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URN etd-0831109-121655 Statistics This thesis had been viewed 2564 times. Download 3287 times. Author Yao-De Chiu Author's Email Address No Public. Department Computer Science and Enginerring Year 2008 Semester 2 Degree Master Type of Document Master's Thesis Language zh-TW.Big5 Chinese Page Count 68 Title Virtual Multi-touch Screen Visual Hand Tracking by Single Camera Keyword virtual touch screen multi-touch trajectory tracking Hand gesture recognition Hand gesture recognition trajectory tracking multi-touch virtual touch screen Abstract In recent years, many hand gesture recognition systems have been developed to replace some traditional input devices. However, the skin color may look quite differently, depending on camera settings, illumination, shadows, people’s tans, and ethnic groups. To increase the accuracy of hand detection, systems deploying multiple cameras, infrared cameras, and 3D stereoscopic cameras are proposed. Furthermore, some restrictions are made such as lighting and uniform background. Still, the prices of these cameras are more expensive. In this paper, we present a virtual touch screen by visual hand tracking with single ordinary web camera. Firstly, human face is detected by haar–like textures. Face color sampling is used to build the adaptive skin color model for hand detection in unrestrictive environment such as complex background with moving objects. The person nearest to the camera is assumed to be the user whose hands are extracted and tracked. Secondly, extracted hands are represented by chain codes and analyzed to find the moving hand’s palm. The primary benefit of PCA arises from quantifying the importance of each dimension for describing the variability of a data set. Therefore, PCA is adapted to find the eigenvectors and the centroid of oval palm. The centroid of Palm corresponds to the cursor position in the window system. The eigenvectors and eigenvalues could be used to filter out non-palm moving objects. By detecting both palms of two hands, multi-touch interface can be simulated. I-Phone’s multi-touch hand gestures are recognized and accuracy rate is 95.1% in average. The stability of the cursor improved by motion analysis could achieve 94.9%. Touch recognition rate is 93.9% which demonstrate the feasibility of proposed system. Advisor Committee Chen-Chiung Hsieh - advisor
none - co-chair
none - co-chair
Files Date of Defense 2009-07-22 Date of Submission 2009-08-31