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A probabilistic framework for image segmentation

TitleA probabilistic framework for image segmentation
Publication TypeConference Paper
Year of Publication2003
AuthorsWesolkowski, S., and P. Fieguth
Conference NameIEEE International Conference on Image Processing
Keywordsenergy minimization, Gibbs random field, hypothesis testing, image segmentation, Markov random field, probabilistic image segmentation, probability

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.