Image fusion and unsupervised joint segmentation using a HMM and MCMC algorithms

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 joint segmentation which can be considered as the fusion result of these observations. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images where the hidden variables represent the common classification or segmentation labels. These label variables are modeled by the Potts Markov Random Field (PMRF). We propose two particular models for the pixels in each segment (iid. or Markovian) and develop appropriate Markov Chain Monte Carlo (MCMC) algo1 rithms for their implementations. Finally we present some simulation results to show the relative performances of these models and mention the potential applications of the proposed methods in medical imaging and survey and security imaging systems. key words : Data fusion, Segmentation, Markov random field, multi spectral images, HMM, MCMC, Gibbs sampling.