  In this work we propose a Bayesian framework for data fusion of multivariate
signals which arises in imaging systems. More specifically, we consider the
case where we have observed two images of the same object through two different
imaging processes. The objective of this work is then to propose a coherent
approach to combine these data sets to obtain a segmented image which can be
considered as the fusion result of these two images. 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 is
modeled by the Potts Markov Random Field. We propose then an appropriate Markov
Chain Monte Carlo (MCMC) algorithm to implement the method and show some
simulation results and applications.
