Time of Flight Image Segmentation through Co-Regularized Spectral Clustering

Autores
Lorenti, Luciano; Giacomantone, Javier
Año de publicación
2014
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Time of Flight (TOF) cameras generate two simultaneous images, one for intensity and one for range. This allows tackling segmentation problems where the information pertaining to intensity or range alone is not enough to extract objects of interest from a 3D scene. In this paper, we present a spectral segmentation method that combines information from both images. By modifying the affinity matrix of each of the images based on the other, the segmentation of objects in the scene is improved. The proposed method exploits two mechanisms, one for reducing the computational demand when calculating the eigenvectors for each matrix, and another for improving segmentation performance. The experimental results obtained with two sets of real images are presented and used to assess the proposed method.
Publicado en Feierherd, Guillermo; Pesado, Patricia; Spositto, Osvaldo (eds.). Computer Science & Technology Series. XX Argentine Congress of Computer Science. Selected papers. La Plata, Editorial de la Universidad Nacional de La Plata, 2015.
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
segmentation
range images
Time of Flight Cameras
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/54575
Acceder
id SEDICI_9c20f58bcc7aaac5a77f9db59ba63c79
oai_identifier_str oai:sedici.unlp.edu.ar:10915/54575
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Time of Flight Image Segmentation through Co-Regularized Spectral ClusteringLorenti, LucianoGiacomantone, JavierCiencias Informáticassegmentationrange imagesTime of Flight CamerasTime of Flight (TOF) cameras generate two simultaneous images, one for intensity and one for range. This allows tackling segmentation problems where the information pertaining to intensity or range alone is not enough to extract objects of interest from a 3D scene. In this paper, we present a spectral segmentation method that combines information from both images. By modifying the affinity matrix of each of the images based on the other, the segmentation of objects in the scene is improved. The proposed method exploits two mechanisms, one for reducing the computational demand when calculating the eigenvectors for each matrix, and another for improving segmentation performance. The experimental results obtained with two sets of real images are presented and used to assess the proposed method.Publicado en Feierherd, Guillermo; Pesado, Patricia; Spositto, Osvaldo (eds.). <i>Computer Science & Technology Series. XX Argentine Congress of Computer Science. Selected papers</i>. La Plata, Editorial de la Universidad Nacional de La Plata, 2015.Red de Universidades con Carreras en Informática (RedUNCI)2014info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf101-110http://sedici.unlp.edu.ar/handle/10915/54575enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-1985-71-5info:eu-repo/semantics/reference/hdl/10915/48825info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:38:05Zoai:sedici.unlp.edu.ar:10915/54575Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:38:05.924SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Time of Flight Image Segmentation through Co-Regularized Spectral Clustering
title Time of Flight Image Segmentation through Co-Regularized Spectral Clustering
spellingShingle Time of Flight Image Segmentation through Co-Regularized Spectral Clustering
Lorenti, Luciano
Ciencias Informáticas
segmentation
range images
Time of Flight Cameras
title_short Time of Flight Image Segmentation through Co-Regularized Spectral Clustering
title_full Time of Flight Image Segmentation through Co-Regularized Spectral Clustering
title_fullStr Time of Flight Image Segmentation through Co-Regularized Spectral Clustering
title_full_unstemmed Time of Flight Image Segmentation through Co-Regularized Spectral Clustering
title_sort Time of Flight Image Segmentation through Co-Regularized Spectral Clustering
dc.creator.none.fl_str_mv Lorenti, Luciano
Giacomantone, Javier
author Lorenti, Luciano
author_facet Lorenti, Luciano
Giacomantone, Javier
author_role author
author2 Giacomantone, Javier
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
segmentation
range images
Time of Flight Cameras
topic Ciencias Informáticas
segmentation
range images
Time of Flight Cameras
dc.description.none.fl_txt_mv Time of Flight (TOF) cameras generate two simultaneous images, one for intensity and one for range. This allows tackling segmentation problems where the information pertaining to intensity or range alone is not enough to extract objects of interest from a 3D scene. In this paper, we present a spectral segmentation method that combines information from both images. By modifying the affinity matrix of each of the images based on the other, the segmentation of objects in the scene is improved. The proposed method exploits two mechanisms, one for reducing the computational demand when calculating the eigenvectors for each matrix, and another for improving segmentation performance. The experimental results obtained with two sets of real images are presented and used to assess the proposed method.
Publicado en Feierherd, Guillermo; Pesado, Patricia; Spositto, Osvaldo (eds.). <i>Computer Science & Technology Series. XX Argentine Congress of Computer Science. Selected papers</i>. La Plata, Editorial de la Universidad Nacional de La Plata, 2015.
Red de Universidades con Carreras en Informática (RedUNCI)
description Time of Flight (TOF) cameras generate two simultaneous images, one for intensity and one for range. This allows tackling segmentation problems where the information pertaining to intensity or range alone is not enough to extract objects of interest from a 3D scene. In this paper, we present a spectral segmentation method that combines information from both images. By modifying the affinity matrix of each of the images based on the other, the segmentation of objects in the scene is improved. The proposed method exploits two mechanisms, one for reducing the computational demand when calculating the eigenvectors for each matrix, and another for improving segmentation performance. The experimental results obtained with two sets of real images are presented and used to assess the proposed method.
publishDate 2014
dc.date.none.fl_str_mv 2014
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/54575
url http://sedici.unlp.edu.ar/handle/10915/54575
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-987-1985-71-5
info:eu-repo/semantics/reference/hdl/10915/48825
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
101-110
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1842260239990128640
score 13.13397

Publicaciones similares