圆形标记点中心识别是视觉测量的关键技术之一. 为满足工业现场精确标定和测量的需求，提出了一种鲁棒的标记点中心提取算法. 首先对图像进行预处理和Canny边缘提取；然后用增强的Snake方法对边缘进行全局最优搜索，并以具备积分性质的Zernike算子对图像边缘进行亚像素定位；最后采用鲁棒的椭圆拟合算法迭代定位椭圆中心. 仿真和实测表明，该算法在标记点破损或被污染情况下仍能给出精确的中心定位. 理想情况下定位误差小于0.02像素，在标记点破损或被污染情况下定位误差小于0.03像素.

 Detection of artificial circular target center is a key in 3D measurements. In order to get accurate calibration and measurement in industrial fields, a robust algorithm for circular target center detection is proposed. Images with circular target are first preprocessed, and Canny edges extracted. The canny edges
are then refined with an improved Snake algorithm, and sub-pixel edges are found using a Zernike operator. Accurate centers are obtained through robust ellipse fitting. Compared with traditional methods, the proposed algorithm can give accurate center detect results even if the target is damaged or soiled. Simulation results and real data show that the center detection error is less than 0.02 pixel in ideal cases, and less than 0.03 pixel when the target is damaged.