摘要
针对传统局部特征匹配算法在复杂场景中匹配精度低、实时性差的问题,提出一种基于CenSurE-star融合边缘化外点的图像匹配方法。首先对模板图像和待匹配图像进行快速引导滤波预处理;随后提出一种自适应阈值的CenSurE-star算法进行特征检测;其次,本文首次将BEBELID(Boosted efficient binary local image descriptor)描述符和改进的CenSurE-star算法相结合,利用基于机器学习的分类方法得到高效的二值描述符;最后引入MAGSAC++(Mar-ginalizing Sample Consensus)算法边缘化外点得到空间几何变换关系,剔除初步匹配中存在的误匹配,提高匹配精度。通过标准牛津数据集实验对比,相较于BRISK、ORB、AKAZE、传统CenSurE-star算法,该方法的特征点分布更均匀、误匹配点更少,在模糊、光照、视点、尺度变化方面拥有更强的鲁棒性,提高了算法在复杂场景中的匹配精度,实时性也进一步提升。
Aiming at the problems of low matching accuracy and poor real time performance of traditional local feature matching algorithms in complex scenes,an image matching method based on CenSurE star fusion of marginalization outliers is proposed in this paper.Firstly,fast bootstrap filtering preprocessing is performedon the template image and the image to be matched.Subsequently,an adaptive threshold based on CenSurE star algorithm is proposed for feature detection.Secondly,for the first time,the BEBELID(Boosted efficient binary local image descriptor)descriptor is used in conjunction with the improved CenSurE star algorithm to obtain efficient binary descriptors using machine learning based classification methods.Finally,MAGSAC++(Marginalizing Sample Consensus)algorithm is introduced to marginalize outliers and obtain spatial geometric transformation relationships,eliminating errors in preliminary matching and improving matching accuracy.Through the experimental comparison of the standard Oxford dataset,compared with the BRISK,ORB,AKAZE,and the traditional CenSurE star algorithms,this method has a more uniform distribution of feature points,fewer mismatched points,and possesses stronger robustness in terms of blurring,illumination,point of view,and scale variations,which improves the matching accuracy of the algorithm in complex scenes and further enhances the real time performance.
作者
谷学静
楚一凡
肖军发
周记帆
GU Xue-jing;CHU Yi-fan;XIAO Jun-fa;ZHOU Ji-fan(School of Electrical Engineering,North China University of Science and Technology,Tangshan 063210,China;Tangshan Digital Media Engineering Technology Research Center,Tangshan 063000,China)
出处
《激光与红外》
CAS
CSCD
北大核心
2024年第11期1759-1766,共8页
Laser & Infrared
基金
唐山市沉浸式虚拟环境三维仿真基础创新团队项目(No.18130221A)资助。
