@InProceedings{DazziCampHiltCesa:2016:EfObRe,
               author = "Dazzi, Estephan and De Campos, Te{\'o}filo and Hilton, Adrian and 
                         Cesar Jr., Roberto Marcondes",
          affiliation = "{Instituto de Matem{\'a}tica e Estat{\'{\i}}stica - 
                         Universidade de S{\~a}o Paulo} and {CVSSP - University of Surrey} 
                         and {CVSSP - University of Surrey} and {Instituto de 
                         Matem{\'a}tica e Estat{\'{\i}}stica - Universidade de S{\~a}o 
                         Paulo}",
                title = "Efficient object recognition using sampling of keypoint triples 
                         and keygraph structure",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "IEEE Computer Society´s Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "Local image feature matching, semi-local graph matching, graph 
                         topological properties.",
             abstract = "We present an object matching method that employs matches of local 
                         graphs of keypoints, called keygraphs, instead of simple keypoint 
                         matches. For a keygraph match to be valid, vertex (keypoint) 
                         descriptors must be similar and both keygraphs must satisfy 
                         structural properties concerning keypoints orientation, scale, 
                         relative position and cyclic ordering; as a result, the large 
                         majority of initial incorrect keypoint matches is correctly 
                         filtered out. We introduce a novel approach to sample keypoint 
                         triples (i.e. keygraphs) in a query image, based on complementary 
                         Delaunay triangulations; this generates a linear number of triples 
                         with relation to the number of keypoints. Query keygraphs are then 
                         matched against the indexed model keypoints; each established 
                         keygraph match is used to evaluate a candidate pose (an affine 
                         transformation). The proposed method has been evaluated for object 
                         recognition and pose estimation, achieving a better performance in 
                         comparison to state-of-the-art methods.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.024",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.024",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M75PBE",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M75PBE",
           targetfile = "84.pdf",
        urlaccessdate = "2024, May 03"
}