空间约束下异源图像误匹配特征点剔除算法  

Algorithm for Eliminating Mismatched Feature Points in Heterogeneous Images Pairs Under Spatial Constraints

作　　者：沈英[1] 林烨 陈海涛 吴靖 黄峰 Shen Ying;Lin Ye;Chen Haitao;Wu Jing;Huang Feng(School of Mechanical Engineering and Automation,Fuzhou University,Fuzhou 350108,Fujian,China)

机构地区：[1]福州大学机械工程及自动化学院,福建福州350108

出　　处：《光学学报》2024年第20期208-219,共12页Acta Optica Sinica

基　　金：福建省科技厅引导性项目(2023H0005)。

摘　　要：红外与可见光图像因其显著的光谱特性差异,在配准过程中易出现特征点误匹配率高的问题。当前广泛应用的误匹配剔除算法通常采用随机采样结合模型拟合的策略,这类方法往往难以兼顾配准精度和速度,表现为算法迭代次数过高或鲁棒性不强。针对这一问题,提出一种基于空间约束的优先采样一致性(SC-PRISAC)误匹配剔除算法。利用材料辐射率差异设计兼具红外与可见光特征的双光谱标定靶标,基于双边滤波金字塔标定获取相机内外参数,在此基础上利用极线约束定理和深度一致性原则构建异源图像间的空间约束关系。使用高质量特征点优先采样策略减少了算法的迭代次数,有效剔除误匹配特征点。实验表明:所提算法实现了亚像素红外与可见光双目标定,标定误差降低至0.430 pixel;在提高配准精度的同时,也有效提升了处理速度,单应性矩阵估计误差为7.857,处理时间仅为1.919 ms,各项性能均优于RANSAC(random sample consensus)等算法。所提算法为红外与可见光图像配准提供一种更为可靠和高效的误匹配剔除解决方案。Objective Infrared and visible light images exhibit significant differences in spectral properties due to their distinct imaging mechanisms.These differences often result in a high mismatch rate of feature points between the two types of images.Currently,widely used mismatch rejection algorithms,such as random sample consensus(RANSAC)and its variants,typically employ a strategy of random sampling combined with iterative optimization modeling for consistency fitting.However,when aligning heterogeneous images with high outlier rates,these methods often struggle to balance alignment accuracy and speed,leading to a high number of iterations or weak robustness.To address the relatively fixed positions of infrared and visible detectors in dualmodal imaging systems,we propose a spatial constraints priority sampling consensus(SCPRISAC)algorithm.This algorithm leverages image space constraints to provide a robust inlier screening mechanism and an efficient sampling strategy,thus offering stable and reliable support for the fusion of infrared and visible image information.Methods In this study,a bispectral calibration target with both infrared and visible features is designed based on differences in material radiance.We achieve highprecision binocular camera calibration by accurately determining the internal and external parameters of the camera using a bilateral filtering pyramid.Based on this calibration,the spatial relationship between heterogeneous images is constructed using the epipolar constraint theorem and the principle of depth consistency.By implementing a priority sampling strategy based on the matching quality ranking of feature points,the number of iterations required by the algorithm is significantly reduced,allowing for precise and efficient elimination of mismatched feature points.Results and Discussions Our method’s calibration accuracy is assessed through the mean reprojection error(MRE),with comparative results presented in Table 1 and Fig.7.The findings demonstrate a 58.2%improvement in calibration p

关 键 词：图像配准 误匹配特征点剔除 极线约束 双目标定 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]