基于NCCO的十字图像特征亚像素定位

利用标准互相关算子(NCCO)对机器视觉领域常用的十字图像特征进行匹配运算,发现相似函数在峰值区域的分布特征为4个互相对称的双曲面.据此提出一种基于NCCO图像匹配的双曲面拟合亚像素精密定位方法:首先进行粗定位,确定整像素级的相似函数峰值点;然后使用伪逆法对峰值区域的4个卦限分别进行最小二乘拟合;最后求解4个拟合双曲面的交点,得到图像特征的亚像素定位坐标.在微装配显微视觉系统上实现了该算法并进行了实验验证.实验表明:与常用的高斯拟合或二次曲面拟合方法相比,双曲面拟合方法在定位精度上为0.25个像素,而计算量大为减少.

Locating cross-shaped image feature with subpixel accuracy based on NCCO

Normalized cross correlation operator (NCCO) was used for image matching to localize popular cross-shaped features in machine vision. Distribution feature of similarity function around peak zone was found to be four symmetry hyperboloid planes(FSHP). An approach of subpixel localization based on hyperboloid fitting algorithm (HFA) was presented according to the character of FSHP. A coarse pattern position was localized by one-dimension searching in x and y direction respectively to reduce the computational complexity. The discrete NCCO points around the coarse position were fitted to four hyperboloid planes in four quadrants respectively through least square fitting method. The point of four hyperboloid intersection was adopted as subpixel localization position. The HFA was realized on a microscopy vision system of MEMS device micro-assembly work-cell. Experimental results show that the subpixel localization precision of HFA can reach 0.25 pixel. It has greatly decreased computative cost under the comparisons with conicoid fitting algorithm and Gaussian fitting algorithm.