現今，絕大多數的人機介面都是透過鍵盤、滑鼠或遙控器等接觸式裝置來進行控制。然而對於人類而言，手勢的使用是最自然的表達方式。手勢辨識系統的發展就是希望透過影像處理的方法，讓系統能夠自動對攝影機所擷取的影像進行手部之偵測，並進一步分析手勢動作所代表的意義。
目前在手勢辨識的相關研究上，大部分都是必須把鏡頭架設於手部前面，距離需要非常近，因此很難應用於智慧型系統之中，且在實際生活中很少有這樣的情況發生也不方便。通常使用者會離攝影機有一段距離，所以本論文將探討攝影機影像中包含使用者上半身與手部。首先我們要移除不感興趣的區塊，例如身體、臉部和其它雜訊，然後利用影像處理的相關演算法抓取出手部。接著，再對抓取出的手部使用極座標轉換辨識、Hausdorff 距離辨識和樣板比對辨識，當作手部姿勢辨識的方法，最後利用分別給予權重的方式，可以提高手姿的辨識率。經由實驗結果顯示，本論文所提出的系統能成功在即時處理中達成，且有好的辨識率。

Nowadays, we use most of human-computer interfaces like keyboards, mice or remote controls as command input devices. However, gesture is considered as one of the most natural ways for human to communicate with other people or to issue commands. Generally, the gesture recognition system is designed based on image processing to detect human hands and also recognize the gesture automatically.
Now, most researches about hand gesture recognition revealed that the camera needs to be set in front of hands, with a short distance limitation. However, it is difficult to apply in an intelligent system and inconvenient in practice. Usually, users keep a distance from the camera. Thus, our study focuses on the image including both users' upper body part and hands. First, we need to remove unnecessary area, such as body part, face, or noise. We make use of related image processing algorithms to extract image of hands. Second, we develop polar transformation recognition, Hausdorff distance recognition, and template matching recognition as gesture recognition methods. By weighting these three methods, the gesture recognition rate can be improved. The experimental results show that our proposed method can be accomplished successfully in real-time with a good recognition rate.

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