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: | 2015 |
Outros Autores: | , , , , |
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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Repositório Institucional da UNESP |
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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 |
dc.format.none.fl_str_mv |
6 |
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 |
|
_version_ |
1808128487697940480 |