A probabilistic framework for image segmentation
| Autor: | S. Wesolkowski, Paul Fieguth |
|---|---|
| Rok vydání: | 2004 |
| Předmět: |
Random field
Markov random field business.industry Segmentation-based object categorization ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Probabilistic logic Scale-space segmentation Pattern recognition Image segmentation Image texture Region growing Computer Science::Computer Vision and Pattern Recognition Artificial intelligence business Mathematics |
| Zdroj: | ICIP (2) |
| Popis: | 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 Gibbs/Markov 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 Gibbs/Markov 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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