Pattern Recognition via PCNN and Tsallis Entropy

In this paper a novel feature extraction method for image processing via PCNN and Tsallis entropy is presented. We describe the mathematical model of the PCNN and the basic concept of Tsallis entropy in order to find a recognition method for isolated objects. Experiments show that the novel feature...

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Detalles Bibliográficos
Autores principales: Zhang, YuDong, Wu, LeNan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2008
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3787458/
https://www.ncbi.nlm.nih.gov/pubmed/27873942
http://dx.doi.org/10.3390/s8117518
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author Zhang, YuDong
Wu, LeNan
author_facet Zhang, YuDong
Wu, LeNan
author_sort Zhang, YuDong
collection PubMed
description In this paper a novel feature extraction method for image processing via PCNN and Tsallis entropy is presented. We describe the mathematical model of the PCNN and the basic concept of Tsallis entropy in order to find a recognition method for isolated objects. Experiments show that the novel feature is translation and scale independent, while rotation independence is a bit weak at diagonal angles of 45° and 135°. Parameters of the application on face recognition are acquired by bacterial chemotaxis optimization (BCO), and the highest classification rate is 72.5%, which demonstrates its acceptable performance and potential value.
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spelling pubmed-37874582013-10-17 Pattern Recognition via PCNN and Tsallis Entropy Zhang, YuDong Wu, LeNan Sensors (Basel) Article In this paper a novel feature extraction method for image processing via PCNN and Tsallis entropy is presented. We describe the mathematical model of the PCNN and the basic concept of Tsallis entropy in order to find a recognition method for isolated objects. Experiments show that the novel feature is translation and scale independent, while rotation independence is a bit weak at diagonal angles of 45° and 135°. Parameters of the application on face recognition are acquired by bacterial chemotaxis optimization (BCO), and the highest classification rate is 72.5%, which demonstrates its acceptable performance and potential value. Molecular Diversity Preservation International (MDPI) 2008-11-25 /pmc/articles/PMC3787458/ /pubmed/27873942 http://dx.doi.org/10.3390/s8117518 Text en © 2008 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Zhang, YuDong
Wu, LeNan
Pattern Recognition via PCNN and Tsallis Entropy
title Pattern Recognition via PCNN and Tsallis Entropy
title_full Pattern Recognition via PCNN and Tsallis Entropy
title_fullStr Pattern Recognition via PCNN and Tsallis Entropy
title_full_unstemmed Pattern Recognition via PCNN and Tsallis Entropy
title_short Pattern Recognition via PCNN and Tsallis Entropy
title_sort pattern recognition via pcnn and tsallis entropy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3787458/
https://www.ncbi.nlm.nih.gov/pubmed/27873942
http://dx.doi.org/10.3390/s8117518
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AT wulenan patternrecognitionviapcnnandtsallisentropy

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