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Background Subtraction Approach based on Independent Component Analysis

In this work, a new approach to background subtraction based on independent component analysis is presented. This approach assumes that background and foreground information are mixed in a given sequence of images. Then, foreground and background components are identified, if their probability densi...

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Detalles Bibliográficos
Autor principal: Jiménez-Hernández, Hugo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3247749/
https://www.ncbi.nlm.nih.gov/pubmed/22219704
http://dx.doi.org/10.3390/s100606092
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author Jiménez-Hernández, Hugo
author_facet Jiménez-Hernández, Hugo
author_sort Jiménez-Hernández, Hugo
collection PubMed
description In this work, a new approach to background subtraction based on independent component analysis is presented. This approach assumes that background and foreground information are mixed in a given sequence of images. Then, foreground and background components are identified, if their probability density functions are separable from a mixed space. Afterwards, the components estimation process consists in calculating an unmixed matrix. The estimation of an unmixed matrix is based on a fast ICA algorithm, which is estimated as a Newton-Raphson maximization approach. Next, the motion components are represented by the mid-significant eigenvalues from the unmixed matrix. Finally, the results show the approach capabilities to detect efficiently motion in outdoors and indoors scenarios. The results show that the approach is robust to luminance conditions changes at scene.
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spelling pubmed-32477492012-01-04 Background Subtraction Approach based on Independent Component Analysis Jiménez-Hernández, Hugo Sensors (Basel) Article In this work, a new approach to background subtraction based on independent component analysis is presented. This approach assumes that background and foreground information are mixed in a given sequence of images. Then, foreground and background components are identified, if their probability density functions are separable from a mixed space. Afterwards, the components estimation process consists in calculating an unmixed matrix. The estimation of an unmixed matrix is based on a fast ICA algorithm, which is estimated as a Newton-Raphson maximization approach. Next, the motion components are represented by the mid-significant eigenvalues from the unmixed matrix. Finally, the results show the approach capabilities to detect efficiently motion in outdoors and indoors scenarios. The results show that the approach is robust to luminance conditions changes at scene. Molecular Diversity Preservation International (MDPI) 2010-06-18 /pmc/articles/PMC3247749/ /pubmed/22219704 http://dx.doi.org/10.3390/s100606092 Text en © 2010 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Jiménez-Hernández, Hugo
Background Subtraction Approach based on Independent Component Analysis
title Background Subtraction Approach based on Independent Component Analysis
title_full Background Subtraction Approach based on Independent Component Analysis
title_fullStr Background Subtraction Approach based on Independent Component Analysis
title_full_unstemmed Background Subtraction Approach based on Independent Component Analysis
title_short Background Subtraction Approach based on Independent Component Analysis
title_sort background subtraction approach based on independent component analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3247749/
https://www.ncbi.nlm.nih.gov/pubmed/22219704
http://dx.doi.org/10.3390/s100606092
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