@InProceedings{SantosFernBeze:2016:GeReSy,
               author = "Santos, Diego George da Silva and Fernandes, Bruno Jos{\'e} 
                         Torres and Bezerra, Byron Leite Dantas",
          affiliation = "{Universidade de Pernambuco} and {Universidade de Pernambuco} and 
                         {Universidade de Pernambuco}",
                title = "HAGR-D: A Gesture Recognition System based on CIPBR Algorithm",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "CIPBR, HMM, DTW, Gesture Recognition.",
             abstract = "Gesture recognition has been an area of great interest and study 
                         in recent years due to the evolution of technology and computers 
                         processing power, generating a higher degree in the Interaction 
                         Human Computer (IHC). These advances now allow communication 
                         between man and machine through hand gestures or entire body, 
                         especially in games, after the advent of Microsoft Kinect and 
                         other depth sensors. This paper proposes a dynamic gesture 
                         recognition system for user hand. The system is evaluated in two 
                         bases of dynamic hand gestures from the literature. The 
                         experiments show that the proposed model overcomes other 
                         algorithms presented in the literature in hand gesture recognition 
                         tasks, achieving a classification rate of 97.49\% in the 
                         MSRGesture3D dataset and 98.43\% in the RPPDI dynamic gesture 
                         dataset.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M9HLNS",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M9HLNS",
           targetfile = "sibgrap.pdf",
        urlaccessdate = "2024, May 02"
}