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@InProceedings{RodrķguezCahuAraśChav:2015:FiSpRe,
               author = "Rodr{\'{\i}}guez, Karla Catherine Otiniano and Cahuina, Edward 
                         Cayllahua and Ara{\'u}jo, Arnaldo de Albuquerque and Chavez, 
                         Guillermo C{\'a}mara",
          affiliation = "{Federal University of Minas Gerais} and {Federal University of 
                         Minas Gerais} and {Federal University of Minas Gerais} and 
                         {Federal University of Ouro Preto}",
                title = "Finger Spelling Recognition using Kernel Descriptors and Depth 
                         Images",
            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 = "finger spelling, depth images, kernel descriptors, Bag-of-Words.",
             abstract = "Deaf people use systems of communication based on sign language 
                         and finger spelling. Finger spelling is a system where each letter 
                         of the alphabet is represented by a unique and discrete movement 
                         of the hand. RGB and depth images can be used to characterize hand 
                         shapes corresponding to letters of the alphabet. There exists an 
                         advantage of depth sensors, as Kinect, over color cameras for 
                         finger spelling recognition: depth images provide 3D information 
                         of the hand. In this paper, we propose a model for finger spelling 
                         recognition based on depth information using kernel descriptors, 
                         consisting of four stages. The performance of this approach is 
                         evaluated on a dataset of real images of the American Sign 
                         Language finger spelling. Different experiments were performed 
                         using a combination of both descriptors over depth information. 
                         Our approach obtains 92.92% of mean accuracy with 50% of samples 
                         for training; outperforming other state-of-the-art methods.",
  conference-location = "Salvador",
      conference-year = "Aug. 26-29, 2015",
             language = "en",
           targetfile = "113.pdf",
        urlaccessdate = "2022, Jan. 19"
}


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