Unsupervised segmentation of Markov random fields corrupted by nonstationary noise
| Autor: | Amar Aissani, Wojciech Pieczynski, Ahmed Habbouchi, Mohamed El Yazid Boudaren |
|---|---|
| Přispěvatelé: | Ecole Militaire Polytechnique [Alger] (EMP), Ministère de l'Enseignement Supérieur et de la Recherche Scientifique [Algérie] (MESRS)-Ministère de la Défense Nationale [Algérie], Communications, Images et Traitement de l'Information (CITI), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Université des Sciences et de la Technologie Houari Boumediene [Alger] (USTHB), Traitement de l'Information Pour Images et Communications (TIPIC-SAMOVAR), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0211 other engineering and technologies
Markov process 02 engineering and technology Markov model Triplet Markov fields symbols.namesake Hidden Markov fields 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Hidden Markov model Unsupervised segmentation 021101 geological & geomatics engineering Mathematics Markov random field Markov chain business.industry Applied Mathematics Variable-order Markov model Pattern recognition Nonstationary noise Signal Processing symbols 020201 artificial intelligence & image processing Markov property Hidden semi-Markov model Artificial intelligence business [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | IEEE Signal Processing Letters IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2016, 23 (11), pp.1607-1611. ⟨10.1109/LSP.2016.2609887⟩ |
| ISSN: | 1070-9908 |
| DOI: | 10.1109/LSP.2016.2609887⟩ |
| Popis: | 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 $p(y|x)$ . While it is commonly established that the prior distribution is given by a Markov random field, the noise distribution is usually given through a set of Gaussian densities; one per each label. Hence, observed pixels belonging to the same class are assumed to be generated by the same Gaussian density. Such assumption turns out, however, to be too restrictive in some situations. For instance, due to light conditions, pixels belonging to a same label may present quite different visual aspects. In this letter, we overcome this drawback by considering an auxiliary field $U$ in accordance with the triplet Markov field formalism. Experimental results on simulated and real images demonstrate the interest of the proposed model with respect to the common hidden Markov fields. |
| Databáze: | OpenAIRE |
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