| Summary: | 碩士 === 國立臺灣師範大學 === 資訊工程研究所 === 98 === Recently, gesture recognition is an important and interesting research issue in the area of computer vision. Typical applications include intelligent surveillance systems,security activity analysis, precise analysis of athletic performance, and automatic virtual director, etc. Moreover, a somatosensory control is a newly idea, which is based on gesture recognition techniques. People could control the object in the screen without using any controller just like using a huge touch screen. In view of lectures use slides as presentation interface could affected by the projector and lectures are limited to stay the computer table, we proposed a gesture recognition system apply in presentation control.
Most of traditional gesture recognition methods use Hidden Markov Model (HMM), which based on the finite state machine, perform well only in the well observation of the object. To remove the restriction, we present a supervised learning method by Support Vector Machine (SVM) in this thesis. The SVM classifier is trained and learned features from users. Moreover, without using dilation and erosion
algorithm to reduce noise from the input image, we proposed Grid Motion Detection method to improve system performance and also reduce noise affected.
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