A probabilistic framework for image segmentation
Slawomir Wesolkowski, Paul Fieguth · 2004
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.