加速分割特征优化的图像配准方法
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摘要
提出一种加速分割特征算法与快速视网膜关键点描述子(FREAK)结合的图像配准算法。首先对图像建立尺度空间,并在此基础上利用加速分割特征优化算法检测图像特征点,结合Harris算法对特征点进行过滤,保留强角点用于图像配准;其次结合FREAK对检测的特征点进行描述,计算其特征向量,采用汉明距离替代传统的欧氏距离进行图像匹配,并采用随机采样一致性方法精炼匹配点来避免由于噪声和物体位置移动等原因产生的误匹配。从配准精度和配准时间两个方面,对本文方法与尺度不变特征变换算法、二进制稳健独立基本特征算法及原始FREAK算法进行对比实验,结果表明,本文方法具有配准速度快、准确性高、稳定性好等特点。
Image registration combining accelerated segmentation feature algorithm and fast retina keypoint(FREAK)algorithm is proposed.Firstly,the scale space is constructed for the image,and the image feature points are detected by the accelerated segmentation feature optimization algorithm.Keypoints are filtered by Harris algorithm and some strong corners retained are reserved for image registration.Secondly,the strong corners are described by FREAK and eigenvectors are calculated.Keypoints are matched by Hamming distance instead of traditional Euclidean distance.Matches are filtered with random sample consensus algorithm to avoid mismatch due to noise and moving objects.From the two aspects of registration accuracy and registration time,the comparative experiments between scale-invariant feature transform,binary robust independent elementary features,original FREAK and the proposed algorithm are carried out.The experimental results show that the proposed algorithm has the characteristics of fast registration speed,high accuracy and well-stability.
Image registration combining accelerated segmentation feature algorithm and fast retina keypoint(FREAK)algorithm is proposed.Firstly,the scale space is constructed for the image,and the image feature points are detected by the accelerated segmentation feature optimization algorithm.Keypoints are filtered by Harris algorithm and some strong corners retained are reserved for image registration.Secondly,the strong corners are described by FREAK and eigenvectors are calculated.Keypoints are matched by Hamming distance instead of traditional Euclidean distance.Matches are filtered with random sample consensus algorithm to avoid mismatch due to noise and moving objects.From the two aspects of registration accuracy and registration time,the comparative experiments between scale-invariant feature transform,binary robust independent elementary features,original FREAK and the proposed algorithm are carried out.The experimental results show that the proposed algorithm has the characteristics of fast registration speed,high accuracy and well-stability.
引文
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[2]Yang S,Xia M H,Zheng Z S.Medical image registration algorithm based on polynomial deterministic matrix and SIFT transform[J].Laser&Optoelectronics Progress,2016,53(8):081002.杨飒,夏明华,郑志硕.基于多项式确定性矩阵的SIFT医学图像配准算法[J].激光与光电子学进展,2016,53(8):081002.
[3]Yang S,Yang C L.Image registration algorithm based on sparse random projection and scale-invariant feature transform[J].Acta Optica Sinica,2014,34(11):1110001.杨飒,杨春玲.基于压缩感知与尺度不变特征变换的图像配准算法[J].光学学报,2014,34(11):1110001.
[4]Dong Q,Liu J H,Wang C,et al.Image mosaic algorithm based on improved BRISK[J].Journal of Electronics&Information Technology,2017,39(2):444-450.董强,刘晶红,王超,等.基于改进BRISK的图像拼接算法[J].电子与信息学报,2017,39(2):444-450.
[5]Lowe D G.Distinctive image features from scaleinvariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[6]Ke Y,Sukthankar R.PCA-SIFT:a more distinctive representation for local image descriptors[C]∥Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2004.
[7]Bay H,Ess A,Tuytelaars T,et al.Speeded-up robust features(SURF)[J].Computer Vision and Image Understanding,2008,110(3):346-359.
[8]Calonder M,Lepetit V,Strecha C,et al.BRIEF:binary robust independent elementary features[C]∥Proceedings of the 11th European Conference on Computer Vision,2010:778-792.
[9]Rublee E,Rabaud V,Konolige K,et al.ORB:an efficient alternative to SIFT or SURF[C]∥Proceedings of 2011 International Conference on Computer Vision,2011:2564-2571.
[10]Leutenegger S,Chli M,Siegwart R Y.BRISK:binary robust invariant scalable keypoints[C]∥Proceedings of 2011 International Conference on Computer Vision,2011:2548-2555.
[11]Alahi A,Ortiz R,Vandergheynst P.FREAK:fast retina keypoint[C]∥Proceedings of 2012 IEEEConference on Computer Vision and Pattern Recognition,2012:510-517.
[12]Rosten E,Drummond T.Machine learning for highspeed corner detection[C]∥Proceedings of 9th European Conference on Computer Vision,2006:430-443.
[13]Harris C G,Stephens M J.A combined corner and edge detector[C]∥Proceedings of the 4th Alvey Vision Conference,1998,15…50.
[14]Sattler T,Leibe B,Kobbelt L.Efficient&effective prioritized matching for large-scale image-based localization[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2017,39(9):1744-1756.
[15]Hossein-Nejad Z,Nasri M.An adaptive image registration method based on SIFT features and RANSAC transform[J].Computers&Electrical Engineering,2017,62:524-537.
[16]Zou P P,Zhang Z L,Wang P,et al.Binocular camera calibration based on collinear vector and plane homography[J].Acta Optica Sinica,2017,37(11):1115006.邹朋朋,张滋黎,王平,等.基于共线向量与平面单应性的双目相机标定方法[J].光学学报,2017,37(11):1115006.
[17]Nguyen T,Chen S W,Shivakumar S S,et al.Unsupervised deep homography:a fast and robust homography estimation model[J].IEEE Robotics and Automation Letters,2018,3(3):2346-2353.