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@InProceedings{JacquesJrMuss:2015:ImHeHu,
               author = "Jacques Junior, Julio Cezar Silveira and Musse, Soraia Raupp",
          affiliation = "{Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio Grande do 
                         Sul} and {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio 
                         Grande do Sul}",
                title = "Improved head-shoulder human contour estimation through clusters 
                         of learned shape models",
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Papa, Jo{\~a}o Paulo and Sander, Pedro Vieira and Marroquim, 
                         Ricardo Guerra and Farrell, Ryan",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "human head-shoulder estimation, omega-shaped region, human 
                         segmentation.",
             abstract = "In this paper we propose a clustering-based learning approach to 
                         improve an existing model for human head-shoulder contour 
                         estimation. The contour estimation is guided by a learned 
                         head-shoulder shape model, initialized automatically by a face 
                         detector. A dataset with labeled data is used to create the 
                         headshoulder shape model and to quantitatively analyze the 
                         results. In the proposed approach, geometric features are firstly 
                         extracted from the learning dataset. Then, the number of shape 
                         models to be learned is obtained by an unsupervised clustering 
                         algorithm. In the segmentation stage, different graphs with an 
                         omega-like shape are built around the detected face, related to 
                         each learned shape model. A path with maximal cost, related to 
                         each graph, defines a initial estimative of the head-shoulder 
                         contour. The final estimation is given by the path with maximum 
                         average energy. Experimental results indicate that the proposed 
                         technique outperformed the original model, which is based on a 
                         single shape model, learned in a more simple way. In addition, it 
                         achieved comparable accuracy to other state-of-the-art models.",
  conference-location = "Salvador",
      conference-year = "Aug. 26-29, 2015",
                  doi = "10.1109/SIBGRAPI.2015.17",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2015.17",
             language = "en",
           targetfile = "sib2015-camera-ready-pdf-express.pdf",
        urlaccessdate = "2021, Dec. 03"
}


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