基于统计高斯拟合的圆形光斑中心定位方法

Circular Spot Center Location Method Based on Statistical Gaussian Fitting

针对二维高斯曲面拟合算法定位精度不高的问题,提出一种基于统计高斯拟合算法,可用于圆形光斑中心的高精度定位。结合二维高斯曲面的分布特性,对圆形光斑的灰度分布进行分析,指出了在对圆形光斑灰度拟合时需对光斑像素点进行有效筛选,避免因光照或噪声等带来的拟合误差。针对有效圆形光斑像素点的筛选方法进行了研究和分析,提出一种简单有效的方法。该方法根据边缘检测算子得到圆形光斑边界,再利用形态学算法对圆形边界点进行扩充,得到圆形光斑的扩展边界。结合圆形光斑目标区域灰度信息和非光斑背景区域灰度信息,利用统计学方法进一步筛选,获得了最终光斑灰度拟合像素点。与传统的高斯曲面拟合算法相比,仿真实验、运行时间测试实验和实物实验验证了该方法的优势。该方法能有效应用于工业领域圆形光斑中心定位中,具有很好的实际应用价值。

A method for circular spot center location is proposed to solve the problem of low location accuracy of 2D Gaussian fitting method.Based on the 2D Gaussian curve distribution and the gray distribution of circular spots,the object for 2D Gaussian fitting should be selected effectively to avoid the fitting errors caused by light and other noises.An effective and simple method for this selection was proposed through research and analysis.The edge points detected by the edge detector were extended by morphology method,which was the extended edge.Combined with the gray information of both the target area of circular spot and background area of nonspot,the final gray fitting pixels were obtained by fine selection of the extended edge.Compared with the traditional Gaussian surface fitting algorithm,simulation experiments,running time test experiments and actual experiments verify the advantages of the proposed method.The proposed method can be effectively applied to the circular spot center location in the industrial field,and has good practical application value.