Background Subtraction for Time of Flight Imaging

A time of flight camera provides two types of images simultaneously, depth and intensity. In this paper a computational method for background subtraction, combining both images or fast sequences of images, is proposed. The background model is based on unbalanced or semi-supervised classifiers, in pa...

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Detalles Bibliográficos
Autores: Giacomantone, Javier, Violini, María Lucía, Lorenti, Luciano
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2017
País:Argentina
Institución:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
Repositorio:CIC Digital (CICBA)
Idioma:inglés
OAI Identifier:oai:digital.cic.gba.gob.ar:11746/8579
Acceso en línea:https://digital.cic.gba.gob.ar/handle/11746/8579
Access Level:acceso abierto
Palabra clave:Ingenierías y Tecnologías
industrial TOF cameras
machine vision
pattern recognition
support vector machines
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spelling Background Subtraction for Time of Flight ImagingGiacomantone, JavierViolini, María LucíaLorenti, LucianoIngenierías y Tecnologíasindustrial TOF camerasmachine visionpattern recognitionsupport vector machinesA time of flight camera provides two types of images simultaneously, depth and intensity. In this paper a computational method for background subtraction, combining both images or fast sequences of images, is proposed. The background model is based on unbalanced or semi-supervised classifiers, in particular support vector machines. A brief review of one class support vector machines is first given. A method that combines the range and intensity data in two operational modes is then provided. Finally, experimental results are presented and discussed.2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/8579enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2024-05-10T10:39:01Zoai:digital.cic.gba.gob.ar:11746/8579Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412024-05-10 10:39:01.459CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
dc.title.none.fl_str_mv Background Subtraction for Time of Flight Imaging
title Background Subtraction for Time of Flight Imaging
spellingShingle Background Subtraction for Time of Flight Imaging
Giacomantone, Javier
Ingenierías y Tecnologías
industrial TOF cameras
machine vision
pattern recognition
support vector machines
title_short Background Subtraction for Time of Flight Imaging
title_full Background Subtraction for Time of Flight Imaging
title_fullStr Background Subtraction for Time of Flight Imaging
title_full_unstemmed Background Subtraction for Time of Flight Imaging
title_sort Background Subtraction for Time of Flight Imaging
dc.creator.none.fl_str_mv Giacomantone, Javier
Violini, María Lucía
Lorenti, Luciano
author Giacomantone, Javier
author_facet Giacomantone, Javier
Violini, María Lucía
Lorenti, Luciano
author_role author
author2 Violini, María Lucía
Lorenti, Luciano
author2_role author
author
dc.subject.none.fl_str_mv Ingenierías y Tecnologías
industrial TOF cameras
machine vision
pattern recognition
support vector machines
topic Ingenierías y Tecnologías
industrial TOF cameras
machine vision
pattern recognition
support vector machines
description A time of flight camera provides two types of images simultaneously, depth and intensity. In this paper a computational method for background subtraction, combining both images or fast sequences of images, is proposed. The background model is based on unbalanced or semi-supervised classifiers, in particular support vector machines. A brief review of one class support vector machines is first given. A method that combines the range and intensity data in two operational modes is then provided. Finally, experimental results are presented and discussed.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://digital.cic.gba.gob.ar/handle/11746/8579
url https://digital.cic.gba.gob.ar/handle/11746/8579
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:CIC Digital (CICBA)
instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron:CICBA
instname_str Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron_str CICBA
institution CICBA
reponame_str CIC Digital (CICBA)
collection CIC Digital (CICBA)
repository.name.fl_str_mv CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
repository.mail.fl_str_mv marisa.degiusti@sedici.unlp.edu.ar
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score 15,812429

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