@inproceedings {602,
	title = {A probabilistic framework for image segmentation},
	booktitle = {IEEE International Conference on Image Processing},
	year = {2003},
	abstract = {<p>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.</p>
},
	keywords = {energy minimization, Gibbs random field, hypothesis testing, image segmentation, Markov random field, probabilistic image segmentation, probability},
	doi = {http://dx.doi.org/10.1109/ICIP.2003.1246714},
	author = {S Wesolkowski and P Fieguth}
}
