An integrated exploration of heat kernel invariant feature and manifolding technique for 3D object recognition system
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta scientiarum. Technology (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/62608 |
Resumo: | Spectral Graph theory has been utilized frequently in the field of Computer Vision and Pattern Recognition to address challenges in the field of Image Segmentation and Image Classification. In the proposed method, for classification techniques, the associated graph's Eigen values and Eigen vectors of the adjacency matrix or Laplacian matrix created from the images are employed. The Laplacian spectrum and a graph's heat kernel are inextricably linked. Exponentiating the Laplacian eigensystem over time yields the heat kernel, which is the solution to the heat equations. In the proposed technique K-Nearest neighborhood and Delaunay triangulation techniques are used to generate a graph from the 3D model. The graph is then represented into Normalized Laplacian (NL) and Laplacian matrix (L). From each Normalized Laplacian and Laplacian matrix, the feature vectors like Heat Content Invariant and Laplacian Eigen values are extracted. Then, using all of the available clustering algorithms on datasets, the optimum feature vector for clustering is determined. For clustering various manifolding techniques are employed. In the suggested method, the graph heat kernel is constructed using industry-standard objects which are taken from the Engineering bench mark Data set. |
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Acta scientiarum. Technology (Online) |
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An integrated exploration of heat kernel invariant feature and manifolding technique for 3D object recognition systemAn integrated exploration of heat kernel invariant feature and manifolding technique for 3D object recognition systemGraph clustering; laplacian matrix; delaunay triangulation; graph heat kernel; manifolding techniques; structural pattern recognition.Graph clustering; laplacian matrix; delaunay triangulation; graph heat kernel; manifolding techniques; structural pattern recognition.Spectral Graph theory has been utilized frequently in the field of Computer Vision and Pattern Recognition to address challenges in the field of Image Segmentation and Image Classification. In the proposed method, for classification techniques, the associated graph's Eigen values and Eigen vectors of the adjacency matrix or Laplacian matrix created from the images are employed. The Laplacian spectrum and a graph's heat kernel are inextricably linked. Exponentiating the Laplacian eigensystem over time yields the heat kernel, which is the solution to the heat equations. In the proposed technique K-Nearest neighborhood and Delaunay triangulation techniques are used to generate a graph from the 3D model. The graph is then represented into Normalized Laplacian (NL) and Laplacian matrix (L). From each Normalized Laplacian and Laplacian matrix, the feature vectors like Heat Content Invariant and Laplacian Eigen values are extracted. Then, using all of the available clustering algorithms on datasets, the optimum feature vector for clustering is determined. For clustering various manifolding techniques are employed. In the suggested method, the graph heat kernel is constructed using industry-standard objects which are taken from the Engineering bench mark Data set.Spectral Graph theory has been utilized frequently in the field of Computer Vision and Pattern Recognition to address challenges in the field of Image Segmentation and Image Classification. In the proposed method, for classification techniques, the associated graph's Eigen values and Eigen vectors of the adjacency matrix or Laplacian matrix created from the images are employed. The Laplacian spectrum and a graph's heat kernel are inextricably linked. Exponentiating the Laplacian eigensystem over time yields the heat kernel, which is the solution to the heat equations. In the proposed technique K-Nearest neighborhood and Delaunay triangulation techniques are used to generate a graph from the 3D model. The graph is then represented into Normalized Laplacian (NL) and Laplacian matrix (L). From each Normalized Laplacian and Laplacian matrix, the feature vectors like Heat Content Invariant and Laplacian Eigen values are extracted. Then, using all of the available clustering algorithms on datasets, the optimum feature vector for clustering is determined. For clustering various manifolding techniques are employed. In the suggested method, the graph heat kernel is constructed using industry-standard objects which are taken from the Engineering bench mark Data set.Universidade Estadual De Maringá2023-11-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/6260810.4025/actascitechnol.v46i1.62608Acta Scientiarum. Technology; Vol 46 No 1 (2024): Em proceso; e62608Acta Scientiarum. Technology; v. 46 n. 1 (2024): Publicação contínua; e626081806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/62608/751375156634Copyright (c) 2024 Acta Scientiarum. Technologyhttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessUsha, Subramaniam Karthik , Murugesan Jothibasu, Marappan Gowtham, Vijayaragavan 2024-02-08T19:23:30Zoai:periodicos.uem.br/ojs:article/62608Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2024-02-08T19:23:30Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
An integrated exploration of heat kernel invariant feature and manifolding technique for 3D object recognition system An integrated exploration of heat kernel invariant feature and manifolding technique for 3D object recognition system |
title |
An integrated exploration of heat kernel invariant feature and manifolding technique for 3D object recognition system |
spellingShingle |
An integrated exploration of heat kernel invariant feature and manifolding technique for 3D object recognition system Usha, Subramaniam Graph clustering; laplacian matrix; delaunay triangulation; graph heat kernel; manifolding techniques; structural pattern recognition. Graph clustering; laplacian matrix; delaunay triangulation; graph heat kernel; manifolding techniques; structural pattern recognition. |
title_short |
An integrated exploration of heat kernel invariant feature and manifolding technique for 3D object recognition system |
title_full |
An integrated exploration of heat kernel invariant feature and manifolding technique for 3D object recognition system |
title_fullStr |
An integrated exploration of heat kernel invariant feature and manifolding technique for 3D object recognition system |
title_full_unstemmed |
An integrated exploration of heat kernel invariant feature and manifolding technique for 3D object recognition system |
title_sort |
An integrated exploration of heat kernel invariant feature and manifolding technique for 3D object recognition system |
author |
Usha, Subramaniam |
author_facet |
Usha, Subramaniam Karthik , Murugesan Jothibasu, Marappan Gowtham, Vijayaragavan |
author_role |
author |
author2 |
Karthik , Murugesan Jothibasu, Marappan Gowtham, Vijayaragavan |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Usha, Subramaniam Karthik , Murugesan Jothibasu, Marappan Gowtham, Vijayaragavan |
dc.subject.por.fl_str_mv |
Graph clustering; laplacian matrix; delaunay triangulation; graph heat kernel; manifolding techniques; structural pattern recognition. Graph clustering; laplacian matrix; delaunay triangulation; graph heat kernel; manifolding techniques; structural pattern recognition. |
topic |
Graph clustering; laplacian matrix; delaunay triangulation; graph heat kernel; manifolding techniques; structural pattern recognition. Graph clustering; laplacian matrix; delaunay triangulation; graph heat kernel; manifolding techniques; structural pattern recognition. |
description |
Spectral Graph theory has been utilized frequently in the field of Computer Vision and Pattern Recognition to address challenges in the field of Image Segmentation and Image Classification. In the proposed method, for classification techniques, the associated graph's Eigen values and Eigen vectors of the adjacency matrix or Laplacian matrix created from the images are employed. The Laplacian spectrum and a graph's heat kernel are inextricably linked. Exponentiating the Laplacian eigensystem over time yields the heat kernel, which is the solution to the heat equations. In the proposed technique K-Nearest neighborhood and Delaunay triangulation techniques are used to generate a graph from the 3D model. The graph is then represented into Normalized Laplacian (NL) and Laplacian matrix (L). From each Normalized Laplacian and Laplacian matrix, the feature vectors like Heat Content Invariant and Laplacian Eigen values are extracted. Then, using all of the available clustering algorithms on datasets, the optimum feature vector for clustering is determined. For clustering various manifolding techniques are employed. In the suggested method, the graph heat kernel is constructed using industry-standard objects which are taken from the Engineering bench mark Data set. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-11-06 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/62608 10.4025/actascitechnol.v46i1.62608 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/62608 |
identifier_str_mv |
10.4025/actascitechnol.v46i1.62608 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/62608/751375156634 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2024 Acta Scientiarum. Technology http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2024 Acta Scientiarum. Technology http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Technology; Vol 46 No 1 (2024): Em proceso; e62608 Acta Scientiarum. Technology; v. 46 n. 1 (2024): Publicação contínua; e62608 1806-2563 1807-8664 reponame:Acta scientiarum. Technology (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta scientiarum. Technology (Online) |
collection |
Acta scientiarum. Technology (Online) |
repository.name.fl_str_mv |
Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
||actatech@uem.br |
_version_ |
1799315338139009024 |