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A novel approach to unsupervised stochastic model-based image segmentation is presented and the problems of
parameter estimation and image segmentation are formulated as Bayesian learning. In order to draw samples corresponding
to di€ erent classes, a global competition strategy is adopted for label commitment based on the ``powervalue
(PV) associated with each sample (or site). The smaller the value, the more powerful the sample to compete.
Parameter estimation and image segmentation are d in the same process. Bayesian modeling of images by
Markov random ® elds (MRFs) makes it easy to represent the power of each site for competition. The new procedure to
unsupervised image segmentation is performed on synthetic and real images to show its success. ó 2000 Elsevier
Science B.V. All rights reserved-A novel approach to unsupervised stochastic model-based image segmentation is presented and the problems of
parameter estimation and image segmentation are formulated as Bayesian learning. In order to draw samples corresponding
to di€ erent classes, a global competition strategy is adopted for label commitment based on the ``powervalue
(PV) associated with each sample (or site). The smaller the value, the more powerful the sample to compete.
Parameter estimation and image segmentation are d in the same process. Bayesian modeling of images by
Markov random ® elds (MRFs) makes it easy to represent the power of each site for competition. The new procedure to
unsupervised image segmentation is performed on synthetic and real images to show its success. ó 2000 Elsevier
Science B.V. All rights reserved
parameter estimation and image segmentation are formulated as Bayesian learning. In order to draw samples corresponding
to di€ erent classes, a global competition strategy is adopted for label commitment based on the ``powervalue
(PV) associated with each sample (or site). The smaller the value, the more powerful the sample to compete.
Parameter estimation and image segmentation are d in the same process. Bayesian modeling of images by
Markov random ® elds (MRFs) makes it easy to represent the power of each site for competition. The new procedure to
unsupervised image segmentation is performed on synthetic and real images to show its success. ó 2000 Elsevier
Science B.V. All rights reserved-A novel approach to unsupervised stochastic model-based image segmentation is presented and the problems of
parameter estimation and image segmentation are formulated as Bayesian learning. In order to draw samples corresponding
to di€ erent classes, a global competition strategy is adopted for label commitment based on the ``powervalue
(PV) associated with each sample (or site). The smaller the value, the more powerful the sample to compete.
Parameter estimation and image segmentation are d in the same process. Bayesian modeling of images by
Markov random ® elds (MRFs) makes it easy to represent the power of each site for competition. The new procedure to
unsupervised image segmentation is performed on synthetic and real images to show its success. ó 2000 Elsevier
Science B.V. All rights reserved
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