@InProceedings{MedeirosGomeGonç:2021:TaDeUs,
               author = "Medeiros, Petrucio Ricardo Tavares and Gomes, Rafael Beserra and 
                         Gon{\c{c}}alves, Luiz Marcos Garcia",
          affiliation = "Department of Computer Engineering, Federal University of Rio 
                         Grande do Norte and Department of Computer Science and Applied 
                         Mathematics, Federal University of Rio Grande do Norte and 
                         Department of Computer Engineering, Federal University of Rio 
                         Grande do Norte",
                title = "Targets Detection Using Multiple Foveas",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Multifoveation, target detection, gradient descent, maximum 
                         likelihood, trilateration and barycentric coordinates.",
             abstract = "Target detection enables running a robotic task. However, their 
                         limited resources make large amount of data processing harder. 
                         Image foveation is an approach that can reduce processing demand 
                         by reducing the amount of data to be processed. However, as an 
                         important visual stimulli can be attenuated by this reduction, 
                         some strategy should be applied in order to keep/recover awareness 
                         of it. This work compares gradient descent (potential field), 
                         maximum likelihood, multilateration, trilateration, and 
                         barycentric coordinates to solve this problem in a multiple mobile 
                         foveas context. Our results demonstrate that the proposed 
                         methodology detects the target converging with an average 
                         euclidian distance of 51 pixels from the target's center 
                         position.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45DNFM2",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45DNFM2",
           targetfile = "paper.pdf",
        urlaccessdate = "2024, May 06"
}