[1]朱兴龙,曹毓,马倩,等.斑点轮廓中心坐标值鲁棒性研究及去噪算法实现[J].郑州大学学报(工学版),2021,42(04):63-69.[doi:10.13705/j.issn.1671-6833.2021.04.024]
　Zhu Xinglong,Cao Yu,Ma Qian,et al.Robustness Research of Center Coordinate Value for Speckle Contour and Denoising Algorithm Implementation[J].Journal of Zhengzhou University (Engineering Science),2021,42(04):63-69.[doi:10.13705/j.issn.1671-6833.2021.04.024]
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斑点轮廓中心坐标值鲁棒性研究及去噪算法实现()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42卷
期数:
2021年04期
页码:
63-69
栏目:
出版日期:
2021-07-30

文章信息/Info

Title:
Robustness Research of Center Coordinate Value for Speckle Contour and Denoising Algorithm Implementation
作者:
朱兴龙; 曹毓; 马倩; 尹珺瑶
扬州大学机械工程学院;

Author(s):
Zhu Xinglong; Cao Yu; Ma Qian; Yin Yiyao;
School of Mechanical Engineering, Yangzhou University;

关键词:
Keywords:
DOI:
10.13705/j.issn.1671-6833.2021.04.024
文献标志码:
A
摘要:
激光点照射在被测物体表面上，经相机成像一般为椭圆斑点,斑点中心坐标值的鲁棒性直接影响深度测量的精度。由于受被测表面粗糙度等缺陷影响,其斑点轮廓出现散射,经图像处理后的轮廓出现噪点。当采用带轮廓噪点的数据拟合斑点中心时,斑点中心坐标值将受轮廓噪点的影响,鲁棒性较差。本文阐述了单目视觉与激光点复合进行深度测量的原理,分析了斑点轮廓噪点产生的原因,提出了基于统计学原理确定内敛外扩椭圆边界剔除轮廓噪点的方法和去噪算法。采用两步法验证所提算法的中心坐标值鲁棒性,第一步采用理想数据,即通过已知斑点椭圆,加入噪点验证了算法能够收敛到已知的椭圆中心,且该算法优于其他典型算法,具有较好的鲁棒性第二步通过图像测量系统采集测量表面的不同深度的实际图像,获取斑点轮廓的中心坐标值,采用拟合方法得到测量表面深度信息与斑点轮廓中心坐标值的关系模型,以测量表面不同位置的实测数据和关系模型的解算数据比较,结果表明本算法测量的精度优于其他算法,间接验证了本算法在去噪后的斑点中心更加接近理论斑点中心,具有较好的鲁棒性。
Abstract:
The combination of monocular vision and laser spot can measure the depth information. The laser spot is irradiated on the surface of the ob＜x＞ject to be measured, after imaging by CCD camera, it is generally elliptical spot, the accuracy of the center coordinate value (CCV) of the speckle will effect of the depth information. Due to the influence of surface roughness, surface bumps, pits and other defects to be measured, the edge of the imaging speckle is scattered, resulting in noise in the contour after image processing. When this data with contour noise is used to fit the spot center, the CCV of the speckle will be affected by the contour noise. The principle of depth information measurement by monocular vision and laser point combination was expounded, the causes of speckle contour noise was analyzed, a method to eliminate contour noise ba＜x＞sed on statistical principle at introverted and extroverted ellipse boundaries was proposed, two-step method is adopted to verify the robustness of the CCV of the proposed algorithm. In the first step, ideal data is adopted, that is, the known speckle ellipse is added with various noise points to verify that the algorithm can converge to the known ellipse center, and the proposed algorithm is superior to other typical algorithms and has better robustness. In the second step, the actual images of different depths of the measured surface are collected by the image measuring system to obtain the CCV of the speckle contour. The relationship model between the depth information of the measured surface and the CCV of the speckle contour is obtained by the fitting method. The comparison between the measured data of different positions of the measured surface and the calculated data of the relationship model shows that the measurement accuracy of the algorithm is better than other algorithms, which indirectly verifies that the speckle center of the algorithm after denoising is closer to the theoretical speckle center and has better robustness.
更新日期/Last Update: 2021-08-26