姿勢辨識在電腦視覺領域中，是項越來越重要的議題，舉凡是在監控、安全照護、運動員姿勢分析、自動後製影片等的應用越來越廣，然而近年來更將姿勢辨識提升成體感操控的重要發展，人們只需站在攝影機前就能操控畫面中的物體，就像是超大型的觸控螢幕，而有鑒於老師們在教學過程使用投影片時，無法適時的站在投影布幕前提示重點，必須侷限在講桌前操控電腦，我們提出將姿勢辨識的操控應用在教學過程中最常使用的投影片切換頁控制上。
一般研究姿勢辨識的議題上常見的方法是利用有限狀態機(Finite State Machine，FSM)最具代表性的就是隱馬爾可夫模型(Hidden Markov Model，HMM)，但利用FSM為基礎的辨識方式需要隨時了解觀察物體處於何種狀態中，而容易造成錯誤累積的情況，而本研究先定義指令動作所搭配的操作指令，利用監督式學習(supervised learning)的方式，藉由學習使用者做出不同指令動作的特徵，再以支持向量機(Support Vector Machine，SVM)分類器執行辨識動作，同時為了達到即時辨識的效果，避免一般在處理雜訊時較常使用的膨脹(dilation)和腐蝕(erosion)演算法會造成運算上的負擔，本研究運用網格移動偵測(Grid Motion Detection)的方法，同時避免雜訊的干擾也能偵測物體移動的情況，分類器有更好的辨識效果。

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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