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		<doi>10.1109/SIBGRAPI.2016.039</doi>
		<citationkey>RamosWataTrai:2016:NoPrTe</citationkey>
		<title>FOMP: A Novel Preprocessing Technique to   Speed-Up the Outlier Removal from Matched Points</title>
		<format>On-line</format>
		<year>2016</year>
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		<author>Ramos, Jonathan da Silva,</author>
		<author>Watanabe, Carolina Yukari Veludo,</author>
		<author>Traina, Agma Juci Machado,</author>
		<affiliation>University of São Paulo</affiliation>
		<affiliation>Federal University of Rondônia</affiliation>
		<affiliation>University of São Paulo</affiliation>
		<editor>Aliaga, Daniel G.,</editor>
		<editor>Davis, Larry S.,</editor>
		<editor>Farias, Ricardo C.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Gibson, Stuart J.,</editor>
		<editor>Giraldi, Gilson A.,</editor>
		<editor>Gois, João Paulo,</editor>
		<editor>Maciel, Anderson,</editor>
		<editor>Menotti, David,</editor>
		<editor>Miranda, Paulo A. V.,</editor>
		<editor>Musse, Soraia,</editor>
		<editor>Namikawa, Laercio,</editor>
		<editor>Pamplona, Mauricio,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Santos, Jefersson dos,</editor>
		<editor>Schwartz, William Robson,</editor>
		<editor>Thomaz, Carlos E.,</editor>
		<e-mailaddress>jonathansramos@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)</conferencename>
		<conferencelocation>São José dos Campos, SP, Brazil</conferencelocation>
		<date>4-7 Oct. 2016</date>
		<publisher>IEEE Computer Society´s Conference Publishing Services</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>Feature Point Matching, Outliers Removal, Filtering, Graph-based Approach.</keywords>
		<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.</abstract>
		<language>en</language>
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		<usergroup>jonathansramos@gmail.com</usergroup>
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