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@InProceedings{CasacaPaivNona:2011:SpSeUs,
               author = "Casaca, Wallace and Paiva, Afonso and Nonato, Luis Gustavo",
          affiliation = "{Instituto de Ci{\^e}ncias Matem{\'a}ticas e de 
                         Computa{\c{c}}{\~a}o  USP} and {Instituto de Ci{\^e}ncias 
                         Matem{\'a}ticas e de Computa{\c{c}}{\~a}o  USP} and {Instituto 
                         de Ci{\^e}ncias Matem{\'a}ticas e de Computa{\c{c}}{\~a}o  
                         USP}",
                title = "Spectral Segmentation using Cartoon-Texture Decomposition and 
                         Inner Product-based metric",
            booktitle = "Proceedings...",
                 year = "2011",
               editor = "Lewiner, Thomas and Torres, Ricardo",
         organization = "Conference on Graphics, Patterns and Images, 24. (SIBGRAPI)",
            publisher = "IEEE Computer Society Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "Spectral cut, image segmentation, similarity graph, 
                         cartoon-texture decomposition, harmonic analysis, normalized 
                         cuts.",
             abstract = "This paper presents a user-assisted image partition technique that 
                         combines cartoon-texture decomposition, inner product-based 
                         similarity metric, and spectral cut into a unified framework. The 
                         cartoon-texture decomposition is used to first split the image 
                         into textured and texture-free components, the latter being used 
                         to define a gradient-based inner-product function. An affinity 
                         graph is then derived and weights are assigned to its edges 
                         according to the inner product-based metric. Spectral cut is 
                         computed on the affinity graph so as to partition the image. The 
                         computational burden of the spectral cut is mitigated by a 
                         fine-to-coarse image representation process, which enables 
                         moderate size graphs that can be handled more efficiently. The 
                         partitioning can be steered by interactively by changing the 
                         weights of the graph through user strokes. Weights are updated by 
                         combining the texture component computed in the first stage of our 
                         pipeline and a recent harmonic analysis technique that captures 
                         waving patterns. Finally, a coarse-to-fine interpolation is 
                         applied in order to project the partition back onto the original 
                         image. The suitable performance of the proposed methodology is 
                         attested by comparisons against state-of-art spectral segmentation 
                         methods.",
  conference-location = "Macei{\'o}",
      conference-year = "Aug. 28 - 31, 2011",
             language = "en",
           targetfile = "Sibgrapi_paper.pdf",
        urlaccessdate = "2019, Dec. 09"
}


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