摘要
针对随机抽样一致性(RANSAC)算法在特征点匹配中存在的精度低、稳定性差等问题,提出了一种基于平滑约束和聚类分析的图像配准算法。首先,利用邻域匹配特征点的尺度信息及空间角度顺序构建平滑约束,将初始匹配点划分为高内点率的抽样集和高内点数的验证集;然后,通过反复抽样和模型检验求解暂定内点集,并对其进行聚类分析,根据聚类中心在图像重叠区域的分布质量选取最优内点集;最后,利用最优内点集求解模型参数,实现图像的稳健配准。仿真结果表明,相比RANSAC算法,本算法的配准精度提高了26.83%,误差标准差由0.68降至0.19。
Aiming to solve the problem of low accuracy and poor stability of random sample consensus(RANSAC)algorithm in feature point matching,an image registration algorithm based on smoothness constraint and cluster analysis is proposed in this paper.First,the scale information and spatial angle order of neighborhood matching feature points are used to construct a smoothness constraint,and the initial matching points are divided into a sampling set with a high inlier rate and a verification set with a high inlier number.Then,the solution is solved by repeated sampling and model testing.Next,the inlier set is temporarily determined,and cluster analysis is performed on it.Further,the optimal inlier set is selected according to the distribution quality of the cluster center in the image overlapping area.Finally,the optimal inlier set is used to solve the model parameters to achieve image robust registration.The simulation results show that compared with the RANSAC algorithm,the registration accuracy of the algorithm improved by 26.83%,and the error standard deviation is reduced from 0.68 to 0.19.
作者
赵迪迪
李加慧
谭奋利
曾晨欣
季轶群
Zhao Didi;Li Jiahui;Tan Fenli;Zeng Chenxin;Ji Yiqun(School of Optoelectronic Science and Engineering,Soochow University,Suzhou,Jiangsu215006,China;Jiangsu Key Laboratory of Advanced Optical Manufacturing Technologies,Soochow University,Suzhou,Jiangsu215006,China;Key Laboratory of Modern Optical Technologies,Ministry of Education,Soochow University,Suzhou,Jiangsu215006,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2021年第12期140-146,共7页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61405134,61340007)
国防基础科研计划(JCKY2018414C013)
江苏省自然科学基金(BK20161512)
江苏高校优势学科建设工程(PAPD)。
关键词
图像处理
图像配准
随机抽样一致性
平滑约束
聚类分析
image processing
image registration
random sample consensus
smoothness constraint
cluster analysis
