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@InProceedings{AmaralLimaVieiViei:2017:ReGeEs,
               author = "Amaral, Lucas and Lima, Givanildo and Vieira, Tiago and Vieira, 
                         Thales",
          affiliation = "{Universidade Federal de Alagoas} and {Universidade Federal de 
                         Alagoas} and {Universidade Federal de Alagoas} and {Universidade 
                         Federal de Alagoas}",
                title = "Reconhecimento de gestos est{\'a}ticos da m{\~a}o usando a 
                         Transformada de Dist{\^a}ncia e aplica{\c{c}}{\~o}es em 
                         Libras",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Transformada de Dist{\^a}ncia, Redes Neurais Convolucionais, 
                         Gestos de Libras.",
             abstract = "In this paper we propose a method to recognize static hand 
                         gestures from depth images. We first segment the hand from the 
                         background, and then compute the Distance Transform to train a 
                         Convolutional Neural Network (CNN) that is later used to classify 
                         hand poses. In order to evaluate our method in a practical 
                         context, we collected a dataset containing 1400 images 
                         representing 14 different hand configurations representing signs 
                         of the Brazilian Sign Language (Libras). Our method achieved an 
                         average recognition rate of 96.42.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "pt",
                  ibi = "8JMKD3MGPAW/3PHJJAP",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3PHJJAP",
           targetfile = "Artigo_Distancia.pdf",
        urlaccessdate = "2021, Jan. 25"
}


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