改进ORB算法在图像匹配中的应用
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摘要
在计算机视觉领域,图像匹配是一个核心问题。为了提高图像特征点匹配算法的准确度,增强算法的抗干扰能力,针对ORB(oriented FAST and rotated BRIEF)算法的不足,提出一种改进的图像特征点匹配算法。该算法通过设置自适应阈值来进行特征点检测,并在算法粗匹配结果的基础上剔除不符合图像几何特性的部分外点。最后,利用仿射不变性约束筛选出精确匹配点。实验表明,该方法可有效提高算法匹配质量且执行时间短,对于不同模糊度和曝光度的图像匹配均具有很好的鲁棒性。
In the field of computer vision,image matching is a core issue. In order to improve the accuracy of image feature matching and enhance its anti-interference ability,in view of the insufficiency of ORB( oriented FAST and rotated BRIEF),an improved method based on ORB is proposed. This algorithm sets self-adaptive threshold in the process of detecting feature points.Furthermore,the outer points which do not conform to the geometric characteristics of images are removed after the coarse matching,then the affine invariant constraint is chosen to pick out the exact matching points. The experimental results show that the proposed method has greatly improved the matching quality and has low computing cost. In addition,the proposed method has strong robustness for different blur and exposure degrees.
In the field of computer vision,image matching is a core issue. In order to improve the accuracy of image feature matching and enhance its anti-interference ability,in view of the insufficiency of ORB( oriented FAST and rotated BRIEF),an improved method based on ORB is proposed. This algorithm sets self-adaptive threshold in the process of detecting feature points.Furthermore,the outer points which do not conform to the geometric characteristics of images are removed after the coarse matching,then the affine invariant constraint is chosen to pick out the exact matching points. The experimental results show that the proposed method has greatly improved the matching quality and has low computing cost. In addition,the proposed method has strong robustness for different blur and exposure degrees.
引文
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[18]赵璐璐,耿国华,李康,等.基于SURF和快速近似最近邻搜索的图像匹配算法[J].计算机应用研究,2013,30(3):921-923.
[19]李小红,谢成明,贾易臻,等.基于ORB特征的快速目标检测算法[J].电子测量与仪器学报,2013,27(5):455-460.
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[2]周敏,刘宁钟,王立春.基于无人机航拍的公路图像拼接方法研究[J].计算机与现代化,2016(10):49-53.
[3]侯志强,王利平,郭建新,等.基于颜色、空间和纹理信息的目标跟踪[J].光电工程,2018,45(5):39-46.
[4]马俊.基于图像和深度信息融合的目标跟踪[J].计算机与现代化,2013(9):91-94.
[5]ABATE A F,NAPPI M,RICCIO D,et al.2D and 3D face recognition:A survey[J].Pattern Recognition Letters,2007,28(14):1885-1906.
[6]ZHANG T,LIU J H,LIU S L,et al.A 3D reconstruction method for pipeline inspection based on multi-vision[J].Measurement,2017,98:35-48.
[7]MUR-ARTAL R,MONTIEL J M M,TARDOS J D.ORB-SLAM:A versatile and accurate monocular SLAM system[J].IEEE Transactions on Robotics,2015,31(5):1147-1163.
[8]权美香,朴松昊,李国.视觉SLAM综述[J].智能系统学报,2016,11(6):768-776.
[9]高晓峰,史朝辉.一种改进的基于灰度投影的图像匹配算法[J].航空计算技术,2012,42(6):85-87.
[10]李建虎,郭艳柳.图像灰度匹配算法研究[J].海洋测绘,2008,28(6):18-20.
[11]朱奇光,张朋珍,李昊立,等.基于全局和局部特征融合的图像匹配算法研究[J].仪器仪表学报,2016,37(1):170-176.
[12]赵莹,刘森,李庆猛,等.基于图像特征匹配的商品识别算法[J].计算机与现代化,2014(1):18-21.
[13]刘毅飞,张旭明,丁明跃.归一化互相关灰度图像匹配的多核信号处理器实现[J].计算机应用,2011,31(12):3334-3336.
[14]高峰.图像配准中的几何特征不确定性建模及匹配方法研究[D].长沙:国防科学技术大学,2011.
[15]时培明,蒋金水,刘彬,等.基于边界特征尺度匹配延拓的EMD改进方法及应用[J].中国机械工程,2014,25(12):1616-1623.
[16]于子翊,李晓娟.基于形状不变矩的储粮微生物识别[J].微计算机信息,2006,22(16):251-253.
[17]傅卫平,秦川,刘佳,等.基于SIFT算法的图像目标匹配与定位[J].仪器仪表学报,2011,32(1):163-169.
[18]赵璐璐,耿国华,李康,等.基于SURF和快速近似最近邻搜索的图像匹配算法[J].计算机应用研究,2013,30(3):921-923.
[19]李小红,谢成明,贾易臻,等.基于ORB特征的快速目标检测算法[J].电子测量与仪器学报,2013,27(5):455-460.
[20]LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[21]BAY H,ESS A,TUYTELAARS T,et al.Speeded-up robust features(SURF)[J].Computer Vision and Image Understanding,2008,110(3):346-359.
[22]RUBLEE E,RABAUD V,KONOLIGE K,et al.ORB:An efficient alternative to SIFT or SURF[C]//Proceedings of the 2011 IEEE International Conference on Computer Vision.2011:2564-2571.
[23]ROSTEN E,DRUMMOND T.Machine learning for highspeed corner detection[C]//Proceedings of the 9th European Conference on Computer Vision.2006:430-443.