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Reference TypeConference Proceedings
Sitesibgrapi.sid.inpe.br
Identifier6qtX3pFwXQZG2LgkFdY/LzdxM
Repositorysid.inpe.br/sibgrapi@80/2006/06.26.11.48
Last Update2006:07.10.17.21.57 administrator
Metadatasid.inpe.br/sibgrapi@80/2006/06.26.11.48.25
Metadata Last Update2020:02.19.03.17.33 administrator
Citation KeyRibeiroGonz:2006:CoAn
TitleHand Image Segmentation in Video Sequence by GMM: a comparative analysis
FormatOn-line
Year2006
Date8-11 Oct. 2006
Access Date2020, Dec. 03
Number of Files1
Size1015 KiB
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Author1 Ribeiro, Hebert Luchetti
2 Gonzaga, Adilson
Affiliation1 School of Engineering at Sao Carlos
2 School of Engineering at Sao Carlos
EditorOliveira Neto, Manuel Menezes de
Carceroni, Rodrigo Lima
e-Mail Addressagonzaga@sc.usp.br
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)
Conference LocationManaus
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2006-07-10 17:21:58 :: adilson -> banon ::
2006-08-30 21:58:08 :: banon -> adilson ::
2008-07-17 14:11:02 :: adilson -> administrator ::
2009-08-13 20:38:00 :: administrator -> banon ::
2010-08-28 20:02:22 :: banon -> administrator ::
2020-02-19 03:17:33 :: administrator -> :: 2006
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Is the master or a copy?is the master
Document Stagecompleted
Transferable1
Content TypeExternal Contribution
Tertiary TypeFull Paper
KeywordsVideo Image Segmentation, Gaussian Mixture Model.
AbstractThis paper describes different approaches of realtimeGMM (Gaussian Mixture Method) backgroundsubtraction algorithm using video sequences for handimage segmentation. In each captured image, thesegmentation takes place where pixels belonging to thehands are separated from the background based onbackground extraction and skin-color segmentation. Atime-adaptive mixture of Gaussians is used to modelthe distribution of each pixel color value. For an inputimage, every new pixel value is checked, deciding if itmatches with one of the existing Gaussians based onthe distance from the mean in terms of the standarddeviation. The best matching distribution parametersare updated and its weight is increased. It is assumedthat the values of the background pixels have lowvariance and large weight. These matched pixels,considered as foreground, are compared based on skincolor thresholds. The hands position and otherattributes are tracked by frame. That enables us todistinguish the hand movement from the backgroundand other objects in movement, as well as to extractthe information from the movement for dynamic handgesture recognition.
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Target FileRibeiro-HandImageSegmentationInVideoSequenceByGMM.pdf
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Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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