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@InProceedings{Cuadros-VargasGerCasBatNon:2006:Im2DMe,
               author = "Cuadros-Vargas, Alex J. and Gerhardinger, Leandro C. and Castro, 
                         M{\'a}rio and Batista Neto, Jo{\~a}o and Nonato, Luis G.",
          affiliation = "{ICMC - Instituto de Ci{\^e}ncias Matem{\'a}ticas e de 
                         Computa{\c{c}}{\~a}o - USP} and {ICMC - Instituto de 
                         Ci{\^e}ncias Matem{\'a}ticas e de Computa{\c{c}}{\~a}o - USP} 
                         and {ICMC - Instituto de Ci{\^e}ncias Matem{\'a}ticas e de 
                         Computa{\~a}o - USP} and {ICMC - Instituto de Ci{\^e}ncias 
                         Matem{\'a}ticas e de Computa{\~a}o - USP} and {ICMC - Instituto 
                         de Ci{\^e}ncias Matem{\'a}ticas e de Computa{\~a}o - USP}",
                title = "Improving 2D mesh image segmentation with Markovian Random 
                         Fields",
            booktitle = "Proceedings...",
                 year = "2006",
               editor = "Oliveira Neto, Manuel Menezes de and Carceroni, Rodrigo Lima",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 19. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Polygonal Meshes, Texture Analysis, Segmentation.",
             abstract = "Traditional mesh segmentation methods normally operate on 
                         geometrical models with no image information. On the other hand, 
                         2D image-based mesh generation and segmentation counterparts, such 
                         as Imesh \cite{Vargas:05} perform the task by following a set of 
                         well defined rules derived from the geometry of the triangles, but 
                         with no statistical information of the mesh elements. This paper 
                         presents a novel segmentation method that combines the original 
                         Imesh image-based segmentation approach with Markovian Random 
                         Field (MRF) models. It takes an image as input, generate a mesh of 
                         triangles and, by treating the mesh as a Markovian field, produces 
                         quality unsupervised segmentation. The results have demonstrated 
                         that the method not only provides better segmentation than that of 
                         original Imesh, but is also capable of producing MRF-like 
                         segmentation output for certain types of images, with considerable 
                         cut in processing times.",
  conference-location = "Manaus",
      conference-year = "8-11 Oct. 2006",
             language = "en",
           targetfile = "Vargas-MRF_Mesh.pdf",
        urlaccessdate = "2020, Dec. 04"
}


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