融合GMS与VCS+GC-RANSAC的图像配准算法  

Image registration algorithm combining GMS and VCS+GC-RANSAC

作　　者：丁辉 李丽宏 原钢 DING Hui;LI Lihong;YUAN Gang(College of Electrical and Power Engineering,Taiyuan University of Technology,Taiyuan Shanxi 030000,China;Taiyuan Research Institute,China Coal Technology and Engineering Group,Taiyuan Shanxi 030000,China)(Department of Nuclear Explosion Detection Engineering,Rocket Force University of Engineering,Xi’an Shaanxi 710025,China)

机构地区：[1]太原理工大学电气与动力工程学院,太原030000 [2]中国煤炭科工集团太原研究院,太原030000

出　　处：《计算机应用》2020年第4期1138-1143,共6页journal of Computer Applications

基　　金：山西省自然科学基金资助项目(201801D121189)。

摘　　要：针对当前图像配准算法配准时间过长、配准正确率低等问题,提出一种基于网格运动统计(GMS)、矢量系数相似度(VCS)与图割随机抽样一致性(GC-RANSAC)的图像配准算法。首先,通过ORB(Oriented FAST and Rotated BRIEF)算法对图像进行特征点提取,并对特征点进行暴力匹配。之后,通过GMS算法对图像中的粗匹配特征点进行网格划分,利用网格中正确匹配点邻域内具有较高特征支持量的原理对粗匹配对进行筛选;并引入图像匹配对在进行矢量运算时VCS不超过某一设定阈值的原理对匹配对进行部分剔除,以利于算法后期的快速收敛。最后,运用GCRANSAC算法进行局部最优模型拟合,得到精匹配特征点集,实现高精度的图像配准和拼接。通过与ASIFT+RANSAC、GMS、AKAZE+RANSAC、GMS+GC-RANSAC等算法对比,实验结果表明,该算法在平均匹配精度上提高了30.34%,平均匹配时间缩短0.54 s。Aiming at the problems of long registration time and low registration accuracy of current image registration algorithms,an image registration algorithm based on Grid-based Motion Statistics(GMS),Vector Coefficient Similarity(VCS)and Graph-Cut RANdom SAmple Consensus(GC-RANSAC)was proposed.Firstly,the feature points of the image were extracted through the ORB(Oriented FAST and Rotated BRIEF)algorithm,and Brute-Force matching of the feature points was performed.Then,the coarse matching feature points in the image were meshed by the GMS algorithm,and the coarse matching pairs were filtered based on the principle that high feature support exists in the neighborhood of the correct matching points in the grid.And the part elimination was performed to the matching pairs by introducing the principle that the image matching pair has VCS not exceed a set threshold during vector operation,which is beneficial to the fast convergence of the algorithm in the later stage.Finally,the local optimal model fitting was performed by using the GCRANSAC algorithm to obtain the fine matching feature point set and achieve image registration and stitching with high precision.Compared with algorithms such as ASIFT+RANSAC,GMS,AKAZE+RANSAC,GMS+GC-RANSAC,the results show that the proposed algorithm improves the average matching accuracy by 30.34%and reduces the average matching time by 0.54 s.

关 键 词：图割 随机抽样一致性 网格运动统计 特征点匹配 矢量系数相似度 图像配准 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]