近景影像特征点匹配方法比较研究

余俊鹏; 林洁鸿; 詹松辉; 姚乃文

doi:10.12052/gdutxb.170166

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近景影像特征点匹配方法比较研究

综合研究 | 更新时间：2026-05-27

- 近景影像特征点匹配方法比较研究
- A Comparative Study of Close-Range Image Feature Points Matching Methods
- 广东工业大学学报 2018年35卷第4期页码：56-60
- 作者机构：
  
  广东工业大学　土木与交通工程学院,广东,广州,510006
- 作者简介：
  
  余俊鹏(1982–)，男，高级工程师，博士，主要研究方向为摄影测量与遥感
- 基金信息：
  
  国家自然科学基金青年基金资助项目(41704019);2017年大学生创新创业训练项目(201711845126)
- DOI：10.12052/gdutxb.170166
  中图分类号： P234.1
- 网络首发：2018-07-09，
  
  纸质出版：2018
- 稿件说明：
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余俊鹏, 林洁鸿, 詹松辉, 姚乃文. 近景影像特征点匹配方法比较研究[J]. 广东工业大学学报, 2018, 35(4): 56-60.

Yu Jun-peng, Lin Jie-hong, Zhan Song-hui, et al. A Comparative Study of Close-Range Image Feature Points Matching Methods[J]. 2018, 35(4): 56-60.
余俊鹏, 林洁鸿, 詹松辉, 姚乃文. 近景影像特征点匹配方法比较研究[J]. 广东工业大学学报, 2018, 35(4): 56-60. DOI： 10.12052/gdutxb.170166.

Yu Jun-peng, Lin Jie-hong, Zhan Song-hui, et al. A Comparative Study of Close-Range Image Feature Points Matching Methods[J]. 2018, 35(4): 56-60. DOI： 10.12052/gdutxb.170166.

摘要

近景摄影测量因其拍摄方式灵活

影像之间的相对几何变形大

常导致同名点匹配失败. 本文采用SIFT、SURF、FAST+BRIEF和ORB 4种计算机视觉算法

对不同场景和摄影条件下的近景像对进行特征点检测与描述

结合BFMatch和FlannMatch两种方法对特征点实施匹配. 实验表明

所用算法的计算耗时越长

匹配结果越好. SIFT、SURF适合于高精度连接点的自动生成

而FAST+BRIEF和ORB可用于相对几何变形小的立体影像密集点匹配.

Abstract

Because of flexible shooting mode

the relative geometric deformation between images

one of the main problems of close-range photogrammetry is same name point matching. The SIFT

SURF

FAST + BRIEF

ORB were used to detect and describe the feature points of close-range images under different scenes and photography conditions. The BFMatch and FlannMatch methods were used to match the feature points. Experiments show that the longer the algorithm is

the better the matching result is. SIFT and SURF are both suitable for automatic generation of high-precision connection points. FAST + BRIEF and ORB can be used for stereo image matching with small relative geometric deformation.

关键词

Keywords

references

ZHANG Y, LI J Z, LI D L, et al . Research of remote sensing image matching based on key points [J ] . Radio Engineering, 2016, 46(2): 61-64.

WANG Z J. The research of matching method based on image for retraction and description of feature points [J]. Electronic Design Engineering, 2015, 23(24): 173-174.

ZHOU L L, JIANG F. Image matching algorithm based on Fast and BRIEF [J]. Engineering and Design, 2015, 36(5): 1269-1273.

FENG G S. Feature based image matching [J]. Electronic Test, 2014, 303(17): 17-19.

LOWE D G. Object recognition from local scale-invariant features[C]//IEEE International Conference on Computer Vision. Corfu: [s.n.], 1999: 1150-1157.

毛星云,冷雪飞, 王碧辉,等. OpenCV3编程入门[M]. 北京:电子工业出版社,2016.

BAY H, TINNE T, GOOL L V. Surf: speeded up robust features[J]. Proceedings of the European Conference on Computer Vision, 2006, 3951: 404-417.

ROSTEN E, DRUMMOND T. Machine learning for high-speed corner detection[J]. Proceedings of the European Conference on Computer Vision, 2006, 1: 430-443.

KOU Y, XU J Z. Algorithm for remote sensing matching based on the structure characteristics [J]. Journal of Image and Graphics, 2013, 18(5): 565-573.

WANG P Z, CHEN P, ZHOU F, et al . Method of feature extraction and matching for complex image [J ] . Journal of Anhui University of Technology (Natural Science), 2012, 29(1): 73-77.

RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: an efficient alternative to SIFT or SURF[C]//2011 International Conference on Computer Vision. Barcelona:IEEE, 2011: 2564-2571.

HUANG W, WU L D, SONG H C. RBRIEF: a robust descriptor based on random binary comparisons [J]. Computer Vision Iet, 2013, 7(1): 29-35.

PENG D M, LIU R, LIU Y. Research and implementation of image feature point matching method based on OpenCV [J]. Geomatics Spatial Information Technology, 2016, 39(9): 61-63.

HUO J ,YANG N , CAO M Y , et al . A reliable algorithm for image matching based on SIFT [J ] . Journal of Harbin Institute of Technology, 2012, 19(4): 90-95.

YU H, YANG W. A fast feature extraction and matching algorithm for unmanned aerial vehicle image [J]. Journal of Electronics Information Technology, 2016, 38(3): 509-516.

YU J P, HE C R. An accuracy analysis of GPS-RTK assisted close-range photogrammetric bundle adjustment [J]. Journal of Guangdong University of Technology, 2017, 34(6): 73-77.

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