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		<doi>10.1109/SIBGRAPI.2016.024</doi>
		<citationkey>DazziCampHiltCesa:2016:EfObRe</citationkey>
		<title>Efficient object recognition using sampling of keypoint triples and keygraph structure</title>
		<format>On-line</format>
		<year>2016</year>
		<numberoffiles>1</numberoffiles>
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		<author>Dazzi, Estephan,</author>
		<author>De Campos, Teófilo,</author>
		<author>Hilton, Adrian,</author>
		<author>Cesar Jr., Roberto Marcondes,</author>
		<affiliation>Instituto de Matemática e Estatística - Universidade de São Paulo</affiliation>
		<affiliation>CVSSP - University of Surrey</affiliation>
		<affiliation>CVSSP - University of Surrey</affiliation>
		<affiliation>Instituto de Matemática e Estatística - Universidade de 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>estefhan@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>Local image feature matching, semi-local graph matching, graph topological properties.</keywords>
		<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.</abstract>
		<language>en</language>
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		<usergroup>estefhan@gmail.com</usergroup>
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