Online gesture-based interaction with visual oriental characters based on manifold learning

Wang, Y; Liu, J; Fan, X; He, X; Jia, Q; Gao, R

Online gesture-based interaction with visual oriental characters based on manifold learning

Wang, Y Liu, J Fan, X He, X

Jia, Q Gao, R

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Publication Type:: Journal Article
Citation:: Signal Processing, 2015, 110 pp. 123 - 131
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Liu, J	en_US
dc.contributor.author	Fan, X	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Jia, Q	en_US
dc.contributor.author	Gao, R	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Signal Processing, 2015, 110 pp. 123 - 131	en_US
dc.identifier.issn	0165-1684	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34093
dc.description.abstract	© 2014 Elsevier B.V. Online gesture-based interaction with characters has become a more natural and informative human-computer interface with the popularity of new interactive devices (e.g., Kinect and Leap Motion). In this paper, a new feature descriptor named Segmented Directed-edge Vector (SDV) is proposed. This simple and yet quite effective descriptor is able to capture the characteristics of visual oriental characters. Moreover, we explicitly build the mappings from SDVs to features in a subspace by a modified Locality Preserving Projections (LPP) method with stroke class constraints. These mappings can yield meaningful subspace structures for larger character sets. Extensive experiments on the online interactive system demonstrate the robustness of our method to various issues in gesture-based characters input, such as unnatural breaks, overlapped or distorted radicals, and unconscious or quivering trajectories. Our system can still achieve accurate recognition when accumulative errors occur with complex characters.	en_US
dc.relation.ispartof	Signal Processing	en_US
dc.relation.isbasedon	10.1016/j.sigpro.2014.08.042	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Online gesture-based interaction with visual oriental characters based on manifold learning	en_US
dc.type	Journal Article
utslib.citation.volume	110	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	080106 Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	110	en_US

Abstract:

© 2014 Elsevier B.V. Online gesture-based interaction with characters has become a more natural and informative human-computer interface with the popularity of new interactive devices (e.g., Kinect and Leap Motion). In this paper, a new feature descriptor named Segmented Directed-edge Vector (SDV) is proposed. This simple and yet quite effective descriptor is able to capture the characteristics of visual oriental characters. Moreover, we explicitly build the mappings from SDVs to features in a subspace by a modified Locality Preserving Projections (LPP) method with stroke class constraints. These mappings can yield meaningful subspace structures for larger character sets. Extensive experiments on the online interactive system demonstrate the robustness of our method to various issues in gesture-based characters input, such as unnatural breaks, overlapped or distorted radicals, and unconscious or quivering trajectories. Our system can still achieve accurate recognition when accumulative errors occur with complex characters.

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http://hdl.handle.net/10453/34093