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@InProceedings{JúniorMedeBezeUshi:2011:CoDeWi,
               author = "J{\'u}nior, I{\'a}lis Cavalcante de Paula and Medeiros, 
                         F{\'a}tima Nelsizeuma Sombra de and Bezerra, Francisco Nivando 
                         and Ushizima, Daniela Mayumi",
          affiliation = "{Universidade Federal do Cear{\'a}} and {Universidade Federal do 
                         Cear{\'a}} and Instituto Federal de Educa{\c{c}}{\~a}o, 
                         Ci{\^e}ncia e Tecnologia and {Lawrence Berkeley National 
                         Laboratory}",
                title = "Corner detection within a multiscale framework",
            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 = "corner detection, high curvature points (HCP), shape 
                         reconstruction, curvature space-scale (CSS).",
             abstract = "We present a new multiscale method for corner detection. The 
                         proposed algorithm embodies an undecimated wavelet decomposition 
                         of the angulation signal of a shape contour to identify 
                         significant points on it. It detects peaks that persist through 
                         several scales from the correlation signal between scales of its 
                         non-orthogonal sub-band decompositions. These peaks correspond to 
                         high curvature points (HCPs). Furthermore, we compare the proposed 
                         method with others available in the literature, including the 
                         well-known curvature scale-space (CSS) method. The quantitative 
                         assessment of the algorithms is provided by some figures of merit 
                         (FOM) measures that indicate which method better detects the 
                         relevant points in terms of compaction and shape reconstruction.",
  conference-location = "Macei{\'o}",
      conference-year = "Aug. 28 - 31, 2011",
             language = "en",
           targetfile = "Camera_Ready_SIBGRAPI.pdf",
        urlaccessdate = "2019, Dec. 07"
}


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