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@InProceedings{LopesCons:2005:RBPeMo,
               author = "Lopes, Fabr{\'{\i}}cio Martins and Consularo, Lu{\'{\i}}s 
                         Augusto",
          affiliation = "{CEFET-PR - Centro Federal de Educa{\c{c}}{\~a}o 
                         Tecnol{\'o}gica do Paran{\'a}} and Av. Alberto Carazzai, 1640, 
                         86300-000, Corn{\'e}lio Proc{\'o}pio, PR, Brasil. and {UNIMEP - 
                         Universidade Metodista de Piracicaba} and Rodovia do 
                         A{\c{c}}{\'u}car, Km 156, 13400-911, Piracicaba, SP, Brasil.",
                title = "A RBFN perceptive model for image thresholding",
            booktitle = "Proceedings...",
                 year = "2005",
               editor = "Rodrigues, Maria Andr{\'e}ia Formico and Frery, Alejandro 
                         C{\'e}sar",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 18. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Segmentation, Thresholding, RBFN, Psychophysical.",
             abstract = "The digital image segmentation challenge has demanded the 
                         development of a plethora of methods and approaches. A quite 
                         simple approach, the thresholding, has still been intensively 
                         applied mainly for real-time vision applications. However, the 
                         threshold criteria often depend on entropic or statistical image 
                         features. This work searches a relationship between these features 
                         and subjective human threshold decisions. Then, an image 
                         thresholding model based on these subjective decisions and global 
                         statistical features was developed by training a Radial Basis 
                         Functions Network (RBFN). This work also compares the automatic 
                         thresholding methods to the human responses. Furthermore, the 
                         RBFN-modeled answers were compared to the automatic thresholding. 
                         The results show that entropic-based method was closer to 
                         RBFN-modeled thresholding than variance-based method. It was also 
                         found that another automatic method which combines global and 
                         local criteria presented higher correlation with human 
                         responses.",
  conference-location = "Natal",
      conference-year = "9-12 Oct. 2005",
             language = "en",
           targetfile = "lopesf_rbfnperceptive.pdf",
        urlaccessdate = "2020, Nov. 29"
}


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