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

1-s2.0-S0167865599001373-main.pdf

• We are an exchange download platform that only provides communication channels. The downloaded content comes from the internet. Except for download issues, please Google on your own.
• The downloaded content is provided for members to upload. If it unintentionally infringes on your copyright, please contact us.
• Please use Winrar for decompression tools
• If download fail, Try it againg or Feedback to us.
• If downloaded content did not match the introduction, Feedback to us，Confirm and will be refund.
• Before downloading, you can inquire through the uploaded person information

Nothing．