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@InProceedings{GattoJśniSant:2017:OrHaSu,
               author = "Gatto, Bernardo Bentes and J{\'u}nior, Waldir Sabino da Silva and 
                         Santos, Eulanda Miranda dos",
          affiliation = "{Federal University of Amazonas} and {Federal University of 
                         Amazonas} and {Federal University of Amazonas}",
                title = "Orthogonal Hankel Subspaces for Applications in Gesture 
                         Recognition",
            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 = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Hankel matrix, subspace method, gesture recognition.",
             abstract = "Gesture recognition is an important research area in video 
                         analysis and computer vision. Gesture recognition systems include 
                         several advantages, such as the interaction with machines without 
                         needing additional external devices. Moreover, gesture recognition 
                         involves many challenges, as the distribution of a specific 
                         gesture largely varies depending on viewpoints due to its multiple 
                         joint structures. In this paper, We present a novel framework for 
                         gesture recognition. The novelty of the proposed framework lies in 
                         three aspects: first, we propose a new gesture representation 
                         based on a compact trajectory matrix, which preserves spatial and 
                         temporal information. We understand that not all images of a 
                         gesture video are useful for the recognition task, therefore it is 
                         necessary to create a method where it is possible to detect the 
                         images that do not contribute to the recognition task, decreasing 
                         the computational cost of the overall framework. Second, we 
                         represent this compact trajectory matrix as a subspace, achieving 
                         discriminative information, as the trajectory matrices obtained 
                         from different gestures generate dissimilar clusters in a low 
                         dimension space. Finally, we introduce an automatic procedure to 
                         infer the optimal dimension of each gesture subspace. We show that 
                         our compact representation presents practical and theoretical 
                         advantages, such as compact representation and low computational 
                         requirements. We demonstrate the advantages of the proposed method 
                         by experimentation employing Cambridge gesture and Human-Computer 
                         Interaction datasets.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "Hankel_Subspace.pdf",
        urlaccessdate = "2021, Jan. 21"
}


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