A probabilistic framework for image segmentation
نویسندگان
چکیده
A new probabilistic image segmentation model based on hypothesis testing and Gibbs Random Fields is introduced. First, a probabilistic difference measure derived from a set of hypothesis tests is introduced. Next, a GibbsMarkov Random Field model endowed with the new measure is then applied to the image segmentation problem to determine the segmented image directly through energy minimization. The Gibbshlarkov Random Fields approach permits us to construct a rigorous computational framework where local and regional constraints can be globally optimized. Results on grayscale and color images are encouraging.
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