Name
Abstract | ||
|---|---|---|
Hidden Markov fields have been widely used in image processing thanks to their ability to characterize spatial information. In such models, the process of interest X is hidden and is to be estimated from an observable process Y . One common way to achieve the associated inference tasks is to define, on one hand, the prior distribution p(x); and on the other hand, the noise distribution p(y/x). Whi... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LSP.2016.2609887 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Hidden Markov models,Markov processes,Image segmentation,Bayes methods,Context,Adaptation models,Image restoration | Markov process,Pattern recognition,Markov property,Markov random field,Markov model,Markov chain,Artificial intelligence,Variable-order Markov model,Hidden Markov model,Mathematics,Hidden semi-Markov model | Journal |
Volume | Issue | ISSN |
23 | 11 | 1070-9908 |
Citations | PageRank | References |
1 | 0.35 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmed Habbouchi | 1 | 1 | 0.69 |
| Mohamed El Yazid Boudaren | 2 | 31 | 5.93 |
| Amar Aissani | 3 | 139 | 12.49 |
| Wojciech Pieczynski | 4 | 37 | 8.74 |