融合形态学和Franklin矩的亚像素边缘检测方法*

doi:10.16731/j.cnki.1671-3133.2026.08.018

现代制造工程 ›› 2026, Vol. 551 ›› Issue (8): 132-137.doi: 10.16731/j.cnki.1671-3133.2026.08.018

• 仪器仪表/检测/监控 • 上一篇下一篇

融合形态学和Franklin矩的亚像素边缘检测方法^*

闫洪波, 余高超, 赵威, 王鑫辉

内蒙古科技大学机械工程学院,包头 014010

收稿日期:2025-10-16 出版日期:2026-08-18 发布日期:2026-09-01
作者简介:闫洪波,博士,教授,主要研究方向为机器视觉图像处理及工程应用。余高超,硕士研究生,主要研究方向为机器视觉图像处理和测量。E-mail:760996877@qq.com
基金资助:
^*内蒙古自然科学基金项目(2024MS05010)

A sub-pixel edge detection method that integrates morphological analysis and Franklin moments

YAN Hongbo, YU Gaochao, ZHAO Wei, WANG Xinhui

School of Mechanical Engineering,Inner Mongolia University of Science & Technology,Baotou 014010,China

Received:2025-10-16 Online:2026-08-18 Published:2026-09-01

摘要/Abstract

摘要： 对精密测量场景中的检测精度和执行效率进行了研究,提出了一种改进多结构元素形态学梯度和Franklin矩的亚像素边缘检测方法。首先,使用双边滤波对图像进行预处理,构建改进多结构元素形态学梯度滤波算子进行边缘粗定位;然后,通过自适应Otsu阈值分割和Franklin矩数学推导,确定亚像素边缘点模板的关键参数;最后,利用粗定位边缘点结合Franklin矩实现亚像素级精确定位。实验表明,该方法检测精度达到10 μm级,执行效率较对比算法效率提升约16.5 %,且抗噪性更好,能更好地满足精密测量边缘检测中高精度和高效率的要求。

关键词: 边缘检测, 亚像素, 改进多结构元素形态学梯度, 自适应Otsu阈值, Franklin矩

Abstract: The detection accuracy and execution efficiency in the precision measurement scenarios were studied,and an improved sub-pixel edge detection method based on multi-structural element morphological gradient and Franklin moment was proposed. Firstly,bilateral filtering was used for image preprocessing,and an improved multi-structural element morphological gradient filter operator was constructed for coarse edge location. Then,the key parameters of the sub-pixel edge point template were determined through adaptive Otsu threshold segmentation and Franklin moment mathematical derivation. Finally,the sub-pixel level precise location was achieved by combining the coarse located edge points with Franklin moment. Experiments show that the detection accuracy of this method reaches the 10 μm level,the execution efficiency was improved by about 16.5 % compared with the comparison algorithm,and it had better noise resistance,which can better meet the requirements of high accuracy and high efficiency in edge detection of precision measurement.

Key words: edge detection, subpixel, improved multi-structural element morphological gradient, adaptive Otsu thresholding, Franklin moments

中图分类号:

TP391.4

闫洪波, 余高超, 赵威, 王鑫辉. 融合形态学和Franklin矩的亚像素边缘检测方法^*[J]. 现代制造工程, 2026, 551(8): 132-137.

YAN Hongbo, YU Gaochao, ZHAO Wei, WANG Xinhui. A sub-pixel edge detection method that integrates morphological analysis and Franklin moments[J]. Modern Manufacturing Engineering, 2026, 551(8): 132-137.

参考文献

[1] 刘利平,孙建,孙文悦. 改进Zernike矩的亚像素圆孔类零件测量方法[J]. 电子测量技术,2023,46(4):69-77.
[2] 曾猛杰,汪晨曦,赖俊杰,等. 亚像素边缘检测算法综述[J]. 光学精密工程,2024,32(23):3513-3524.
[3] 娄群,吕钧澔,文立华,等. 基于亚像素边缘检测的高精度相机标定方法[J]. 光学学报,2022,42(20):82-90.
[4] HE Q,JI Z,YU P,et al. A unified and accurate subpixel detector of control points for camera calibration[J]. IEEE Transactions on Instrumentation and Measurement,2023,72:5020712.
[5] 康朝海,洪立,任伟建,等. 基于亚像素边缘检测和圆心修正补偿的高精度相机标定方法[J]. 激光与光电子学进展,2024,61(8):136-144.
[6] 谢绍敏,李新荣. 亚像素级数字图像弱边缘小目标快速检测算法[J]. 现代电子技术,2024,47(13):23-26.
[7] 吴一全,邹宇,刘忠林. 基于Franklin矩的亚像素级图像边缘检测算法[J]. 仪器仪表学报,2019,40(5):221-229.
[8] 李锦鹏,熊显名,曾启林,等. 基于三次B样条小波变换和Franklin矩亚像素级图像边缘检测算法[J]. 红外技术,2022,44(3):255-261.
[9] 杜港,佟强,侯凌燕,等. 基于Canny-Franklin矩的亚像素边缘检测方法[J]. 计算机集成制造系统,2025,31(3):903-912.
[10] GAVASKAR R G,CHAUDHURV K N. Fast adaptive bilateral filtering[J]. IEEE Transactions on Image Process-ing,2019,28(2):779-790.
[11] 宋冬梅. 数学形态学在数字图像处理中的应用研究[J]. 计算技术与自动化,2021,40(2):136-139.
[12] 魏本征,赵志敏,华晋. 基于改进多结构元素形态学梯度和Zernike矩的亚像素边缘检测方法[J]. 仪器仪表学报,2010,31(4):838-844.
[13] 刘月鹏,张新宇,乔栋,等. 改进Zernike矩的小行星接近过程图像的亚像素边缘检测方法[J]. 中国科学:技术科学,2024,54(8):1473-1484.

融合形态学和Franklin矩的亚像素边缘检测方法^*

A sub-pixel edge detection method that integrates morphological analysis and Franklin moments

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