Pattern recognition in trunk images based on co-occurrence descriptors: A proposal applied to tree species identification
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/LA-CCI.2015.7435983 http://hdl.handle.net/11449/168673 |
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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Repositório Institucional da UNESP |
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Pattern recognition in trunk images based on co-occurrence descriptors: A proposal applied to tree species identificationBrazilian forestCo-occurrence descriptorsDecision TreeImage processingTrunk imagesTree 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.Environmental Sciences Graduate Program UNESP - Univ. Estadual PaulistaEnvironmental Sciences Graduate Program UNESP - Univ. Estadual PaulistaUniversidade Estadual Paulista (Unesp)Bressane, Adriano [UNESP]Roveda, Jos� Arnaldo Frutuoso [UNESP]Martins, Antonio Cesar Germano [UNESP]2018-12-11T16:42:28Z2018-12-11T16:42:28Z2016-03-17info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/LA-CCI.2015.74359832015 Latin-America Congress on Computational Intelligence, LA-CCI 2015.http://hdl.handle.net/11449/16867310.1109/LA-CCI.2015.74359832-s2.0-8496962594189596375594042060000-0002-4899-3983Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2015 Latin-America Congress on Computational Intelligence, LA-CCI 2015info:eu-repo/semantics/openAccess2021-10-23T21:47:02Zoai:repositorio.unesp.br:11449/168673Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:43:37.701755Repositório 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] Brazilian forest Co-occurrence descriptors Decision Tree Image processing Trunk images |
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] Roveda, Jos� Arnaldo Frutuoso [UNESP] Martins, Antonio Cesar Germano [UNESP] |
author_role |
author |
author2 |
Roveda, Jos� Arnaldo Frutuoso [UNESP] Martins, Antonio Cesar Germano [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Bressane, Adriano [UNESP] Roveda, Jos� Arnaldo Frutuoso [UNESP] Martins, Antonio Cesar Germano [UNESP] |
dc.subject.por.fl_str_mv |
Brazilian forest Co-occurrence descriptors Decision Tree Image processing Trunk images |
topic |
Brazilian forest Co-occurrence descriptors Decision Tree Image processing Trunk images |
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 |
2016 |
dc.date.none.fl_str_mv |
2016-03-17 2018-12-11T16:42:28Z 2018-12-11T16:42:28Z |
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 |
http://dx.doi.org/10.1109/LA-CCI.2015.7435983 2015 Latin-America Congress on Computational Intelligence, LA-CCI 2015. http://hdl.handle.net/11449/168673 10.1109/LA-CCI.2015.7435983 2-s2.0-84969625941 8959637559404206 0000-0002-4899-3983 |
url |
http://dx.doi.org/10.1109/LA-CCI.2015.7435983 http://hdl.handle.net/11449/168673 |
identifier_str_mv |
2015 Latin-America Congress on Computational Intelligence, LA-CCI 2015. 10.1109/LA-CCI.2015.7435983 2-s2.0-84969625941 8959637559404206 0000-0002-4899-3983 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2015 Latin-America Congress on Computational Intelligence, LA-CCI 2015 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus 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 |
|
_version_ |
1808128691858833408 |