classification of radar images in the framework of hidden Markov models and generalized mixture estimation. Hidden Markov chain models, applied to a Hilbert–Peano(More)
We propose a new model called a Pairwise Markov Chain (PMC), which generalizes the classical Hidden Markov Chain (HMC) model. The generalization, which allows one to model more complex situations, in particular implies that in PMC the hidden process is not necessarily a Markov process. However, PMC allows one to use the classical Bayesian restoration(More)
The use of random elds, which allows one to take into account the spatial interaction among random variables in complex systems, becomes a frequent tool in numerous problems of statistical mechanics, spatial statistics, neural network modelling, and others. In particular, Markov random eld based techniques can be of exceptional eeciency in some image(More)