Classificação automática de padrões projetados por um sistema de luz estruturada
Autor(a) principal: | |
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Data de Publicação: | 2005 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://ojs.c3sl.ufpr.br/ojs/index.php/bcg/article/view/1547 http://hdl.handle.net/11449/68101 |
Resumo: | One of the main problems in Computer Vision and Close Range Digital Photogrammetry is 3D reconstruction. 3D reconstruction with structured light is one of the existing techniques and which still has several problems, one of them the identification or classification of the projected targets. Approaching this problem is the goal of this paper. An area based method called template matching was used for target classification. This method performs detection of area similarity by correlation, which measures the similarity between the reference and search windows, using a suitable correlation function. In this paper the modified cross covariance function was used, which presented the best results. A strategy was developed for adaptative resampling of the patterns, which solved the problem of deformation of the targets due to object surface inclination. Experiments with simulated and real data were performed in order to assess the efficiency of the proposed methodology for target detection. The results showed that the proposed classification strategy works properly, identifying 98% of targets in plane surfaces and 93% in oblique surfaces. |
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Repositório Institucional da UNESP |
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Classificação automática de padrões projetados por um sistema de luz estruturadaAutomatic classification of targets projected with a structured light systemcomputer visiondigital photogrammetryimage classificationOne of the main problems in Computer Vision and Close Range Digital Photogrammetry is 3D reconstruction. 3D reconstruction with structured light is one of the existing techniques and which still has several problems, one of them the identification or classification of the projected targets. Approaching this problem is the goal of this paper. An area based method called template matching was used for target classification. This method performs detection of area similarity by correlation, which measures the similarity between the reference and search windows, using a suitable correlation function. In this paper the modified cross covariance function was used, which presented the best results. A strategy was developed for adaptative resampling of the patterns, which solved the problem of deformation of the targets due to object surface inclination. Experiments with simulated and real data were performed in order to assess the efficiency of the proposed methodology for target detection. The results showed that the proposed classification strategy works properly, identifying 98% of targets in plane surfaces and 93% in oblique surfaces.Universidade Estadual Paulista Faculdade de Ciências e Tecnologia Departamento de Cartografia, Rua Roberto Simonsen, 305, 19060-900 Presidente PrudenteUniversidade Estadual Paulista Faculdade de Ciências e Tecnologia Departamento de Cartografia, Rua Roberto Simonsen, 305, 19060-900 Presidente PrudenteUniversidade Estadual Paulista (Unesp)de Carvalho Kokubum, Christiane Nogueira [UNESP]Tommaselli, Antonio Maria Garcia [UNESP]Reiss, Mário Luiz Lopes [UNESP]2014-05-27T11:21:15Z2014-05-27T11:21:15Z2005-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article89-116application/pdfhttp://ojs.c3sl.ufpr.br/ojs/index.php/bcg/article/view/1547Boletim de Ciencias Geodesicas, v. 11, n. 1, p. 89-116, 2005.1413-4853http://hdl.handle.net/11449/681012-s2.0-273444561882-s2.0-27344456188.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPporBoletim de Ciências Geodésicas0,188info:eu-repo/semantics/openAccess2024-06-18T15:01:10Zoai:repositorio.unesp.br:11449/68101Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:43:37.708250Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Classificação automática de padrões projetados por um sistema de luz estruturada Automatic classification of targets projected with a structured light system |
title |
Classificação automática de padrões projetados por um sistema de luz estruturada |
spellingShingle |
Classificação automática de padrões projetados por um sistema de luz estruturada de Carvalho Kokubum, Christiane Nogueira [UNESP] computer vision digital photogrammetry image classification |
title_short |
Classificação automática de padrões projetados por um sistema de luz estruturada |
title_full |
Classificação automática de padrões projetados por um sistema de luz estruturada |
title_fullStr |
Classificação automática de padrões projetados por um sistema de luz estruturada |
title_full_unstemmed |
Classificação automática de padrões projetados por um sistema de luz estruturada |
title_sort |
Classificação automática de padrões projetados por um sistema de luz estruturada |
author |
de Carvalho Kokubum, Christiane Nogueira [UNESP] |
author_facet |
de Carvalho Kokubum, Christiane Nogueira [UNESP] Tommaselli, Antonio Maria Garcia [UNESP] Reiss, Mário Luiz Lopes [UNESP] |
author_role |
author |
author2 |
Tommaselli, Antonio Maria Garcia [UNESP] Reiss, Mário Luiz Lopes [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
de Carvalho Kokubum, Christiane Nogueira [UNESP] Tommaselli, Antonio Maria Garcia [UNESP] Reiss, Mário Luiz Lopes [UNESP] |
dc.subject.por.fl_str_mv |
computer vision digital photogrammetry image classification |
topic |
computer vision digital photogrammetry image classification |
description |
One of the main problems in Computer Vision and Close Range Digital Photogrammetry is 3D reconstruction. 3D reconstruction with structured light is one of the existing techniques and which still has several problems, one of them the identification or classification of the projected targets. Approaching this problem is the goal of this paper. An area based method called template matching was used for target classification. This method performs detection of area similarity by correlation, which measures the similarity between the reference and search windows, using a suitable correlation function. In this paper the modified cross covariance function was used, which presented the best results. A strategy was developed for adaptative resampling of the patterns, which solved the problem of deformation of the targets due to object surface inclination. Experiments with simulated and real data were performed in order to assess the efficiency of the proposed methodology for target detection. The results showed that the proposed classification strategy works properly, identifying 98% of targets in plane surfaces and 93% in oblique surfaces. |
publishDate |
2005 |
dc.date.none.fl_str_mv |
2005-01-01 2014-05-27T11:21:15Z 2014-05-27T11:21:15Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://ojs.c3sl.ufpr.br/ojs/index.php/bcg/article/view/1547 Boletim de Ciencias Geodesicas, v. 11, n. 1, p. 89-116, 2005. 1413-4853 http://hdl.handle.net/11449/68101 2-s2.0-27344456188 2-s2.0-27344456188.pdf |
url |
http://ojs.c3sl.ufpr.br/ojs/index.php/bcg/article/view/1547 http://hdl.handle.net/11449/68101 |
identifier_str_mv |
Boletim de Ciencias Geodesicas, v. 11, n. 1, p. 89-116, 2005. 1413-4853 2-s2.0-27344456188 2-s2.0-27344456188.pdf |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
Boletim de Ciências Geodésicas 0,188 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
89-116 application/pdf |
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_ |
1808128554656858112 |