摘要
针对现有的数字图像相关(DIC)法图像匹配技术中,子集的大小通常由用户根据自己的经验来定义的,这样就会使得DIC的匹配精度降低。提出了一种基于香农熵自适应选择子集的方法来优化爬山算法,该算法能够在不降低计算效率的前提下,显著提高位移测量精度。通过实验验证得出,当子集大小选择恰当时,该算法能使匹配精度达到0.98,将其与常用的爬山算法相比较发现准确率提高了0.04,而且随着位移不断增大,这种优势越明显。最后通过刚体位移实验也验证了此算法的有效性,改进的算法适用于DIC形变分析。
For the existing digital image correlation(DIC)image matching technology,the size of the subset is usually defined by users according to their own experience,which will reduce the matching accuracy of DIC.In this paper,an adaptive subset selection method based on Shannon entropy is proposed to optimize the hill-climbing algorithm,which can significantly improve the displacement measurement accuracy without reducing the computational efficiency.Through experimental verification,it is concluded that when the subset size is properly selected,the algorithm can achieve a matching accuracy of 0.98.Compared with the commonly used hill-climbing algorithm,it is found that the accuracy is increased by 0.04,and this advantage becomes more pronounced as the displacement increases.Finally,the validity of this algorithm is verified by rigid body displacement experiments.So the improved algorithm is suitable for DIC deformation analysis.
作者
刘贵香
刘吉
武锦辉
牛天利
Liu Guixiang;Liu Ji;Wu Jinhui;Niu Tianli(School of Information and Communication Engineering,North University of China,Taiyuan 030051,China)
出处
《国外电子测量技术》
北大核心
2022年第6期20-24,共5页
Foreign Electronic Measurement Technology
基金
山西回国留学人员科研项目(HGKY2019068)
山西省自然科学基金(201901D111159)项目资助。
关键词
光学测量
DIC
爬山算法
自适应选择子集
香农熵
optical measurement
DIC
mountain climbing algorithm
adaptive subset selection
Shannon entropy
作者简介
刘贵香,硕士研究生,主要研究方向为光电检测技术,数字图像处理。E-mail:744614785@qq.com。
