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@InProceedings{MoreiraCost:1996:NeCoIm,
               author = "Moreira, Jander and Costa, Luciano da Fontoura",
                title = "Neural-based color image segmentation and classification using 
                         self-organizing maps",
            booktitle = "Anais...",
                 year = "1996",
               editor = "Velho, Luiz and Albuquerque, Arnaldo de and Lotufo, Roberto A.",
                pages = "47--54",
         organization = "Simp{\'o}sio Brasileiro de Computa{\c{c}}{\~a}o Gr{\'a}fica e 
                         Processamento de Imagens, 9. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "color segmentation, neural networks, self-organizing maps, 
                         classification, k-means segmentation, nearest-neighbor 
                         classification.",
             abstract = "This paper presents a method for color image segmentation which 
                         uses classification to group pixels into regions. The chromaticity 
                         is used as data source for the method because it is normalized and 
                         considers only hue and saturation, excluding the luminance 
                         component. The classification is carried out by means of a 
                         self-organizing map (SOM), which is employed to obtain the main 
                         chromaticities present in the image. Then, each pixel is 
                         classified according to the identified classes. The number of 
                         classes is a priori unknown and the artificial neural network that 
                         implements the SOM is used to determine the main classes. The 
                         detection of the classes in the SOM is done by using a K-means 
                         segmentation. The obtained results substantiate the feasibility of 
                         the method, whose performance is compared, for evaluation, to 
                         human-assisted segmentation. A comparison of the method with a 
                         segmentation based on the k-nearest-neighbor classification is 
                         also presented.",
  conference-location = "Caxambu",
      conference-year = "29 out. - 1 nov. 1996",
                 isbn = "85-244-0103-6",
             language = "en",
         organisation = "SBC - Sociedade Brasileira de Computa{\c{c}}{\~a}o; UFMG - 
                         Universidade Federal de Minas Gerais",
                  ibi = "83LX3pFwXQZW44Lb/cMRPz",
                  url = "http://urlib.net/ibi/83LX3pFwXQZW44Lb/cMRPz",
           targetfile = "a19.pdf",
                 type = "Reconhecimento de Padr{\~o}es",
        urlaccessdate = "2021, Nov. 30"
}


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