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@InProceedings{DonattiWürt:2007:MeOrIn,
               author = "Donatti, Guillermo S. and W{\"u}rtz, Rolf P.",
          affiliation = "Institut f{\"u}r Neuroinformatik, International Graduate School 
                         of Neuroscience, Ruhr-Universit{\"a}t Bochum and Institut 
                         f{\"u}r Neuroinformatik, International Graduate School of 
                         Neuroscience, Ruhr-Universit{\"a}t Bochum",
                title = "Memory Organization for Invariant Object Recognition and 
                         Categorization",
            booktitle = "Proceedings...",
                 year = "2007",
               editor = "Gon{\c{c}}alves, Luiz and Wu, Shin Ting",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 20. 
                         (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Computer Vision, Theoretical Neuroscience, Neuroscience.",
             abstract = "The integration of bottom-up with top-down object processing has 
                         always been a topic of major concern in computer vision. However, 
                         while a lot is known about feature extraction, the 
                         knowledge-driven aspect of perception has been recognized as 
                         important, but hard to probe experimentally and difficult to 
                         implement in computer vision systems. How object knowledge must be 
                         organized so that it supports scene perception and can be acquired 
                         automatically is a research problem of outstanding significance 
                         for the biological, the psychological, and the computational 
                         approach to understand perception. The present work aims to 
                         develop an object memory model which can provide fast retrieval 
                         and robust recognition and categorization. The underlying data 
                         structure is inspired by the neural network structure of the human 
                         brain, connecting similar object views with excitatory synapses 
                         and using inhibitory synapses to separate different ones. The 
                         insights derived from building such a computational theory and the 
                         properties of the resulting model have implications for strategies 
                         and experimental paradigms to analyze human object memory as well 
                         as technical applications for robotics and computer vision.",
  conference-location = "Belo Horizonte",
      conference-year = "Oct. 7-10, 2007",
             language = "en",
           targetfile = "sibgrapi_donatti_final.pdf",
        urlaccessdate = "2020, Oct. 26"
}


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