Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/70448
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dc.titleHand posture recognition via sparse representation
dc.contributor.authorCao, C.
dc.contributor.authorSun, Y.
dc.date.accessioned2014-06-19T03:12:14Z
dc.date.available2014-06-19T03:12:14Z
dc.date.issued2010
dc.identifier.citationCao, C.,Sun, Y. (2010). Hand posture recognition via sparse representation. APSIPA ASC 2010 - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference : 883-886. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/70448
dc.description.abstractWe propose a new method for hand gesture recognition via sparse representation. Initially, we present the region of the hand is detected based on skin color segmentation in the YCbCr color space and image normalization. Then, the recognition of hand posture is casted as the sparse representation of a test image with a set of the database images. The l1- minimization is applied to accurately and efficiently calculate the sparse representation so as to classify different postures under a variety of conditions. Experimental results demonstrate the effective and robust performance of the proposed method.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.sourcetitleAPSIPA ASC 2010 - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
dc.description.page883-886
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

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