A Hidden Markov model for Bayesian data fusion of multivariate signals

• DOI: 10.48550/arxiv.physics/0403149
• Publication Date: 2004-01-01
• Authors: Olivier Féron; Ali Mohammad‐Djafari
• Keywords: Physics - Data Analysis, Statistics and Probability; 0202 electrical engineering, electronic engineering, information engineering; FOS: Physical sciences; 02 engineering and technology; Data Analysis, Statistics and Probability (physics.data-an)

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