http://www.cnr.it/ontology/cnr/individuo/prodotto/ID198055
High-Speed Image Processing Algorithms for Real-Time Detection of MARFEs on JET (Articolo in rivista)
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- High-Speed Image Processing Algorithms for Real-Time Detection of MARFEs on JET (Articolo in rivista) (literal)
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- 2012-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1109/TPS.2012.2223235 (literal)
- Alternative label
Márcio Portes de Albuquerque; Marcelo Portes de Albuquerque; Germano T. Chacon; E. L. de Faria; Andrea Murari; JET-EFDA Contributors (2012)
High-Speed Image Processing Algorithms for Real-Time Detection of MARFEs on JET
in IEEE transactions on plasma science; IEEE-Institute Of Electrical And Electronics Engineers Inc., Piscataway (Stati Uniti d'America)
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- Márcio Portes de Albuquerque; Marcelo Portes de Albuquerque; Germano T. Chacon; E. L. de Faria; Andrea Murari; JET-EFDA Contributors (literal)
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- Part: 3
La rivista è pubblicata anche online con ISSN 1939-9375. (literal)
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- 1 Centro Brasileiro de Pesquisas Físicas (CBPF), 22299-180 Rio de Janeiro-RJ, Brazil;
2 Consorzio RFX- Associazione EURATOM-ENEA per la Fusione, 35127 Padova, Italy;
JET-EFDA Contributors: JET-EFDA, Culham Science Centre, Abingdon OX14 3DB, U.K. (See the Appendix of F. Romanelli et al., Proceedings of the 23rd IAEA Fusion Energy Conference 2010, Daejeon, Korea.)
(Márcio Portes de Albuquerque 1; Marcelo Portes de Albuquerque1; Germano T. Chacon 1; E. L. de Faria 1; Andrea Murari; JET-EFDA Contributors) (literal)
- Titolo
- High-Speed Image Processing Algorithms for Real-Time Detection of MARFEs on JET (literal)
- Abstract
- This paper presents a high-speed image processing algorithm applied to multifaceted asymmetric radiation from the edge detection on Joint European Torus. The algorithm has been developed in C/C++ language using the OpenCV library for image processing and the LibSVM library for pattern classification and has been tested on a dedicated Intel 64-bit Linux computing platform. The code implemented achieves at the same time high accuracy and good run-time performance. The final version presents a correct detection rate of 93.3% and an average image processing rate of 650 frames per second. A complete analysis of each image processing module is presented to illustrate the overall characteristics and performance of the algorithm. (literal)
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