Non-rigid 3D Shape Classification based on Convolutional Neural Networks
Descripción del Articulo
We thank CIENCIACTIVA and their Undergraduate Thesis Program since this research has been funded by them. They have encouraged us to continue this journey and provided us with the required material to pursue our goal.
| Autores: | , |
|---|---|
| Formato: | objeto de conferencia |
| Fecha de Publicación: | 2017 |
| Institución: | Consejo Nacional de Ciencia Tecnología e Innovación |
| Repositorio: | CONCYTEC-Institucional |
| Lenguaje: | inglés |
| OAI Identifier: | oai:repositorio.concytec.gob.pe:20.500.12390/985 |
| Enlace del recurso: | https://hdl.handle.net/20.500.12390/985 https://doi.org/10.1109/LA-CCI.2017.8285693 |
| Nivel de acceso: | acceso abierto |
| Materia: | solid modelling convolution feature extraction feedforward neural nets image classification learning (artificial intelligence) shape recognition https://purl.org/pe-repo/ocde/ford#1.02.00 |
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| dc.title.none.fl_str_mv |
Non-rigid 3D Shape Classification based on Convolutional Neural Networks |
| title |
Non-rigid 3D Shape Classification based on Convolutional Neural Networks |
| spellingShingle |
Non-rigid 3D Shape Classification based on Convolutional Neural Networks Quenaya, JFL solid modelling convolution feature extraction feedforward neural nets image classification learning (artificial intelligence) shape recognition https://purl.org/pe-repo/ocde/ford#1.02.00 |
| title_short |
Non-rigid 3D Shape Classification based on Convolutional Neural Networks |
| title_full |
Non-rigid 3D Shape Classification based on Convolutional Neural Networks |
| title_fullStr |
Non-rigid 3D Shape Classification based on Convolutional Neural Networks |
| title_full_unstemmed |
Non-rigid 3D Shape Classification based on Convolutional Neural Networks |
| title_sort |
Non-rigid 3D Shape Classification based on Convolutional Neural Networks |
| author |
Quenaya, JFL |
| author_facet |
Quenaya, JFL Del Alamo, CJL |
| author_role |
author |
| author2 |
Del Alamo, CJL |
| author2_role |
author |
| dc.contributor.author.fl_str_mv |
Quenaya, JFL Del Alamo, CJL |
| dc.subject.none.fl_str_mv |
solid modelling |
| topic |
solid modelling convolution feature extraction feedforward neural nets image classification learning (artificial intelligence) shape recognition https://purl.org/pe-repo/ocde/ford#1.02.00 |
| dc.subject.es_PE.fl_str_mv |
convolution feature extraction feedforward neural nets image classification learning (artificial intelligence) shape recognition |
| dc.subject.ocde.none.fl_str_mv |
https://purl.org/pe-repo/ocde/ford#1.02.00 |
| description |
We thank CIENCIACTIVA and their Undergraduate Thesis Program since this research has been funded by them. They have encouraged us to continue this journey and provided us with the required material to pursue our goal. |
| publishDate |
2017 |
| dc.date.accessioned.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.available.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.issued.fl_str_mv |
2017 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
| format |
conferenceObject |
| dc.identifier.isbn.none.fl_str_mv |
978-1-5386-3734-0 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12390/985 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1109/LA-CCI.2017.8285693 |
| dc.identifier.isi.none.fl_str_mv |
416178900011 |
| identifier_str_mv |
978-1-5386-3734-0 416178900011 |
| url |
https://hdl.handle.net/20.500.12390/985 https://doi.org/10.1109/LA-CCI.2017.8285693 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
IEEE |
| publisher.none.fl_str_mv |
IEEE |
| dc.source.none.fl_str_mv |
reponame:CONCYTEC-Institucional instname:Consejo Nacional de Ciencia Tecnología e Innovación instacron:CONCYTEC |
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Consejo Nacional de Ciencia Tecnología e Innovación |
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CONCYTEC |
| institution |
CONCYTEC |
| reponame_str |
CONCYTEC-Institucional |
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CONCYTEC-Institucional |
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Repositorio Institucional CONCYTEC |
| repository.mail.fl_str_mv |
repositorio@concytec.gob.pe |
| _version_ |
1839175831710072832 |
| spelling |
Publicationrp02694600rp02695600Quenaya, JFLDel Alamo, CJL2024-05-30T23:13:38Z2024-05-30T23:13:38Z2017978-1-5386-3734-0https://hdl.handle.net/20.500.12390/985https://doi.org/10.1109/LA-CCI.2017.8285693416178900011We thank CIENCIACTIVA and their Undergraduate Thesis Program since this research has been funded by them. They have encouraged us to continue this journey and provided us with the required material to pursue our goal.Over the years, the scientific interest towards 3D models analysis has become more popular. Problems such as classification, retrieval and matching are studied with the idea to offer robust solutions. This paper introduces a 3D object classification method for non-rigid shapes, based on the detection of key points, the use of spectral descriptors and deep learning techniques. We adopt an approach of converting the models into a “spectral image”. By extracting interest points and calculating three types of spectral descriptors (HKS, WKS and GISIF), we generate a three-channel input to a convolutional neural network. This CNN is trained to automatically learn features such as topology of 3D models. The results are evaluated and analyzed using the Non-Rigid Classification Benchmark SHREC 2011. Our proposal shows promising results in classification tasks compared to other methods, and also it is robust under several types of transformations.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengIEEEinfo:eu-repo/semantics/openAccesssolid modellingconvolution-1feature extraction-1feedforward neural nets-1image classification-1learning (artificial intelligence)-1shape recognition-1https://purl.org/pe-repo/ocde/ford#1.02.00-1Non-rigid 3D Shape Classification based on Convolutional Neural Networksinfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#20.500.12390/985oai:repositorio.concytec.gob.pe:20.500.12390/9852024-05-30 15:36:21.474http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="fd8640cd-6361-4c79-98f9-a8ccc51d1766"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Non-rigid 3D Shape Classification based on Convolutional Neural Networks</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017</PublicationDate> <DOI>https://doi.org/10.1109/LA-CCI.2017.8285693</DOI> <ISI-Number>416178900011</ISI-Number> <ISBN>978-1-5386-3734-0</ISBN> <Authors> <Author> <DisplayName>Quenaya, JFL</DisplayName> <Person id="rp02694" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Del Alamo, CJL</DisplayName> <Person id="rp02695" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>IEEE</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>solid modelling</Keyword> <Keyword>convolution</Keyword> <Keyword>feature extraction</Keyword> <Keyword>feedforward neural nets</Keyword> <Keyword>image classification</Keyword> <Keyword>learning (artificial intelligence)</Keyword> <Keyword>shape recognition</Keyword> <Abstract>Over the years, the scientific interest towards 3D models analysis has become more popular. Problems such as classification, retrieval and matching are studied with the idea to offer robust solutions. This paper introduces a 3D object classification method for non-rigid shapes, based on the detection of key points, the use of spectral descriptors and deep learning techniques. We adopt an approach of converting the models into a “spectral image”. By extracting interest points and calculating three types of spectral descriptors (HKS, WKS and GISIF), we generate a three-channel input to a convolutional neural network. This CNN is trained to automatically learn features such as topology of 3D models. The results are evaluated and analyzed using the Non-Rigid Classification Benchmark SHREC 2011. Our proposal shows promising results in classification tasks compared to other methods, and also it is robust under several types of transformations.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1 |
| score |
13.439101 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
