Hidden Markov chain (HMC) models have been widely used in unsupervised image
segmentation. In these models, there is a double process; a hidden one noted X and an …

The article studies different methods for estimating the Viterbi path in the Bayesian
framework. The Viterbi path is an estimate of the underlying state path in hidden Markov …

Modeling strongly correlated random variables is a critical task in the context of latent
variable models. A new probabilistic model, called Gaussian Pairwise Markov Field, is …

Pixel labeling problem stands among the most commonly considered topics in image
processing. Many statistical approaches have been developed for this purpose, particularly …

Hidden Markov Trees (HMTs) are successful probabilistic models [1][2][3] in image
segmentation or genetic analysis for example. They offer a good compromise between the …

An adaptive method based on three dimensional Gaussian Markov Random fields (3D-
GMRF) is proposed in this paper for volumetric texture segmentation. A feature vector is …

This paper introduces a triplet Markov tree model designed to minimize the block effect that
may be encountered while segmenting image using Hidden Markov Tree (HMT) modeling …

In this article, we develop a Triplet Markov Tree model with auxiliary random variables for an
approximate inference in an intractable probabilistic model. It is based on recent advances …

An adaptive method based on three dimensional Gaussian Markov Random fields (3D-
GMRF) is proposed in this paper for volumetric texture segmentation. A feature vector is …