Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parameter estimation in hidden fuzzy Markov random fields and image segmentation
Graphical Models and Image Processing
Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
SAR image segmentation based on mixture context and wavelet hidden-class-label Markov random field
Computers & Mathematics with Applications
Markov Random Field Modeling in Image Analysis
Markov Random Field Modeling in Image Analysis
Unsupervised image segmentation using triplet Markov fields
Computer Vision and Image Understanding
Multiband segmentation based on a hierarchical Markov model
Pattern Recognition
Pattern Recognition Letters
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Quantitative comparison of the performance of SAR segmentation algorithms
IEEE Transactions on Image Processing
Sonar image segmentation using an unsupervised hierarchical MRF model
IEEE Transactions on Image Processing
Multiscale image segmentation using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
A tree-structured Markov random field model for Bayesian image segmentation
IEEE Transactions on Image Processing
Directional multiscale modeling of images using the contourlet transform
IEEE Transactions on Image Processing
The Nonsubsampled Contourlet Transform: Theory, Design, and Applications
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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Triplet Markov fields (TMFs) model recently proposed is to deal with nonstationary image segmentation and has achieved promising results. In this paper, we propose a multiscale and multidirection TMF model for nonstationary synthetic aperture radar (SAR) image multiclass segmentation in nonsubsampled contourlet transform (NSCT) domain, named as NSCT-TMF model. NSCT-TMF model is capable of capturing the contextual information of image content in the spatial and scale spaces effectively by the construction of multiscale energy functions. And the derived multiscale and multidirection likelihoods of NSCT-TMF model can capture the dependencies of NSCT coefficients across scale and directions. In this way, the proposed model is able to achieve multiscale information fusion in terms of image configuration and features in underlying labeling process. Experimental results demonstrate that due to the effective propagation of the contextual information, NSCT-TMF model turns out to be more robust against speckle noise and improves the performance of nonstationary SAR image segmentation.