A probabilistic framework for image segmentation

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.

• Type: Conference
• Year: 2003
• Where: ICIP
• Authors: Slawo Wesolkowski, Paul W. Fieguth
• Keywords: Gibbs Random Fields, ICIP 2003, Image Processing, Image Segmentation Problem, Probabilistic Difference Measure, Probabilistic Image Segmentation, Random Field Model