朱标.基于优化ORB算法的图像角点特征匹配方法[J].重庆工商大学学报(自然科学版),2021,38(4):42-51
ZHU Biao.An Image Corner Features Matching Method Based on Optimized ORB Algorithm[J].Journal of Chongqing Technology and Business University(Natural Science Edition),2021,38(4):42-51
基于优化ORB算法的图像角点特征匹配方法
An Image Corner Features Matching Method Based on Optimized ORB Algorithm
  
DOI:
中文关键词:  ORB算法  Shi-Tomasi算法  BRIEF算法  SURF算法  特征匹配
英文关键词:ORB algorithm  Shi-Tomasi algorithm  BRIEF algorithm  SURF algorithm  feature matching
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作者单位
朱标 1.中航华东光电有限公司安徽 芜湖 241002 2.安徽省现代显示技术重点实验室安徽 芜湖 2410023.国家特种显示工程技术研究中心安徽 芜湖 241002 4.特种显示国家工程实验室安徽 芜湖 241002 
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中文摘要:
      针对传统ORB算法的图像角点特征匹配精度不高的问题,提出基于优化ORB算法的图像角点特征匹配方法;首先使用Shi-Tomasi算法检测图像角点特征,然后使用BRIEF和SURF相融合算法生成图像角点特征双描述子序列并使用随机投影原理进行降维,最后使用优化的匹配算法进行匹配,简称Shi-Tomasi-SURFORB算法,仿真实验通过统计角点特征数、角点特征匹配数、角点特征正确匹配数、角点特征匹配精度、角点特征匹配精度率、图像匹配时间和图像匹配时间率共7个指标进行分析;分析结果表明:Shi-Tomasi-SURFORB算法与传统ORB算法相比,在图像角点特征匹配时间方面提升了9.46%,但在图像角点特征匹配精度方面提升了8.88%,为图像角点特征匹配提供了一种更加均衡的解决方案。
英文摘要:
      Aiming at the problem that the image corner features matching accuracy of the traditional ORB algorithm is not high, an image corner features matching method based on optimized ORB algorithm is proposed. Firstly, this paper uses the Shi-Tomasi algorithm to detect the image corner features, then uses the fusion algorithm of BRIEF and SURF to generate the double-descriptor sequences of the image corner features, and reduces dimension of the double-descriptor sequences by the random projection principle, finally, the optimized matching algorithm is used to match, referred to as the Shi-Tomasi-SURFORB algorithm. The simulation experiment analyzes seven indicators, including the number of corner features, the number of corner features matching, the number of correct corner features matching, the accuracy of corner features matching, the corner feature matching accuracy ratio, the image matching time, and the image matching time rate, the analysis results show that compared with the traditional ORB algorithm, the Shi-Tomasi-SURFORB algorithm improves the image corner features matching time by 9.46%, but improves the image corner features matching accuracy by 8.88%, which provides a more balanced solution for image corner features matching.
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