Dempster-Shafer fusion of evidential pairwise Markov chains
| Autor: | Mohamed El Yazid Boudaren, Wojciech Pieczynski |
|---|---|
| Přispěvatelé: | Ecole Militaire Polytechnique [Alger] (EMP), Ministère de l'Enseignement Supérieur et de la Recherche Scientifique [Algérie] (MESRS)-Ministère de la Défense Nationale [Algérie], 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), Communications, Images et Traitement de l'Information (CITI), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Markov process
02 engineering and technology Computer Science::Artificial Intelligence Markov model Machine learning computer.software_genre symbols.namesake Theory of evidence Artificial Intelligence Dempster–Shafer theory 0202 electrical engineering electronic engineering information engineering Triplet Markov chains Hidden Markov model Mathematics Markov chain business.industry Applied Mathematics Variable-order Markov model Maximum-entropy Markov model 020206 networking & telecommunications Hidden Markov chains Dempster-Shafer fusion Computational Theory and Mathematics Control and Systems Engineering symbols 020201 artificial intelligence & image processing Markov property Artificial intelligence business computer [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | IEEE Transactions on Fuzzy Systems IEEE Transactions on Fuzzy Systems, Institute of Electrical and Electronics Engineers, 2016, 24 (6), pp.1598-1610. ⟨10.1109/TFUZZ.2016.2543750⟩ |
| ISSN: | 1063-6706 |
| DOI: | 10.1109/TFUZZ.2016.2543750⟩ |
| Popis: | International audience; Hidden Markov models have been extended in many directions, leading to pairwise Markov models, triplet Markov models, or discriminative random fields, all of which have been successfully applied in many fields covering signal and image processing. The Dempster-Shafer theory of evidence has also shown its interest in a wide range of situations involving reasoning under uncertainty and/or information fusion. There are, however, only few works dealing with both of these modeling tools simultaneously. The aim of this paper, which falls under this category of works, is to propose a general evidential Markov model offering wide modeling and processing possibilities regarding information imprecision, sensor unreliability and data fusion. The main interest of the proposed model relies in the possibility of achieving, easily, the Dempster-Shafer fusion without destroying the Markovianity |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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