In this paper we propose a Bayesian framework for unsupervised image fusion and joint segmentation. More specifically we consider the case where we have observed images of the same object through different imaging processes or through different spectral bands (multi or hyper spectral images). The objective of this work is then to propose a coherent approach to combine these images and obtain a ...

In this paper, we demonstrate that multiscale Bayesian image segmentation can be enhanced by improving both contextual modeling and statistical texture characterization. Firstly, we show a joint multi-context and multiscale approach to achieve more robust contextual modeling by using multiple context models. Secondly, we study statistical texture characterization using wavelet-domain Hidden Mar...

This paper deals with a recent statistical model based on fuzzy Markov random chains for image segmentation, in the context of stationary and non-stationary data. On one hand, fuzzy scheme takes into account discrete and continuous classes through the modeling of hidden data imprecision and on the other hand, Markovian Bayesian scheme models the uncertainty on the observed data. A non-stationar...

In this project1, we first study the Gaussian-based hidden Markov random field (HMRF) model and its expectationmaximization (EM) algorithm. Then we generalize it to Gaussian mixture model-based hidden Markov random field. The algorithm is implemented in MATLAB. We also apply this algorithm to color image segmentation problems and 3D volume segmentation problems.

Image interpretation consists of interleaving the low-level task of image segmentation and the high-level task of interpretation. The idea being that the interpretation block guides the segmentation block which in turn helps the interpretation block in better interpretation. In this paper, we develop a joint segmentation and image interpretation scheme using the notion of joint hidden Markov mo...

In this paper we are dealing with audio segmentation. The audio tracks are sampled in short sequences which are classified into several classes. Every sequence can then be further analysed depending on the class it belongs to. We first describe simple techniques for segmentation in two or three classes. These methods rely on amplitude, spectral or cepstral analysis, and classical hidden markov ...

We present the design and development of a Hidden Markov Model for the division of news broadcasts into story segments. Model topology, and the textual features used, are discussed, together with the non-parametric estimation techniques that were employed for obtaining estimates for both transition and observation probabilities. Visualization methods developed for the analysis of system perform...