A Comparative Study of Close-Range Image Feature Points Matching Methods

Yu Jun-peng; Lin Jie-hong; Zhan Song-hui; Yao Nai-wen

doi:10.12052/gdutxb.170166

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A Comparative Study of Close-Range Image Feature Points Matching Methods

更新时间：2026-05-27

- A Comparative Study of Close-Range Image Feature Points Matching Methods
- Vol. 35, Issue 4, Pages: 56-60(2018)
- 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：
- 基金信息：
- DOI：10.12052/gdutxb.170166
  CLC： P234.1
- Online First：09 July 2018，
  
  Published：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.

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references

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