Image matching of variable circular domain compass features

LI Jia; SHENG Ye-hua; ZHANG Ka; DUAN Ping

doi:10.3788/OPE.20142205.1339

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Image matching of variable circular domain compass features

更新时间：2020-08-13

- Image matching of variable circular domain compass features
- Optics and Precision Engineering Vol. 22, Issue 5, Pages: 1339-1346(2014)
- 作者机构：
  
  南京师范大学虚拟地理环境教育部重点实验室,江苏南京,210023
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20142205.1339
  CLC： TP751
- Received：16 September 2013，
  
  Revised：10 November 2013，
  
  Published：25 May 2014
- 稿件说明：
移动端阅览
李佳, 盛业华, 张卡等. 变圆域罗盘特征图像匹配[J]. 光学精密工程, 2014,22(5): 1339-1346

LI Jia, SHENG Ye-hua, ZHANG Ka etc. Image matching of variable circular domain compass features[J]. Editorial Office of Optics and Precision Engineering, 2014,22(5): 1339-1346
李佳, 盛业华, 张卡等. 变圆域罗盘特征图像匹配[J]. 光学精密工程, 2014,22(5): 1339-1346 DOI： 10.3788/OPE.20142205.1339.

LI Jia, SHENG Ye-hua, ZHANG Ka etc. Image matching of variable circular domain compass features[J]. Editorial Office of Optics and Precision Engineering, 2014,22(5): 1339-1346 DOI： 10.3788/OPE.20142205.1339.

摘要

针对现有图像匹配算法特征算子计算复杂度高，关键点描述过程中点对选取存在不确定性等问题，提出了变圆域罗盘特征匹配（VCDCM）方法。该方法首先利用罗盘检测法对图像进行关键点检测，采用变圆模式接受域选取理想点对，根据接受域内点对之间的距离将点对分为长点对集和短点对集；然后用长点对集描述关键点方向，短点对集构建关键点描述符。最后采用Hamming距离代替传统的欧式距离进行匹配，并采用随机抽样一致（RANSAC）方法精炼匹配点以避免由于噪声和物体位置移动等原因产生的误匹配。从鲁棒性和实时性两个方面对本文提出的方法与尺度不变特征变换（SIFT）和二元加速鲁棒特征（BRIEF）方法进行了对比试验分析，实验结果表明，本文提出的方法具有匹配速度快、准确性高、稳定性好等特点。

Abstract

As existing image matching algorithms show the problems of high computational complexity and uncertainty in point-pair selection

a Variable Circular Domain Compass Matching (VCDCM) algorithm was proposed. After key-points being detected by four compasses

the variable circular receiving domain was used to choose the ideal point-pairs. Then

the point-pair set was divided into two subsets according to the distance of point-pairs.The subset of long-distance pairs was used to describe the direction of a key-point and that of short-distance pairs was used to build the descriptor of key-point. Finally

key-points were matched by Hamming distance instead of traditional Euclidean distance

while the match points were filtered with Random Sample Consensus (RANSAC) algorithm to avoid mismatches caused by the noise and moving objects. Comparative experiments between Scale Invariant Feature Transform (SIFT) and Binary Robust Independent Elementary Features (BRIEF) algorithms were performed on the robustness and efficiency. The experimental results show that the proposed algorithm is faster with high accuracy and stability.

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references

刘志文, 刘定生, 刘鹏. 应用尺度不变特征变换的多源遥感影像特征点匹配[J]. 光学精密工程, 2013, 21(8):2146-2153. LIU ZH W, LIU D SH, LIU P.SIFT feature matching algorithm of multi-source remote image[J].Opt.Precision Eng. , 2013, 21(8):2146-2153.(in Chinese)

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