Pattern recognition in trunk images based on co-occurrence descriptors: a proposal applied to tree species identification

Detalhes bibliográficos
Autor(a) principal: Bressane, Adriano [UNESP]
Data de Publicação: 2015
Outros Autores: Frutuoso Roveda, Jose Arnaldo [UNESP], Germano Martins, Antonio Cesar [UNESP], Vellasco, MMBR, Valdivia, YJT, Lopes, H. S.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/158983
Resumo: Tree species identification is required for many applications. However, current techniques are dependent on the presence of morphological structures such as leaves, which restricts its use in certain situations and seasons. In this context, the use of trunk images can be an alternative. Therefore, the present study developed a pattern recognition based on co-occurrence descriptors, aiming evaluate its performance in the identification of 8 tree species from the Brazilian deciduous native forest, achieving promising results, with precision better than 0.8 for most of them, accuracy equivalent to 0.77 and average area under curve by Receiver Operating Characteristic of 0.88, during the tests with cross-validation sets.
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spelling Pattern recognition in trunk images based on co-occurrence descriptors: a proposal applied to tree species identificationImage processingDecision TreeCo-occurrence descriptorsTrunk imagesBrazilian forestTree species identification is required for many applications. However, current techniques are dependent on the presence of morphological structures such as leaves, which restricts its use in certain situations and seasons. In this context, the use of trunk images can be an alternative. Therefore, the present study developed a pattern recognition based on co-occurrence descriptors, aiming evaluate its performance in the identification of 8 tree species from the Brazilian deciduous native forest, achieving promising results, with precision better than 0.8 for most of them, accuracy equivalent to 0.77 and average area under curve by Receiver Operating Characteristic of 0.88, during the tests with cross-validation sets.UNESP Univ Estadual Paulista, Environm Sci Grad Program, Sorocaba City, SP, BrazilUNESP Univ Estadual Paulista, Environm Sci Grad Program, Sorocaba City, SP, BrazilIeeeUniversidade Estadual Paulista (Unesp)Bressane, Adriano [UNESP]Frutuoso Roveda, Jose Arnaldo [UNESP]Germano Martins, Antonio Cesar [UNESP]Vellasco, MMBRValdivia, YJTLopes, H. S.2018-11-26T15:30:37Z2018-11-26T15:30:37Z2015-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject62015 Latin America Congress On Computational Intelligence (la-cci). New York: Ieee, 6 p., 2015.http://hdl.handle.net/11449/158983WOS:00038039630005589596375594042060000-0002-4899-3983Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2015 Latin America Congress On Computational Intelligence (la-cci)info:eu-repo/semantics/openAccess2021-10-23T21:47:03Zoai:repositorio.unesp.br:11449/158983Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:15:12.927632Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Pattern recognition in trunk images based on co-occurrence descriptors: a proposal applied to tree species identification
title Pattern recognition in trunk images based on co-occurrence descriptors: a proposal applied to tree species identification
spellingShingle Pattern recognition in trunk images based on co-occurrence descriptors: a proposal applied to tree species identification
Bressane, Adriano [UNESP]
Image processing
Decision Tree
Co-occurrence descriptors
Trunk images
Brazilian forest
title_short Pattern recognition in trunk images based on co-occurrence descriptors: a proposal applied to tree species identification
title_full Pattern recognition in trunk images based on co-occurrence descriptors: a proposal applied to tree species identification
title_fullStr Pattern recognition in trunk images based on co-occurrence descriptors: a proposal applied to tree species identification
title_full_unstemmed Pattern recognition in trunk images based on co-occurrence descriptors: a proposal applied to tree species identification
title_sort Pattern recognition in trunk images based on co-occurrence descriptors: a proposal applied to tree species identification
author Bressane, Adriano [UNESP]
author_facet Bressane, Adriano [UNESP]
Frutuoso Roveda, Jose Arnaldo [UNESP]
Germano Martins, Antonio Cesar [UNESP]
Vellasco, MMBR
Valdivia, YJT
Lopes, H. S.
author_role author
author2 Frutuoso Roveda, Jose Arnaldo [UNESP]
Germano Martins, Antonio Cesar [UNESP]
Vellasco, MMBR
Valdivia, YJT
Lopes, H. S.
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Bressane, Adriano [UNESP]
Frutuoso Roveda, Jose Arnaldo [UNESP]
Germano Martins, Antonio Cesar [UNESP]
Vellasco, MMBR
Valdivia, YJT
Lopes, H. S.
dc.subject.por.fl_str_mv Image processing
Decision Tree
Co-occurrence descriptors
Trunk images
Brazilian forest
topic Image processing
Decision Tree
Co-occurrence descriptors
Trunk images
Brazilian forest
description Tree species identification is required for many applications. However, current techniques are dependent on the presence of morphological structures such as leaves, which restricts its use in certain situations and seasons. In this context, the use of trunk images can be an alternative. Therefore, the present study developed a pattern recognition based on co-occurrence descriptors, aiming evaluate its performance in the identification of 8 tree species from the Brazilian deciduous native forest, achieving promising results, with precision better than 0.8 for most of them, accuracy equivalent to 0.77 and average area under curve by Receiver Operating Characteristic of 0.88, during the tests with cross-validation sets.
publishDate 2015
dc.date.none.fl_str_mv 2015-01-01
2018-11-26T15:30:37Z
2018-11-26T15:30:37Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv 2015 Latin America Congress On Computational Intelligence (la-cci). New York: Ieee, 6 p., 2015.
http://hdl.handle.net/11449/158983
WOS:000380396300055
8959637559404206
0000-0002-4899-3983
identifier_str_mv 2015 Latin America Congress On Computational Intelligence (la-cci). New York: Ieee, 6 p., 2015.
WOS:000380396300055
8959637559404206
0000-0002-4899-3983
url http://hdl.handle.net/11449/158983
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2015 Latin America Congress On Computational Intelligence (la-cci)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Ieee
publisher.none.fl_str_mv Ieee
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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