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Conference Paper: Adaptive background defogging with foreground decremental preconditioned conjugate gradient

TitleAdaptive background defogging with foreground decremental preconditioned conjugate gradient
Authors
KeywordsAdaptive backgrounds
Background region
Foreground regions
Foreground/background segmentation
Preconditioned conjugate gradient
Issue Date2013
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The 11th Asian Conference on Computer Vision (ACCV 2012), Daejeon, South Korea, 5-9 November 2012. In Lecture Notes in Computer Science, 2013, v. 7727, p. 602-614 How to Cite?
AbstractThe quality of outdoor surveillance videos are always degraded by bad weathers, such as fog, haze, and snowing. The degraded videos not only provide poor visualizations, but also increase the difficulty of vision-based analysis such as foreground/background segmentation. However, haze/fog removal has never been an easy task, and is often very time consuming. Most of the existing methods only consider a single image, and no temporal information of a video is used. In this paper, a novel adaptive background defogging method is presented. It is observed that most of the background regions between two consecutive video frames do not vary too much. Based on this observation, each video frame is firstly defogged by a background transmission map which is generated adaptively by the proposed foreground decremental preconditioned conjugate gradient (FDPCG). It is shown that foreground/background segmentation can be improved dramatically with such background-defogged video frames. With the help of a foreground map, the defogging of foreground regions is then completed by 1) foreground transmission estimation by fusion, and 2) transmission refinement by the proposed foreground incremental preconditioned conjugate gradient (FIPCG). Experimental results show that the proposed method can effectively improve the visualization quality of surveillance videos under heavy fog and snowing weather. Comparing with the state-of-the-art image defogging methods, the proposed method is much more efficient. © 2013 Springer-Verlag.
DescriptionLNCS v. 7724-7727 (pts. 1-4) entitled: Computer vision - ACCV 2012: 11th Asian Conference on Computer Vision ... 2012: revised selected papers
Persistent Identifierhttp://hdl.handle.net/10722/177406
ISBN
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252

 

DC FieldValueLanguage
dc.contributor.authorYuk, JSCen_US
dc.contributor.authorWong, KKYen_US
dc.date.accessioned2012-12-18T05:08:39Z-
dc.date.available2012-12-18T05:08:39Z-
dc.date.issued2013en_US
dc.identifier.citationThe 11th Asian Conference on Computer Vision (ACCV 2012), Daejeon, South Korea, 5-9 November 2012. In Lecture Notes in Computer Science, 2013, v. 7727, p. 602-614en_US
dc.identifier.isbn978-3-642-37446-3-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/177406-
dc.descriptionLNCS v. 7724-7727 (pts. 1-4) entitled: Computer vision - ACCV 2012: 11th Asian Conference on Computer Vision ... 2012: revised selected papers-
dc.description.abstractThe quality of outdoor surveillance videos are always degraded by bad weathers, such as fog, haze, and snowing. The degraded videos not only provide poor visualizations, but also increase the difficulty of vision-based analysis such as foreground/background segmentation. However, haze/fog removal has never been an easy task, and is often very time consuming. Most of the existing methods only consider a single image, and no temporal information of a video is used. In this paper, a novel adaptive background defogging method is presented. It is observed that most of the background regions between two consecutive video frames do not vary too much. Based on this observation, each video frame is firstly defogged by a background transmission map which is generated adaptively by the proposed foreground decremental preconditioned conjugate gradient (FDPCG). It is shown that foreground/background segmentation can be improved dramatically with such background-defogged video frames. With the help of a foreground map, the defogging of foreground regions is then completed by 1) foreground transmission estimation by fusion, and 2) transmission refinement by the proposed foreground incremental preconditioned conjugate gradient (FIPCG). Experimental results show that the proposed method can effectively improve the visualization quality of surveillance videos under heavy fog and snowing weather. Comparing with the state-of-the-art image defogging methods, the proposed method is much more efficient. © 2013 Springer-Verlag.-
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/-
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.rightsThe original publication is available at www.springerlink.com-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectAdaptive backgrounds-
dc.subjectBackground region-
dc.subjectForeground regions-
dc.subjectForeground/background segmentation-
dc.subjectPreconditioned conjugate gradient-
dc.titleAdaptive background defogging with foreground decremental preconditioned conjugate gradienten_US
dc.typeConference_Paperen_US
dc.identifier.emailWong, KKY: kykwong@cs.hku.hken_US
dc.identifier.authorityWong, KKY=rp01393en_US
dc.description.naturepostprint-
dc.identifier.doi10.1007/978-3-642-37447-0_46-
dc.identifier.scopuseid_2-s2.0-84875896715-
dc.identifier.hkuros212816en_US
dc.identifier.volume7727-
dc.identifier.spage602-
dc.identifier.epage614-
dc.publisher.placeGermany-
dc.customcontrol.immutablesml 140219-

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