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DC poleHodnotaJazyk
dc.contributor.authorSoares, Alan dos Santos
dc.contributor.authorApolinário Jr., Antonio L.
dc.contributor.editorSkala, Václav
dc.date.accessioned2017-08-31T11:14:12Z
dc.date.available2017-08-31T11:14:12Z
dc.date.issued2017
dc.identifier.citationJournal of WSCG. 2017, vol. 25, no. 1, p. 59-66.en
dc.identifier.issn1213-6972 (print)
dc.identifier.issn1213-6980 (CD-ROM)
dc.identifier.issn1213-6964 (on-line)
dc.identifier.uriwscg.zcu.cz/WSCG2017/!_2017_Journal_WSCG-No-1.pdf
dc.identifier.urihttp://hdl.handle.net/11025/26280
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesJournal of WSCGen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectgesta rukoucs
dc.subjectgeometrické modelovánícs
dc.subject3D klasifikace gestcs
dc.subjectakvizicecs
dc.titleReal-time 3D gesture recognition using dynamic time warping and simplification methodsen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe recognition of dynamic gestures of hands using pure geometric 3D data in real-time is a challenge. RGBD sensors simplified this task, giving an easy way to acquire 3D points and track them using the depth maps information. But use this collection of raw 3D points as a gesture representation in a classification process is prone to mismatches, since gestures of different people can vary in scale, location and velocity. In this paper we analyze how different techniques of simplification and regularization can provide more accurate representations of the gestures. Using Dynamic Time Warping (DTW) as the classification method, we show that the simplification and regularization steps can improve the recognition rate and also reduce the time of gesture recognition.en
dc.subject.translatedhands gestureen
dc.subject.translatedgeometric modelingen
dc.subject.translatedacquisitionen
dc.subject.translated3D gesture classificationen
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 25, Number 1 (2017)

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