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一种改进的ORB特征匹配算法

廖泓真 王亮 孙宏伟 刘云清

廖泓真, 王亮, 孙宏伟, 等 . 一种改进的ORB特征匹配算法[J]. 北京航空航天大学学报, 2021, 47(10): 2149-2154. doi: 10.13700/j.bh.1001-5965.2020.0359
引用本文: 廖泓真, 王亮, 孙宏伟, 等 . 一种改进的ORB特征匹配算法[J]. 北京航空航天大学学报, 2021, 47(10): 2149-2154. doi: 10.13700/j.bh.1001-5965.2020.0359
LIAO Hongzhen, WANG Liang, SUN Hongwei, et al. An improved ORB feature matching algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(10): 2149-2154. doi: 10.13700/j.bh.1001-5965.2020.0359(in Chinese)
Citation: LIAO Hongzhen, WANG Liang, SUN Hongwei, et al. An improved ORB feature matching algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(10): 2149-2154. doi: 10.13700/j.bh.1001-5965.2020.0359(in Chinese)

一种改进的ORB特征匹配算法

doi: 10.13700/j.bh.1001-5965.2020.0359
基金项目: 

吉林省发改委重点研发项目 2018C035-3

详细信息
    通讯作者:

    刘云清, E-mail: mzliuyunqing@163.com

  • 中图分类号: TP391

An improved ORB feature matching algorithm

Funds: 

Key Reserach Program of Jilin Province Development and Reform Commission 2018C035-3

More Information
  • 摘要:

    针对现有的ORB特征匹配算法在图像模糊、光照变化、图像压缩、噪声条件下,匹配准确率下降问题,提出了一种改进的ORB特征匹配算法。首先,在提取特征点过程中,对图像进行网格化处理,并引入四叉树结构,使提取的特征点在图像中均匀分布,解决传统的特征提取方法遇到的特征点集中问题。然后,利用暴力匹配进行初步匹配,并采用交叉验证的方式,剔除部分误匹配,改善暴力匹配的结果。最后,利用高斯核对网格运动统计的结果做加权处理,优化统计结果,进一步剔除误匹配,得到准确率更高的匹配集合。实验结果表明:改进后的算法在图像模糊、光照变化、图像压缩和噪声条件下,平均准确率分别提高了3.5%、4.2%、2.2%和6%。

     

  • 图 1  GMS示意图

    Figure 1.  Schematic of GMS

    图 2  图像网格化示意图

    Figure 2.  Schematic of grid-based image

    图 3  实验用图

    Figure 3.  Images in experiment

    图 4  准确率对比

    Figure 4.  Accuracy comparison

    表  1  实验结果

    Table  1.   Experimental results

    实验图像 最高准确率/% 最低准确率/% 平均准确率/% 准确率标准差/%
    原算法 本文算法 原算法 本文算法 原算法 本文算法 原算法 本文算法
    图像模糊 95.4 98.3 68.1 79.7 85.8 89.3 7.95 5.37
    光照变化 96.8 97.8 87.5 92.7 91.2 95.4 2.48 1.56
    图像压缩 99.8 99.7 91.3 94.3 95.7 97.9 2.65 1.72
    高斯噪声 96.3 98.3 74 57.1 86.2 92.2 7.21 9.57
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-07-24
  • 录用日期:  2020-08-14
  • 刊出日期:  2021-10-20

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