基于局部方差和后验概率分类的快速模板匹配算法  

Fast template matching based on local variance and posterior probabilityclassification

作　　者：林煜桐 朱姗姗 彭凌西[3] 彭绍湖[1] 谢翔 林焕然 Lin Yutong;Zhu Shanshan;Peng Lingxi;Peng Shaohu;Xie Xiang;Lin Huanran(School of Electronics and Communication Engineering,Guangzhou University,Guangzhou 510006,China;Faculty of Electronic and Information Engineering,Guangdong Baiyun University,Guangzhou 510450,China;School of Mechanical and Electrical Engineering,Guangzhou University,Guangzhou 510006,China)

机构地区：[1]广州大学电子与通信工程学院,广东广州510006 [2]广东白云学院电气与信息工程学院,广东广州510450 [3]广州大学机械与电气工程学院,广东广州510006

出　　处：《电子技术应用》2023年第9期97-102,共6页Application of Electronic Technique

基　　金：广州市教育局高校科研项目(202235165)。

摘　　要：具有旋转不变性的模板匹配算法在工业制造上具有广泛的应用。为解决传统的模板匹配方法在目标旋转、匹配速度上的问题,提出一种基于局部方差和后验概率分类的模板匹配方法。为减少计算量,在匹配中通过局部方差过滤掉部分候选窗口,并在后验概率分类模块中通过对比不同区域稳定特征点对的灰度来计算窗口相关性。使用后验概率分类计算窗口相关度能在预处理过程实现旋转不变性,并保证准确率在95%以上。实验结果表明,该算法在80万像素级的任意角度匹配图像上选择合适的窗口移动步长后,可将匹配时间减少到10 ms以内,相较于现有算法速度更快。Template matching algorithm with rotation invariant is widely used in industrial manufacturing.To solve the problems of traditional template matching methods such as target rotation and matching speed,a template matching method based on local variance and posterior probability classification is proposed.To reduce the amount of computation,some candidate windows are filtered out by local variance in the matching process,and window correlation is calculated by comparing the gray levels of stable feature points in different regions in the posterior probability classification module.Using posterior probability classification to calculate window correlation can achieve rotation invariant during preconditioning and ensure that the accuracy is above 95%.Experimental results show that the algorithm can reduce the matching time to less than 10 ms after selecting the appropriate sliding window moving step on 800000 pixel-level arbitrary angle matching images,which is faster than the existing algorithms.

关 键 词：机器视觉 模板匹配 局部方差 稳定特征点 后验概率分类 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]