- 學位論文
基於梯度向量相似度之角點金字塔影像定位
Corner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity
摘要
本論文提出一個以角點為基底的影像定位演算法。此演算法主要包含了兩個階段：(1)訓練階段、(2)匹配階段。在訓練階段中，首先採用改良式Harris角點偵測演算法對模板影像進行角點偵測，接著利用這些角點資訊建立具有強健性的角點金字塔。在匹配階段中，與訓練階段相同，採用修正型Harris角點偵測演算法與角點金字塔進行檢測影像資料之建立，接著將兩金字塔影像透過梯度向量積的方式進行相似度計算完成影像定位，最後拋物線擬合法進行估測更加精準的旋轉角。根據實驗結果顯示本論文所提出的角點定位演算法在效能分析中，較傳統以邊緣基底定位演算法優異，同時對於光源變化及雜訊干擾也具有相當足夠的強健性；精度部分，在本演算法能媲美傳統邊緣基底的影像定位演算法，基於以上幾點，證明本論文所提出的演算法無論在執行速度、精度、強健性皆具相當的水準。
並列摘要
A corner-based image alignment algorithm based on the procedures of corner-based template matching is presented in this study. This algorithm consists of two stages: training and matching. In the matching phase, the corners are obtained using Harris corner detection algorithm which is better than intuitive corner detection by experiment. These corners are then used to build the pyramid images. In the matching phase, the corners are obtained using the same corner detection algorithm. The similarity measure is then determined by the differences of gradient vector between the corners obtained in the template image and the inspection image, respectively. Furthermore, it further applied the refined function to evaluate the geometric relationship between the template and the inspection images. Results show that the corner-based template matching outperforms the original edge-based template matching in efficiency, and both of them are robust against lighting changes and noise.