Title :
Fusion of Hidden Markov Random Field Models and Its Bayesian Estimation
Author :
Destrempes, Francois ; Angers, Jean-Francois ; Mignotte, Max
Author_Institution :
DIRO, Univ. de Montreal, Que.
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
Keywords :
Bayes methods; Gaussian processes; hidden Markov models; image colour analysis; image segmentation; parameter estimation; sensor fusion; Bayesian estimation; Gaussian kernels; Julesz ensemble textons; Markov hyper-parameter; asymptotic convergence; color fusion; exploration-selection-estimation procedure; hidden Markov random field data-fusion model; mixture proportions; natural image segmentation; parameter estimation; region labels; Bayesian methods; Biomedical imaging; Convergence; Entropy; Hidden Markov models; Image processing; Image segmentation; Kernel; Monte Carlo methods; Parameter estimation; Bayesian estimation; Exploration/Selection algorithm; Exploration/Selection/Estimation procedure; Julesz ensembles; Markov Chain Monte Carlo (MCMC) algorithm; color and texture segmentation; fusion of hidden Markov random field models;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2006.877522
