Markov random field modeling in image analysis
Markov random field modeling in image analysis
Flexible nonlinear contextual classification
Pattern Recognition Letters - Special issue: Pattern recognition for remote sensing (PRRS 2002)
A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model
IEEE Transactions on Image Processing
Image classification using spectral and spatial information based on MRF models
IEEE Transactions on Image Processing
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This paper addresses the problem of maximum pseudo-likelihood estimation of the non-homogeneous Potts image model parameters using higher-order non-causal neighborhood systems in a computationally efficient way. The motivation is the development of a new methodology for contextual classification that uses combination of sub-optimal MRF algorithms for multispectral image classification, which requires accurate parameters estimation. Our objective is to make multispectral image contextual classification fully operational without human intervention. The results show that the method is consistent with real data and in the presence of random noise.