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@InProceedings{SpinaFalc:2010:InUnUs,
               author = "Spina, Thiago Vallin and Falc{\~a}o, Alexandre Xavier",
          affiliation = "{Institute of Computing - University of Campinas} and {Institute 
                         of Computing - University of Campinas}",
                title = "Intelligent understanding of user input applied to arc-weight 
                         estimation for graph-based foreground segmentation",
            booktitle = "Proceedings...",
                 year = "2010",
               editor = "Bellon, Olga and Esperan{\c{c}}a, Claudio",
         organization = "Conference on Graphics, Patterns and Images, 23. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "graph-based image segmentation, intelligent arc-weight estimation, 
                         image foresting transform, fuzzy supervised classification, 
                         clustering, multiscale image filtering.",
             abstract = "We present an intelligent approach for understanding user 
                         interaction to simplify the interface of graph-based image 
                         segmentation. The user draws a set of markers (strokes) over the 
                         object and the background, and our method automatically determines 
                         a subset of these pixels with dissimilar image properties for 
                         arc-weight estimation. Arc-weight estimation combines object 
                         information learned from the set of selected pixels with image 
                         information, to make object delineation more effective. Our method 
                         differs from approaches that recompute the arc weights carelessly 
                         during delineation, by further interpreting user interaction to 
                         determine when and where to recompute them. Furthermore, we build 
                         our framework around the image foresting transform (IFT), by 
                         taking advantage of its operators for supervised fuzzy 
                         classification, clustering, and object delineation. We evaluate 
                         our framework using a dataset with 50 natural images and by 
                         comparing it against another recent IFT-based method, which 
                         computes arc weights in a separated step of user interaction for 
                         more effective segmentation.",
  conference-location = "Gramado",
      conference-year = "Aug. 30 - Sep. 3, 2010",
             language = "en",
           targetfile = "PID1393191.pdf",
        urlaccessdate = "2020, Nov. 29"
}


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