摘要
在激光辅助的视觉测量系统中,高斯拟合法的拟合数据选取影响激光光束中心提取精度。针对不均匀光照下激光图像难以确定高斯拟合数据数量的问题,文中提出一种基于随机抽样一致性算法的自适应高斯拟合方法。根据激光图像背景区域灰度分布的变化特性,采用随机抽样一致性算法估计背景区域灰度分布模型;将该模型的局外点作为拟合数据,从而利用高斯拟合法获得激光光束中心点的亚像素坐标。实验结果表明,该方法选取激光图像的高斯拟合数据平均拟合系数达到0.934,与固定拟合数据长度的高斯拟合法相比,提高了激光中心提取精度。
In laser-assisted vision measurement system,the selection of fitting data in Gauss fitting method affects the accuracy of laser light center extraction.Aiming at the difficulty to determine the number of Gaussian fitting data in laser images under uneven illumination,an adaptive Gaussian fitting method based on random sampling consistency algorithm was proposed in this paper.According to the characteristic of gray-scale variation in background region of laser image,the random sampling consistency algorithm was used to estimate the gray-scale distribution model of background region.The outliers of the model were extracted as Gaussian fitting data.Then,the Gaussian fitting method was used to obtain the sub-pixel coordinates of the center point of the laser light.The experimental results showed that the Gaussian fitting data in the laser image had an average fitting coefficient of 0.934,which improved the accuracy of laser light center extraction compared with the Gaussian fitting method for fixed fitting data length.
作者
章天昊
李郝林
ZHANG Tianhao;LI Haolin(School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《电子科技》
2020年第5期72-76,共5页
Electronic Science and Technology
基金
上海市科学技术委员会基金(17DZ2283300)。
关键词
结构光
激光中心
高斯拟合法
随机抽样一致性算法
图像处理
视觉测量
structured light
laser light center
Gaussian fitting method
random sample consensus algorithm
image processing
vision measurement
