摘要
ORB算法拥有匹配速度快,匹配性能较好的特点,但是其不具有尺度不变性。针对此弊端,对ORB算法进行改进,采用SIFT-ORB-MRANSAC融合算法,完成特征点的提取、匹配以及去除误匹配。实验效果表明,改进算法相对于传统的ORB算法,在尺度不变性、匹配效率、匹配精准度等方面得到显著提高,具有很好的研究意义和价值。
The ORB algorithm has the characteristics of fast matching and good matching performance,but it does not have scale invariance. Aiming at this drawback,the ORB algorithm is improved,and the SIFT-ORB-MRANSAC fusion algorithm is used to complete the feature point extraction and matching and remove the mismatch. The experimental results show that the improved algorithm is significantly improved in terms of scale invariance,matching efficiency and matching accuracy compared with the traditional ORB algorithm,and has good research significance and value.
引文
[1]Sinha S N,Frahm J M,Pollefeys M,et al. Feature tracking and matching in video using programmable graphics hardware[J]. Machine Vision&Applications,2011(1).
[2]王健,于鸣,任洪娥.一种用于图像拼接的改进ORB算法[J].液晶与显示,2018(6).
[3]葛山峰,于莲芝,谢振.基于ORB特征的目标跟踪算法[J].电子科技,2017(2).
[4]LOWE D G. Distinctive Image Features from Scale Invariant Keypoints[J]. International Journal of Computer Vision,2004(2).
[5]Bay H,Ess A,Tuytelaars T,et al. Speeded-Up Robust Features(SURF)[J]. Computer Vision&Image Understanding,2008(3).
[6]Hamady M,Walker J J,Harris J K,et al. Error-correcting barcoded primers for pyrosequencing hundreds of samples in multiplex.[J]. Nature Methods,2008(3).
