SOMM: Self organizing Markov map for gesture recognition

Caridakis, G; Karpouzis, K; Drosopoulos, A; Kollias, S

dc.contributor.author	Caridakis, G	en
dc.contributor.author	Karpouzis, K	en
dc.contributor.author	Drosopoulos, A	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T01:34:38Z
dc.date.available	2014-03-01T01:34:38Z
dc.date.issued	2010	en
dc.identifier.issn	0167-8655	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20780
dc.subject	Gesture recognition	en
dc.subject	Gesture-based interaction	en
dc.subject	Markov models	en
dc.subject	Self organizing maps	en
dc.subject	Spatiotemporal pattern recognition	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	Appropriate distances	en
dc.subject.other	Classification mechanism	en
dc.subject.other	Gesture-based interaction	en
dc.subject.other	Hand gesture	en
dc.subject.other	Markov map	en
dc.subject.other	Markov models	en
dc.subject.other	Optimal trajectories	en
dc.subject.other	Processing speed	en
dc.subject.other	Proposed architectures	en
dc.subject.other	Real time	en
dc.subject.other	Recognition process	en
dc.subject.other	Self organizing	en
dc.subject.other	Self organizing feature maps	en
dc.subject.other	Spatiotemporal information	en
dc.subject.other	Spatiotemporal pattern recognition	en
dc.subject.other	Video sequences	en
dc.subject.other	Feature extraction	en
dc.subject.other	Markov processes	en
dc.subject.other	Self organizing maps	en
dc.subject.other	Video recording	en
dc.subject.other	Gesture recognition	en
dc.title	SOMM: Self organizing Markov map for gesture recognition	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.patrec.2009.09.009	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.patrec.2009.09.009	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	Present work introduces a probabilistic recognition scheme for hand gestures. Self organizing feature maps are used to model spatiotemporal information extracted through image processing. Two models are built for each gesture category and, along with appropriate distance metrics, produce a validated classification mechanism that performs consistently during experiments on acted gestures video sequences. The main focus of current work is to tackle intra and inter user variability during gesture performance by adding flexibility to the decoding procedure and allowing the algorithm to perform an optimal trajectory search while the processing speed of both the feature extraction and the recognition process indicate that the proposed architecture is appropriate for real time and large scale lexicon applications. (C) 2009 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Pattern Recognition Letters	en
dc.identifier.doi	10.1016/j.patrec.2009.09.009	en
dc.identifier.isi	ISI:000272518000007	en
dc.identifier.volume	31	en
dc.identifier.issue	1	en
dc.identifier.spage	52	en
dc.identifier.epage	59	en