A Vision-Based Human Hand Gesture Recognition Interface For Image Browsing Application

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dc.contributor.authorChan, Lih Yang
dc.date.accessioned2018-07-02T01:56:41Z
dc.date.available2018-07-02T01:56:41Z
dc.date.issued2009-12
dc.description.abstractComputer mouse has been an efficient input device. However, the mouse usage limits user’s freedom. Besides, the devices are easily contaminated with bacteria and spreading disease among users. The contactless vision-based hand gesture recognition is one of the solutions to the freedom and hygiene problem. But it faces challenges of usability in term of cost and environmental variation like lighting. This thesis proposes and implements hand gesture recognition methods in image browsing application, to allow users views pictures contactless from input device in real time. The lower level of the approach implements the posture recognition with Viola-Jones object detection method that utilizes Haar-like features and the AdaBoost learning algorithm. With this algorithm, real-time performance and high recognition accuracy up to 94% detection rate can be obtained. The application system yield average of 89% successful input command in a series of evaluation. Moreover, the application requires only common PC and webcam to address the concern of deployment cost. To further enhance the speed of hand detection in real-time application, an idea to reduce the area of search window by incorporating skin colour segmentation is proposed in this thesis. A reduction of 19% of processing time is achieved with the proposed method, comparing to the processing time without skin colour segmentation. In addition, the re-training feature in the application enables users to update the classifier easily whenever needed.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/5833
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectA vision-based human hand gesture recognition interfaceen_US
dc.subjectfor image browsing applicationen_US
dc.titleA Vision-Based Human Hand Gesture Recognition Interface For Image Browsing Applicationen_US
dc.typeThesisen_US
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