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@InProceedings{VaqueroBarrHira:2005:MaApMu,
               author = "Vaquero, Daniel Andr{\'e} and Barrera, Junior and Hirata Jr., 
                         Roberto",
          affiliation = "{Universidade de S{\~a}o Paulo}",
                title = "A maximum-likelihood approach for multiresolution W-operator 
                         design",
            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 = "design of image operators, mathematical morphology, 
                         multiresolution, entropy, handwritten digits recognition.",
             abstract = "The design of W-operators from a set of input/output examples for 
                         large windows is a hard problem. From the statistical standpoint, 
                         it is hard because of the large number of examples necessary to 
                         obtain a good estimate of the joint distribution. From the 
                         computational standpoint, as the number of examples grows memory 
                         and time requirements can reach a point where it is not feasible 
                         to design the operator. This paper introduces a technique for 
                         joint distribution estimation in W-operator design. The 
                         distribution is represented by a multiresolution pyramidal 
                         structure and the mean conditional entropy is proposed as a 
                         criterion to choose between distributions induced by different 
                         pyramids. Experimental results are presented for 
                         maximum-likelihood classifiers designed for the problem of 
                         handwritten digits classification. The analysis shows that the 
                         technique is interesting from the theoretical point of view and 
                         has potential to be applied in computer vision and image 
                         processing problems.",
  conference-location = "Natal",
      conference-year = "9-12 Oct. 2005",
             language = "en",
           targetfile = "vaquerod_multiresolution.pdf",
        urlaccessdate = "2020, Nov. 27"
}


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