本論文係研究應用OpenGL (Open Graphics Library) 於居家服務機器人之抗視角變換SIFT (Scale-Invariant Feature Transform) 即時影像處理法。首先本論文提出一個六步驟的資料庫建立方法，SIFT演算法在偵測這個資料庫中的圖片特徵時，錯誤特徵被偵測之情況可降低。也可以增加手動建立資料庫之時間。本論文的第二部份介紹使用Affine-SIFT演算法完成之物件辨識系統，並加入OpenGL與SiftGPU平行化技術，OpenGL平行化了Affine投影的運算過程，SiftGPU則平行化了SIFT演算法，最終完成具有能力即時運算之影像辨識系統。本論文的第三部份描述如何完成人臉辨識系統，該系統結合了Haar-Like人臉偵測、特徵臉辨識與骨架偵測。使用骨架偵測與Haar-Like人臉偵測技術，排除偵測錯誤之情況，加入五官偵測，校正人臉辨識因攝影機視角度造成之傾斜，提高人臉辨識之準確度。本論文第四部份描述OpenGL完成的人機介面，該介面整合各種感測器資訊，用立體的方式呈現在人機介面上，使操作者可以快速得知觀測資訊。最後，將本論文提出的影像辨識系統應用於居家服務型機器人，並透過實驗結果與2012 RoboCup@Home日本公開賽的比賽成果證明本系統之可行性與強健性。

This thesis mainly discusses the OpenGL (Open Graphics Library) based affine SIFT (Scale-Invariant Feature Transform) real-time image processing method for home service robots. Firstly, a six-step method is proposed to create image database. When SIFT algorithm is adopted to detect features with this database, the rate of error features is reduced. Integration of the six-step method and SIFT can significantly decrease time for setting up the database. Secondly, the object recognition vision system is built up by parallel Affine-SIFT (ASIFT) algorithm, where OpenGL reduces the calculation time of affine projection and SiftGPU (Graphics Processing Unit) accomplishes the real-time operation speed of SIFT. Thirdly, the human recognition vision system is established by combining face detection of Haar-Like features method, the face recognition of Eigenfaces method and human skeleton detection. Integration of human skeleton and face detection lowers the false rate of facial detection. Adding facial organs detection augments the identification rate of face recognition. Furthermore, a user friendly human-computer interface with OpenGL is constructed and described. Operators can easily obtain the information from the interface such as dimensional image of home environment, values of sensors, and the recognition result of vision system. Finally, both the experimental results in the laboratory and competition consequents of 2012 Robocup@home Japan Open demonstrate the validity, effectiveness, and robustness of the proposed real-time vision system on our home service robot.

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