Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/53651
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorFok, KY-
dc.creatorCheng, CT-
dc.creatorGanganath, N-
dc.date.accessioned2016-06-27T02:02:31Z-
dc.date.available2016-06-27T02:02:31Z-
dc.identifier.issn0271-4302-
dc.identifier.urihttp://hdl.handle.net/10397/53651-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.rightsThe following publication Fok, K. -., Cheng, C. -., & Ganganath, N. (2015). Live demonstration: A HMM-based real-time sign language recognition system with multiple depth sensors. Paper presented at the Proceedings - IEEE International Symposium on Circuits and Systems, ISCAS 2015, 1904 is available at 10.1109/ISCAS.2015.7169037en_US
dc.subjectHidden Markov modelsen_US
dc.subjectImage captureen_US
dc.subjectImage fusionen_US
dc.subjectImage motion analysisen_US
dc.subjectNatural language processingen_US
dc.subjectSign language recognitionen_US
dc.titleLive demonstration : a HMM-based real-time sign language recognition system with multiple depth sensorsen_US
dc.typeConference Paperen_US
dc.identifier.spage1904-
dc.identifier.doi10.1109/ISCAS.2015.7169037-
dcterms.abstractAutomatic sign language recognition plays an important role in communications for sign language users. Most existing sign language recognition systems use single sensor input. However, such systems may fail to recognize hand gestures correctly due to occluded regions of hand gestures. In this work, we propose a novel system for real-time recognition of the digits in American Sign Language (ASL) [1]. The proposed system [2] utilizes two Leap Motion sensors [3] to capture hand gestures from different angles. Sensory data are preprocessed using a multi-sensor data fusion approach and ASL digits are recognized in real-time from the fused data using Hidden Markov models (HMM) [4]. Experimental results of the proposed sign language recognition system demonstrate its improved performance over single sensor systems. With a low implementation cost and a high recognition accuracy, the proposed system can be widely adopted in many real world applications and bring conveniences to world-wide ASL users.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation2015 IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, Portugal, 24-27 May 2015, p. 1904-
dcterms.issued2015-
dc.identifier.scopus2-s2.0-84946208406-
dc.relation.conferenceIEEE International Symposium on Circuits and Systems [ISCAS]-
dc.identifier.rosgroupid2014001363-
dc.description.ros2014-2015 > Academic research: refereed > Refereed conference paper-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0020-n08en_US
dc.description.pubStatusPublisheden_US
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