  In this work we propose a Bayesian framework for fully automated image fusion
and their joint segmentation. More specifically, we consider the case where we
have observed images of the same object through different image processes or
through different spectral bands. The objective of this work is then to propose
a coherent approach to combine these data sets and obtain a segmented image
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 with
common segmentation, or equivalently, with common hidden classification label
variables which are modeled by the Potts Markov Random Field. We propose an
appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method
and show some simulation results and applications.
