%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2016/07.21.02.03
%2 sid.inpe.br/sibgrapi/2016/07.21.02.03.13
%@doi 10.1109/SIBGRAPI.2016.039
%T FOMP: A Novel Preprocessing Technique to Speed-Up the Outlier Removal from Matched Points
%D 2016
%A Ramos, Jonathan da Silva,
%A Watanabe, Carolina Yukari Veludo,
%A Traina, Agma Juci Machado,
%@affiliation University of São Paulo
%@affiliation Federal University of Rondônia
%@affiliation University of São Paulo
%E Aliaga, Daniel G.,
%E Davis, Larry S.,
%E Farias, Ricardo C.,
%E Fernandes, Leandro A. F.,
%E Gibson, Stuart J.,
%E Giraldi, Gilson A.,
%E Gois, João Paulo,
%E Maciel, Anderson,
%E Menotti, David,
%E Miranda, Paulo A. V.,
%E Musse, Soraia,
%E Namikawa, Laercio,
%E Pamplona, Mauricio,
%E Papa, João Paulo,
%E Santos, Jefersson dos,
%E Schwartz, William Robson,
%E Thomaz, Carlos E.,
%B Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)
%C São José dos Campos, SP, Brazil
%8 4-7 Oct. 2016
%I IEEE Computer Society´s Conference Publishing Services
%J Los Alamitos
%S Proceedings
%K Feature Point Matching, Outliers Removal, Filtering, Graph-based Approach.
%X 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.
%@language en
%3 41.pdf