多方向Scharr的齿轮边缘检测与中心定位
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摘要
针对齿轮精度检测设备结构较复杂、使用和维护较麻烦等问题,提出基于多方向Scharr的齿轮边缘检测与中心定位方法。通过高斯滤波减小噪声的影响,采用分别代表0°、22. 5°、45°、67. 5°、90°、112. 5°、135°、157. 5°共8个方向的5×5算子模板进行边缘检测,结合Otsu方法得到边缘二值化图像,并结合最小包围矩形法、最小包围圆形法和重心法求出齿轮中心坐标。实验结果表明,该算法能够准确快速地提取齿轮边缘和中心信息,在一定程度上减少了噪声的影响。
Since gear precision measuring equipment exists the complicated structure and difficulty in using and maintaining,an improved edge detection algorithm of multi-direction Scharr operator template in gear edge detection and center location is proposed. Firstly,Gaussian filter is applied to reduce the effect of noise. The algorithm based on eight 5 ×5 size templates of 0°,22. 5°,45°,67. 5°,90°,112. 5°,135°,157. 5°different direction is used to detect edges with the Otsu method. And the minimum encircling rectangle method,the minimum enclosing circular method and the gravity method are used to acquire the gear center coordinate. The results show that the improved algorithm has advantages of accurate and fast edge detection and center location,and reduction of the noise effect to a certain extent.
Since gear precision measuring equipment exists the complicated structure and difficulty in using and maintaining,an improved edge detection algorithm of multi-direction Scharr operator template in gear edge detection and center location is proposed. Firstly,Gaussian filter is applied to reduce the effect of noise. The algorithm based on eight 5 ×5 size templates of 0°,22. 5°,45°,67. 5°,90°,112. 5°,135°,157. 5°different direction is used to detect edges with the Otsu method. And the minimum encircling rectangle method,the minimum enclosing circular method and the gravity method are used to acquire the gear center coordinate. The results show that the improved algorithm has advantages of accurate and fast edge detection and center location,and reduction of the noise effect to a certain extent.
引文
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[2]曾亚君,何毅斌,李伟.基于改进形态学梯度的齿轮边缘检测[J].工具技术,2017,51(1):101-103.
[3]谭媛.遥感图像道路边缘检测与路面提取方法研究[D].湘潭:湘潭大学,2015.
[4]沈德海,侯建,鄂旭,等.基于Sobel的多方向算子模板边缘检测算法[J].现代电子技术,2015,38(4):91-93,97.
[5]陈观应.基于机器视觉的干电池缺陷并行检测方法研究[D].广州:广东工业大学,2016.
[6]初士博.光学相关探测中物面滤波方法的研究[D].长春:长春理工大学,2008.
[7]沈德海,鄂旭,张龙昌.一种简化卷积模板的抗噪型边缘检测算法[J].信息技术,2015(6):5-7,11.
[8]付玮,曾接贤.基于形状特征的图像检索技术研究[J].计算机技术与发展,2007(11):228-232.
[9]郑英娟,张有会,王志巍,等.基于八方向Sobel算子的边缘检测算法[J].计算机科学,2013,40(Z2):354-356.
[10]刘德磊.基于分形分析的图像质量评价[D].广州:华南理工大学,2014.
[11]闫凤,王瑞星,任立胜.小波变换在舰船边缘检测及尺寸测量中的应用[J].舰船科学技术,2016,38(10):70-72.