Human motion segmentation using active shape models
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | |
Tipo de documento: | Livro |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://repositorio-aberto.up.pt/handle/10216/69057 |
Resumo: | Human motion analysis in images is thoroughly related with the developmentof computational techniques capable of automatically identify, track andanalyze relevant structures of the body. In fact, in any system designed for humanmotion analysis from image sequences, the first processing step concerns the identificationof the structures to be analyzed in each of the sequence images, beingthis step commonly referred as image segmentation. Here, a widely used database,the CASIA Gait Database, is used to build Point Distribution Models (PDMs) ofthe human silhouette, including specific joints. The training image dataset used includes14 subjects walking in four different directions, and each shape of the trainingset was represented by a set of labeled landmark points. The contours of thesilhouettes were obtained with the purpose of automatically extract 100 silhouettepoints together with additional 13 anatomic joint points, such as elbows, knees andfeet, to be used as landmarks. In order to obtain the mean shape of the silhouetteas well as its admissible shape variations PDMs for each direction were built. ThePDMs built were finally used in the construction of Active Shape Models (ASMs),which combine the shape model with grey level profiles, with the purpose of furthersegment the modeled silhouettes in new images. The referred technique is aniterative optimization scheme for PDMs allowing initial estimates of pose, scaleand shape of an object to be refined in a new image. The experiments conductedusing this segmentation technique has revealed very encouraging results. |
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7160 |
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Human motion segmentation using active shape modelsCiências Tecnológicas, Ciências da engenharia e tecnologiasTechnological sciences, Engineering and technologyHuman motion analysis in images is thoroughly related with the developmentof computational techniques capable of automatically identify, track andanalyze relevant structures of the body. In fact, in any system designed for humanmotion analysis from image sequences, the first processing step concerns the identificationof the structures to be analyzed in each of the sequence images, beingthis step commonly referred as image segmentation. Here, a widely used database,the CASIA Gait Database, is used to build Point Distribution Models (PDMs) ofthe human silhouette, including specific joints. The training image dataset used includes14 subjects walking in four different directions, and each shape of the trainingset was represented by a set of labeled landmark points. The contours of thesilhouettes were obtained with the purpose of automatically extract 100 silhouettepoints together with additional 13 anatomic joint points, such as elbows, knees andfeet, to be used as landmarks. In order to obtain the mean shape of the silhouetteas well as its admissible shape variations PDMs for each direction were built. ThePDMs built were finally used in the construction of Active Shape Models (ASMs),which combine the shape model with grey level profiles, with the purpose of furthersegment the modeled silhouettes in new images. The referred technique is aniterative optimization scheme for PDMs allowing initial estimates of pose, scaleand shape of an object to be refined in a new image. The experiments conductedusing this segmentation technique has revealed very encouraging results.20132013-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://repositorio-aberto.up.pt/handle/10216/69057engMaria João M. VasconcelosJoão Manuel R. S. Tavaresinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T13:04:55Zoai:repositorio-aberto.up.pt:10216/69057Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:33:16.095089Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Human motion segmentation using active shape models |
title |
Human motion segmentation using active shape models |
spellingShingle |
Human motion segmentation using active shape models Maria João M. Vasconcelos Ciências Tecnológicas, Ciências da engenharia e tecnologias Technological sciences, Engineering and technology |
title_short |
Human motion segmentation using active shape models |
title_full |
Human motion segmentation using active shape models |
title_fullStr |
Human motion segmentation using active shape models |
title_full_unstemmed |
Human motion segmentation using active shape models |
title_sort |
Human motion segmentation using active shape models |
author |
Maria João M. Vasconcelos |
author_facet |
Maria João M. Vasconcelos João Manuel R. S. Tavares |
author_role |
author |
author2 |
João Manuel R. S. Tavares |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Maria João M. Vasconcelos João Manuel R. S. Tavares |
dc.subject.por.fl_str_mv |
Ciências Tecnológicas, Ciências da engenharia e tecnologias Technological sciences, Engineering and technology |
topic |
Ciências Tecnológicas, Ciências da engenharia e tecnologias Technological sciences, Engineering and technology |
description |
Human motion analysis in images is thoroughly related with the developmentof computational techniques capable of automatically identify, track andanalyze relevant structures of the body. In fact, in any system designed for humanmotion analysis from image sequences, the first processing step concerns the identificationof the structures to be analyzed in each of the sequence images, beingthis step commonly referred as image segmentation. Here, a widely used database,the CASIA Gait Database, is used to build Point Distribution Models (PDMs) ofthe human silhouette, including specific joints. The training image dataset used includes14 subjects walking in four different directions, and each shape of the trainingset was represented by a set of labeled landmark points. The contours of thesilhouettes were obtained with the purpose of automatically extract 100 silhouettepoints together with additional 13 anatomic joint points, such as elbows, knees andfeet, to be used as landmarks. In order to obtain the mean shape of the silhouetteas well as its admissible shape variations PDMs for each direction were built. ThePDMs built were finally used in the construction of Active Shape Models (ASMs),which combine the shape model with grey level profiles, with the purpose of furthersegment the modeled silhouettes in new images. The referred technique is aniterative optimization scheme for PDMs allowing initial estimates of pose, scaleand shape of an object to be refined in a new image. The experiments conductedusing this segmentation technique has revealed very encouraging results. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013 2013-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/book |
format |
book |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://repositorio-aberto.up.pt/handle/10216/69057 |
url |
https://repositorio-aberto.up.pt/handle/10216/69057 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799135644040036352 |