title: Fusion of Hidden Markov Random Field models and its Bayesian estimation.
abstract: In this paper, we present a Hidden Markov Random Field (HMRF) data-fusion model. The proposed model is applied to the segmentation of natural images based on the fusion of colors and textons into Julesz ensembles. The corresponding Exploration/ Selection/Estimation (ESE) procedure for the estimation of the parameters is presented. This method achieves the estimation of the parameters of the Gaussian kernels, the mixture proportions, the region labels, the number of regions, and the Markov hyper-parameter. Meanwhile, we present a new proof of the asymptotic convergence of the ESE procedure, based on original finite time bounds for the rate of convergence.
authors.affiliation: Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, Pavilion André-Aisenstadt, Canada. destremp@iro.umontreal.ca
authors.forename: François
authors.initials: F
authors.lastname: Destrempes
authors.forename: Jean-François
authors.initials: JF
authors.lastname: Angers
authors.forename: Max
authors.initials: M
authors.lastname: Mignotte
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D013382:Subtraction Technique