一种基于SURF-ORB的改进图像配准算法  

An Improved Image Registration Algorithm Based on SURF⁃ORB

作　　者：尚明姝 王克朝 高玉宝 SHANG Mingshu;WANG Kechao;GAO Yubao(School of Information Engineering,Harbin Institute,Harbin,Heilongjiang 150080,China;Exhibition Hall of Criminal Evidence of the 731st Army of the Japanese Invading China,Harbin,Heilongjiang 150001,China)

机构地区：[1]哈尔滨学院信息工程学院,黑龙江哈尔滨150080 [2]侵华日军第七三一部队罪证陈列馆,黑龙江哈尔滨150001

出　　处：《计量学报》2025年第3期323-328,共6页Acta Metrologica Sinica

基　　金：黑龙江省重点研发项目(GY2023JD0051);黑龙江省自然科学基金(LH2024F011);黑龙江省哲学社会科学基金(21KGB083;22KGB142)。

摘　　要：提出一种基于SURF-ORB的改进图像配准算法。建立SURF图像金字塔,在其上检测ORB特征点,用256位二进制字符作为特征描述向量,用最近邻法匹配特征点。利用正确匹配点间的邻域平均灰度差、欧式距离、匹配点连线与x轴夹角应近似相等的性质,筛选匹配点。改进了k-means算法,以密度大于阈值的点为类中心点,聚类,删除误差平方和大于阈值的类,将剩余特征点重新分入保留的类中。改进了RANSAC算法,将所有变换模型对应的内点合并为集合,将集合中与候选最优变换模型满足误差距离阈值的匹配点归入其内点。利用最小二乘法用其所有内点计算更精确变换模型。实验结果表明:该算法比SURF算法、ORB算法提取的特征点减少了约32%,匹配正确率提高了约16%,运算时间减少了约0.26%。An improved image registration algorithm based on SURF-ORB is proposed.Establish speeded up robust features(SURF)image pyramid,detect oriented FAST and rotated BRIEF(ORB)feature points on it and use 256 bit binary characters as feature vectors to describe feature points.The nearest neighbor method matches feature points.Filter matching points by utilizing the similar properties of neighborhood average gray difference,Euclidean distance,matching point line and x-axis angle among the correct matching points.The k-means clustering(k-means)algorithm is improved,using points with a density greater than the threshold as the center point of the class,clustering,deleting classes with a sum of squared errors greater than the threshold and reclassifying the remaining feature points into the reserved classes.The random sample consensus(RANSAC)algorithm is improved,merging all transformation matrixes’interior points into a set and classifying the matching points in the set that meet the error distance threshold with the candidate optimal transformation model into its inner points.Use the least squares method to recalculate the transformation matrix with all its interior points to obtain a more accurate solution.The experimental results show that the number of feature points extracted by this algorithm is about 32%less than that of SURF and ORB algorithm,the matching accuracy is improved by about 16%,and the operation time is increased by about 0.26%.

关 键 词：机械视觉 图像配准 尺度空间 SURF算法 ORB算法 特征点分组 变换模型 

分 类 号：TB96[机械工程—光学工程]