Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/ICIP.2003.1246714 | ICIP |
Keywords | Field | DocType |
image segmentation,probability,Gibbs random field,Markov random field,energy minimization,hypothesis testing,probabilistic image segmentation | Computer vision,Scale-space segmentation,Random field,Pattern recognition,Computer science,Image texture,Markov random field,Segmentation-based object categorization,Image segmentation,Region growing,Artificial intelligence,Probabilistic logic | Conference |
Volume | Citations | PageRank |
2 | 0 | 0.34 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Slawo Wesolkowski | 1 | 36 | 5.55 |
| Paul W. Fieguth | 2 | 612 | 54.17 |