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@InProceedings{Paix„oGracJrJr:2008:BaApGr,
                  ibi = "6qtX3pFwXQZG2LgkFdY/V8gFt",
                  url = "http://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/V8gFt",
          affiliation = "{Instituto de Matem{\'a}tica e Estat{\'{\i}}stica - USP} and 
                         {Instituto de Matem{\'a}tica e Estat{\'{\i}}stica - USP} and 
                         {Instituto de Matem{\'a}tica e Estat{\'{\i}}stica - USP} and 
                         {Instituto de Matem{\'a}tica e Estat{\'{\i}}stica - USP}",
             language = "en",
              address = "Los Alamitos",
               author = "Paix{\~a}o, Thiago Meireles and Graciano, Ana Beatriz V. and Jr, 
                         Roberto M. Cesar and Jr, Roberto Hirata",
                title = "A Backmapping Approach for Graph-based Object Tracking",
  conference-location = "Campo Grande, MS, Brazil",
                 year = "2008",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 21. 
                         (SIBGRAPI)",
             keywords = "Digital Image Processing, Graph-based Segmentation, Object 
                         Tracking.",
           targetfile = "hirata-Backmapping.pdf",
               editor = "Jung, Cl{\'a}udio Rosito and Walter, Marcelo",
            publisher = "IEEE Computer Society",
      conference-year = "12-15 Oct. 2008",
            booktitle = "Proceedings...",
             abstract = "Model-based methods play a central role to solve different 
                         problems in computer vision. A particular important class of such 
                         methods rely on graph models where an object is decomposed into a 
                         number of parts, each one being represented by a graph vertex. A 
                         graph model-based tracking algorithm has been recently introduced 
                         in which a model is generated for a given frame (reference frame) 
                         and used to track a target object in the subsequent ones. Because 
                         the view of an object changes along the video sequence, the 
                         solution updated the model using affine transformations. This 
                         paper proposes a different approach and improves the previous one 
                         in several ways. Firstly, instead of updating the model, each 
                         analyzed frame is backmapped to the model space, thus providing 
                         more robustness to the method because model parameters do not have 
                         to be modified. A different method for model generation based on 
                         user traces has also been implemented and used. This model 
                         generation approach is much simpler and user-friendly. Finally, a 
                         graph-matching algorithm that has been recently proposed is used 
                         for object tracking. This new algorithm is more efficient and 
                         leads to better matching results. Experimental results using 
                         synthetic and real sequences from the CAVIAR project are shown and 
                         discussed. .",
                  doi = "10.1109/SIBGRAPI.2008.32",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2008.32",
        urlaccessdate = "2022, May 21"
}


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