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<datestamp>2018-01-11</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Recognize multi-touch gestures by graph modeling and matching</title>
<creator>Chen, Zhaoxin</creator>
<creator>Anquetil, Eric</creator>
<creator>Mouchère, Harold</creator>
<creator>Viard-Gaudin, Christian</creator>
<contributor>Institut National des Sciences Appliquées - Rennes (INSA Rennes)</contributor>
<contributor>Institut de Recherche en Communications et en Cybernétique de Nantes (IRCCyN) ; Mines Nantes (Mines Nantes) - École Centrale de Nantes (ECN) - Ecole Polytechnique de l'Université de Nantes - PRES Université Nantes Angers Le Mans [UNAM] - Centre National de la Recherche Scientifique (CNRS)</contributor>
<description>International audience</description>
<source>17th Biennial Conference of the International Graphonomics Society</source>
<coverage>Pointe-à-Pitre, Guadeloupe</coverage>
<contributor>International Graphonomics Society (IGS)</contributor>
<contributor>Université des Antilles (UA)</contributor>
<contributor>Céline Rémi</contributor>
<contributor>Lionel Prévost</contributor>
<contributor>Eric Anquetil</contributor>
<identifier>hal-01165768</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01165768</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01165768/document</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01165768/file/IGS_2015_submission_6.pdf</identifier>
<source>https://hal.univ-antilles.fr/hal-01165768</source>
<source>Céline Rémi; Lionel Prévost; Eric Anquetil. 17th Biennial Conference of the International Graphonomics Society, Jun 2015, Pointe-à-Pitre, Guadeloupe. 2015, Drawing, Handwriting Processing Analysis: New Advances and Challenges</source>
<language>en</language>
<subject lang=en>multi-touch</subject>
<subject lang=en>SVM</subject>
<subject>[INFO] Computer Science [cs]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>Extract the features for a multi-touch gesture is difficult due to the complex temporal and motion relations between multiple trajectories. In this paper we present a new generic graph model to quantify the shape, temporal and motion information from multi-touch gesture. To make a comparison between graph, we also propose a specific graph matching method based on graph edit distance. Results prove that our graph model can be fruitfully used for multi-touch gesture pattern recognition purpose with the classifier of graph embedding and SVM.</description>
<date>2015-06-21</date>
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