The efficacy of the Eigenvector approach to South African sign language identification

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dc.contributor.advisorConnan, James
dc.contributor.authorSegers, Vaughn Mackman
dc.contributor.otherDept. of Computer Science
dc.contributor.otherFaculty of Science
dc.date.accessioned2013-12-04T12:19:59Z
dc.date.accessioned2024-10-30T14:00:46Z
dc.date.available2011/02/21 11:30
dc.date.available2011/02/21
dc.date.available2013-12-04T12:19:59Z
dc.date.available2024-10-30T14:00:46Z
dc.date.issued2010
dc.descriptionMasters of Scienceen_US
dc.description.abstractThe communication barriers between deaf and hearing society mean that interaction between these communities is kept to a minimum. The South African Sign Language research group, Integration of Signed and Verbal Communication: South African Sign Language Recognition and Animation (SASL), at the University of the Western Cape aims to create technologies to bridge the communication gap. In this thesis we address the subject of whole hand gesture recognition. We demonstrate a method to identify South African Sign Language classifiers using an eigenvector ap- proach. The classifiers researched within this thesis are based on those outlined by the Thibologa Sign Language Institute for SASL. Gesture recognition is achieved in real- time. Utilising a pre-processing method for image registration we are able to increase the recognition rates for the eigenvector approach.en_US
dc.description.countrySouth Africa
dc.identifier.urihttps://hdl.handle.net/10566/16950
dc.language.isoenen_US
dc.publisherUniversity of the Western Capeen_US
dc.rights.holderUniversity of the Western Capeen_US
dc.subjectImage processingen_US
dc.subjectDigital techniquesen_US
dc.subjectEigenvectorsen_US
dc.subjectOptical pattern recognitionen_US
dc.subjectSign languageen_US
dc.titleThe efficacy of the Eigenvector approach to South African sign language identificationen_US
dc.typeThesisen_US

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