Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (4): 1138-1143.DOI: 10.11772/j.issn.1001-9081.2019081465

• Virtual reality and multimedia computing • Previous Articles     Next Articles

Image registration algorithm combining GMS and VCS+GC-RANSAC

DING Hui1, LI Lihong1, YUAN Gang2   

  1. 1. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan Shanxi 030000, China;
    2. Taiyuan Research Institute, China Coal Technology and Engineering Group, Taiyuan Shanxi 030000, China
  • Received:2019-08-23 Revised:2019-11-20 Online:2020-04-10 Published:2019-11-25
  • Supported by:
    This work is partially supported by the Natural Science Foundation of Shanxi Province(201801D121189).


丁辉1, 李丽宏1, 原钢2   

  1. 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
  • 通讯作者: 李丽宏
  • 作者简介:丁辉(1993-),男,山西朔州人,硕士研究生,主要研究方向:图像处理、人工智能;李丽宏(1963-),男,山西太原人,副教授,博士,主要研究方向:模式识别、人工智能;原钢(1992-),男,山西太原人,研究员,硕士,主要研究方向:机器人智能控制。
  • 基金资助:

Abstract: 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 GC-RANSAC 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.

Key words: Graph-Cut (GC), RANdom SAmple Consensus (RANSAC), Grid-based Motion Statistics (GMS), feature point matching, Vector Coefficient Similarity (VCS), image registration

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GC-RANSAC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。

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

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