In this paper we consider the estimation of Markov models where the transition density is unknown. The approach we propose is the empirical characteristic function (ECF) estimation procedure with an approximate optimal weight function. The approximate optimal weight function is obtained through an Edgeworth/Gram-Charlier expansion of the logarithmic transition density of the Markov process. Bas...

This paper deals with the problem of unsupervised image segmentation. Our goal is to propose a method which is able to segment a color image without any human intervention. The only input is the observed image, all other parameters are estimated during the segmentation process. Our method is model-based, we use a rst order Markov random eld (MRF) model (also known as the Potts model) where the ...

This paper deals with the problem of unsupervised image segmentation. Our goal is to propose a method which is able to segment a color image without any human intervention. The only input is the observed image, all other parameters are estimated during the segmentation process. Our method is model-based, we use a rst order Markov random eld (MRF) model (also known as the Potts model) where the ...

We consider the estimation of the order, i.e., the number of hidden states, of a special class of discrete-time finite-alphabet hidden Markov sources. This class can be characterized in terms of equivalent renewal processes. No a priori bound is assumed on the maximum permissible order. An order estimator based on renewal types is constructed, and is shown to be strongly consistent by computing...