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dc.contributor.authorPaulraj, Murugesa Pandiyan, Prof. Dr.
dc.contributor.authorSazali, Yaacob, Prof. Dr.
dc.contributor.authorHazry, Desa, Assoc. Prof. Dr.
dc.contributor.authorWan Mohd Ridzuan, Wan Ab Majid
dc.date.accessioned2014-04-15T04:02:40Z
dc.date.available2014-04-15T04:02:40Z
dc.date.issued2009
dc.identifier.citation5th International Colloquium on Signal Processing & Its Applications, 2009, pages 19-22en_US
dc.identifier.isbn978-1-4244-4151-8
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/33703
dc.descriptionLink to publisher's homepage at http://ieeexplore.ieee.org/en_US
dc.description.abstractThis paper presents simple methods for translating Kod Tangan Bahasa Melayu (KTBM) into voice signal based on subject head and two hand gestures. Different gesture signs made by different subjects are captured using a USB web camera in RGB video stream format with a screen bit depth of 24 bits and a resolution of 320 X 240 pixels. The recorded video of the sign language is divided into number of image frames. Using a simple segmentation technique, the frame image is segmented into three region namely, head region, left hand region and right hand region. After performing the image segmentation, the image frames are converted into binary image format. A simple feature extraction method is then applied and the variations of the features in the subsequent frame are modeled using Discrete Cosine Transform (DCT). The features extracted are associated to the equivalent voice sound and a simple neural network model trained by error prob method is developed. An audio system is used to play the equivalent voice signal from the recognized sign language. Experimental results demonstrate that the recognition rate of the proposed neural network models is about 81.07%.en_US
dc.language.isoenen_US
dc.publisherIEEE Conference Publicationsen_US
dc.subjectSign language recognitionen_US
dc.subjectHead and hand gesturesen_US
dc.subjectDiscrete Cosine Transform (DCT)en_US
dc.subjectNeural networken_US
dc.titleGesture recognition system for Kod Tangan Bahasa Melayu (KTBM) using neural networken_US
dc.typeWorking Paperen_US
dc.identifier.urlhttp://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5069179&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5069179
dc.identifier.urlhttp://dx.doi.org/10.1109/CSPA.2009.5069179
dc.contributor.urlpaul@unimap.edu.my.en_US
dc.contributor.urls.yaacob@unimap.edu.myen_US
dc.contributor.urlhazry@unimap.edu.myen_US


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