@InProceedings{RamosWataTrai:2016:NoPrTe,
               author = "Ramos, Jonathan da Silva and Watanabe, Carolina Yukari Veludo and 
                         Traina, Agma Juci Machado",
          affiliation = "{University of S{\~a}o Paulo} and {Federal University of 
                         Rond{\^o}nia} and {University of S{\~a}o Paulo}",
                title = "FOMP: A Novel Preprocessing Technique to Speed-Up the Outlier 
                         Removal from Matched Points",
            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 = "Feature Point Matching, Outliers Removal, Filtering, Graph-based 
                         Approach.",
             abstract = "Image matching plays a major role in many applications, including 
                         pattern recognition and biomedical imaging. It encompasses three 
                         steps: 1) interest point selection; 2) feature extraction from 
                         each interest point; 3) features point matching. For steps 1 and 
                         2, traditional interest point detectors/extractors have worked 
                         well. However, for step 3 even a few points incorrectly matched 
                         (outliers), might lead to an undesirable result. State-of-the-art 
                         consensus algorithms present a high time cost as the number of 
                         outlier increases. Aimed at overcoming this problem, we present 
                         FOMP, a novel preprocessing approach, that reduces the amount of 
                         outliers in the initial set of matched points by filtering out the 
                         vertices that present a higher difference among their edges in a 
                         complete graph representation of the points. The precision of 
                         traditional methods is kept, while the time is speed up in 50%. 
                         The approach removes, in average, more than 65% of outliers, while 
                         keeping over 98% of the inliers.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.039",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.039",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M5BFUB",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M5BFUB",
           targetfile = "41.pdf",
        urlaccessdate = "2024, May 03"
}