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@InProceedings{RibeiroGonz:2006:CoAn,
               author = "Ribeiro, Hebert Luchetti and Gonzaga, Adilson",
          affiliation = "{School of Engineering at Sao Carlos} and {School of Engineering 
                         at Sao Carlos}",
                title = "Hand Image Segmentation in Video Sequence by GMM: a comparative 
                         analysis",
            booktitle = "Proceedings...",
                 year = "2006",
               editor = "Oliveira Neto, Manuel Menezes de and Carceroni, Rodrigo Lima",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 19. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Video Image Segmentation, Gaussian Mixture Model.",
             abstract = "This 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.",
  conference-location = "Manaus",
      conference-year = "8-11 Oct. 2006",
             language = "en",
           targetfile = "Ribeiro-HandImageSegmentationInVideoSequenceByGMM.pdf",
        urlaccessdate = "2020, Nov. 28"
}


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