摘要

针对采用高斯滤波器对图像进行滤波会导致图像边缘平滑,人为设定高、低阈值会导致阈值的自适应性差,采用双阈值法去除虚假边缘会导致去除效果不佳等问题,提出改进的Canny边缘检测算法并应用于影像测量领域。首先使用开关中值滤波代替高斯滤波,在去除噪声的同时保留非噪声像素点的灰度值不变,从而提高边缘定位精度;然后采用K-means聚类算法以得到高、低梯度值聚类中心,采用OTSU算法以得到梯度阈值,将两个方法结合,可以实现高、低阈值的自适应;最后采用面积形态学的方法去除图像的干扰边缘。实验结果表明,改进的算法具有定位精度高、自适应性强以及干扰点去除效果好等优点。

Abstract

In this study, the Canny edge detection algorithm is proposed and applied to the image measurement field to solve the problems of image edge smoothing due to Gaussian filtering, poor self-adaptability of the threshold caused by artificially setting high and low thresholds, and poor removal effect caused by using the double threshold method to remove the false edge. First, the switching median filter instead of the Gaussian filter is used. The gray value of non-noise pixels is kept unchanged while denoising to improve the edge positioning accuracy. Next, the K-means clustering algorithm is employed to obtain the clustering center of the high and low gradient values. The OTSU algorithm is employed to acquire the gradient threshold value. The self-adaptation of the high and low threshold values could be achieved by combining the two methods. Finally, the interference edge of the image is removed by area morphology. The experimental results show that the improved algorithm has the advantages of high positioning accuracy, strong self-adaptability, and good removal effect of disturbance points.

补充资料

中图分类号：TP391

DOI：10.3788/LOP57.241024

所属栏目：图像处理

基金项目：国家自然科学基金;

收稿日期：2020-05-25

修改稿日期：2020-07-01

网络出版日期：2020-12-01

作者单位    点击查看

联系人作者：张加朋(2439480318@qq.com)

备注：国家自然科学基金;

【1】Zhang W J, Yang Y L, Li D, et al. Key algorithms of automatic image measurement system [J]. Optics and Precision Engineering. 2007, 15(2): 294-301.
张舞杰, 杨义禄, 李迪, 等. 自动影像测量系统关键算法 [J]. 光学精密工程. 2007, 15(2): 294-301.

【2】Liu T, Liu Y, Liu K, et al. Intrusion object recognition and space distance measurement in safety area of high voltage transmission lines . [C]//2019 6th International Conference on Systems and Informatics (ICSAI), November 2-4, 2019, Shanghai, China. New York: IEEE. 2019, 878-883.

【3】Barkavi T, Chidambarathanu N. Processing digital image for measurement of crack dimensions in concrete [J]. Civil Engineering Infrastructures Journal. 2019, 52(1): 11-22.Barkavi T, Chidambarathanu N. Processing digital image for measurement of crack dimensions in concrete [J]. Civil Engineering Infrastructures Journal. 2019, 52(1): 11-22.

【4】Song L M, Li D P, Qin M C, et al. Research on high-precision hole measurement based on robot vision method [J]. Optoelectronics Letters. 2014, 10(5): 378-382.

【5】Duan Y X. Study on the measurement method of part size of automatic image measuring instrument [D]. Taiyuan: North University of China. 2019, 18-24.
段宇秀. 全自动影像测量仪零件尺寸测量方法研究 [D]. 太原: 中北大学. 2019, 18-24.

【6】Zheng Z, Zha B T, Xuchen Y S, et al. Adaptive edge detection algorithm based on grey entropy theory and textural features [J]. IEEE Access. 2019, 7: 92943-92954.Zheng Z, Zha B T, Xuchen Y S, et al. Adaptive edge detection algorithm based on grey entropy theory and textural features [J]. IEEE Access. 2019, 7: 92943-92954.

【7】Shang Y C, Chen J, Tian J W. Sub-pixel edge detection algorithm based on Gauss fitting [J]. Journal of Computer Applications. 2011, 31(1): 179-181.
尚雅层, 陈静, 田军委. 高斯拟合亚像素边缘检测算法 [J]. 计算机应用. 2011, 31(1): 179-181.

【8】Hua C J, Xiong X M, Chen Y. Feature extraction of workpiece circular arc contour based on sobel operator [J]. Laser & Optoelectronics Progress. 2018, 55(2): 021011.
化春键, 熊雪梅, 陈莹. 基于Sobel算子的工件圆弧轮廓特征提取 [J]. 激光与光电子学进展. 2018, 55(2): 021011.

【9】Duan S L, Yin C C, Li D W. Improved adaptive Canny edge detection algorithm [J]. Computer Engineering and Design. 2018, 39(6): 1645-1652.
段锁林, 殷聪聪, 李大伟. 改进的自适应Canny边缘检测算法 [J]. 计算机工程与设计. 2018, 39(6): 1645-1652.Duan S L, Yin C C, Li D W. Improved adaptive Canny edge detection algorithm [J]. Computer Engineering and Design. 2018, 39(6): 1645-1652.
段锁林, 殷聪聪, 李大伟. 改进的自适应Canny边缘检测算法 [J]. 计算机工程与设计. 2018, 39(6): 1645-1652.

【10】Shu Q L, Shan B. Sub-pixel localization algorithm based on modified Canny operator Machinery Design & Manufacture[J]. 0, 2018(10): 165-168.
舒启林, 山博. 改进Canny算子的亚像素定位算法 机械设计与制造[J]. 0, 2018(10): 165-168.

【11】Deng C X, Wang G B, Yang X R. Image edge detection algorithm based on improved Canny operator . [C]//2013 International Conference on Wavelet Analysis and Pattern Recognition, July 14-17, 2013, Tianjin, China. New York: IEEE. 2013, 168-172.

【12】Yuan Q Q, Zhang G, Chen X J, et al. Regularization of point cloud features by fusing improved canny algorithm [J]. Laser & Optoelectronics Progress. 2019, 56(16): 162801.
袁俏俏, 章光, 陈西江, 等. 融合改进Canny算法的点云特征规则化 [J]. 激光与光电子学进展. 2019, 56(16): 162801.

【13】Ju Z Y, Zhang W X, Zhai C Y. Garbage image edge detection based on improved Canny algorithm [J]. Electronic Science and Technology. 2020, 33(8): 16-20.
巨志勇, 张文馨, 翟春宇. 基于改进Canny算子的垃圾图像边缘检测 [J]. 电子科技. 2020, 33(8): 16-20.

【14】Liang Z J, Zhong J. Adaptive Canny algorithm improvement based on Otsu algorithm and histogram analysis [J]. Modern Electronics Technique. 2019, 42(11): 54-58.
梁肇峻, 钟俊. 基于Otsu算法与直方图分析的自适应Canny算法的改进 [J]. 现代电子技术. 2019, 42(11): 54-58.

【15】Rong W B, Li Z J, Zhang W, et al. An improved Canny edge detection algorithm . [C]//2014 IEEE International Conference on Mechatronics and Automation, August 3-6, 2014, Tianjin, China. New York: IEEE. 2014, 577-582.

【16】Chen X F, Guan H B, Gu J N, et al. improvements on Canny algorithm. Advanced Engineering Forum[J]. 2012, 6/7: 205-209.

【17】Liu L X, Li B W, Wang Y P, et al. Remote sensing image segmentation based on improved Canny edge detection [J]. Computer Engineering and Applications. 2019, 55(12): 54-58, 180.
刘丽霞, 李宝文, 王阳萍, 等. 改进Canny边缘检测的遥感影像分割 [J]. 计算机工程与应用. 2019, 55(12): 54-58, 180.

【18】Xu H Y, Xu X L, Zuo Y B. Applying morphology to improve Canny operator''''s image segmentation method [J]. The Journal of Engineering. 2019, 2019(23): 8816-8819.

【19】He Y B, Chen R L, Wu K, et al. Point cloud simplification method based on K-means clustering [J]. Laser & Optoelectronics Progress. 2019, 56(9): 091002.
贺一波, 陈冉丽, 吴侃, 等. 基于K-means聚类的点云精简方法 [J]. 激光与光电子学进展. 2019, 56(9): 091002.

【20】Xiong Z Y, Fan X P, Li Y. A new binary-area-morphology-based algorithm for license plate localization [J]. Computer Simulation. 2008, 25(11): 285-287, 296.
熊哲源, 樊晓平, 黎燕. 一种新的基于二值面积形态学的车牌定位算法 [J]. 计算机仿真. 2008, 25(11): 285-287, 296.

引用该论文

Zhang Jiapeng,Yu Fengqin. Improved Image Measurement Edge Detection Based on Canny Operator[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241024

张加朋,于凤芹. 基于Canny算子改进型的影像测量边缘检测[J]. 激光与光电子学进展, 2020, 57(24): 241024