A fast matching method for large viewpoint changes images based on ORB algorithm

被引:0
作者
Zeng Q.-H. [1 ,2 ]
Chen Y. [1 ]
Wang Y.-S. [1 ,2 ]
Liu J.-Y. [1 ,2 ]
Liu S. [3 ]
机构
[1] College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] Collaborative Innovation Center for Satellite Communication and Navigation, Nanjing
[3] AVIC Luoyang Electro-Optical Equipment Research Institute, Luoyang
来源
Kongzhi yu Juece/Control and Decision | 2017年 / 32卷 / 12期
关键词
Affine transformation; Large viewing; ORB; Perspective transformation;
D O I
10.13195/j.kzyjc.2016.1521
中图分类号
学科分类号
摘要
For the problem that the affine scale invariant feature transform(ASIFT) algorithm does well in large viewing image matching but has low computing efficiency, a fast large viewing image matching method based on the oriented FAST and rotated BRIEF(ORB) algorithm is proposed. The improved algorithm combines the perspective transformation model and ORB algorithm to optimize the affine transformation model and SIFT algorithm in the ASIFT algorithm. The refined matching is performed with the homography matrix based on coarse matching, which can reduce the number of simulation and improve the efficiency of the algorithm. The experimental results show that the proposed algorithm has the ability to resist the angle of view, and is 10 times faster than the ASIFT algorithm. Also, it has strong real-time performance and high engineering application value. © 2017, Editorial Office of Control and Decision. All right reserved.
引用
收藏
页码:2233 / 2239
页数:6
相关论文
共 16 条
[1]  
Warrant E., Dacke M., Visual navigation in nocturnal insects, Physiology, 31, 3, pp. 182-192, (2016)
[2]  
Jia K., Chan T.H., Zeng Z., Et al., ROML: A robust feature correspondence approach for matching objects in a set of images, Int J of Computer Vision, 117, 2, pp. 173-197, (2016)
[3]  
Chen Z.X., He C., Liu C.Y., Image saliency target detection based on global features and local features, Control and Decision, 31, 10, pp. 1899-1902, (2016)
[4]  
Lu Y., Song D., Visual navigation using heterogeneous landmarks and unsupervised geometric constraints, IEEE Trans on Robotics, 31, 3, pp. 1-14, (2015)
[5]  
Xu Y.X., Chen F., Scene matching algorithm based on CenSurE for SAR/INS integrated navigation system, Control and Decision, 26, 8, pp. 1175-1180, (2011)
[6]  
Lowe D.G., Distinctive image features from scale-invariant keypoints, Int J of computer vision, 60, 2, pp. 91-110, (2004)
[7]  
Bay H., Ess A., Tuytelaars T., Et al., Speeded-up robust features (SURF), Computer Vision and Image Understanding, 110, 3, pp. 346-359, (2008)
[8]  
Rublee E., Rabaud V., Konolige K., Et al., ORB: An efficient alternative to SIFT or SURF, Int Conf on Computer Vision, pp. 2564-2571, (2011)
[9]  
Morel J.M., Yu G., ASIFT: A new framework for fully affine invariant image comparison, Siam J on Imaging Sciences, 2, 2, pp. 438-469, (2009)
[10]  
Cai G.R., Jodoin P.M., Li S.Z., Et al., Perspective-SIFT: An efficient tool for low-altitude remote sensing image registration, Signal Processing, 93, 11, pp. 3088-3110, (2013)