Statistical optimization feature matching algorithm based on epipolar geometry

ZHAO Chunhui; FAN Bin; TIAN Limin; HU Jinwen; PAN Quan

doi:10.7527/S1000-6893.2018.21727

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2018 , Vol. 39 >Issue 5: 321727 - 321727

DOI: https://doi.org/10.7527/S1000-6893.2018.21727

Electronics and Electrical Engineering and Control

Statistical optimization feature matching algorithm based on epipolar geometry

ZHAO Chunhui ,
FAN Bin ,
TIAN Limin ,
HU Jinwen ,
PAN Quan

Expand

School of Automation, Northwestern Polytechnical University, Xi'an 710072, China

Received date: 2017-09-06

Revised date: 2018-01-24

Online published: 2018-01-24

Supported by

National Natural Science Foundation of China (61473230, 61603303); Aeronautical Science Foundation (2014ZC-53030); Natural Science Foundation of Shaanxi Province (2017JM6027, 2017JQ6005); State Key Laboratory of Geo-information Engineering under grant agreement (SKLGIE2015-M-3-4)

Fold

Abstract

To solve the problems of small matching number and poor matching with repetitive structures in image matching based on feature points, a statistical optimization feature matching algorithm is proposed based on the epipolar geometry. As correct matching feature points satisfy the epipolar constraint, the feature points' search region can be reduced and the mismatches caused by repetitive structures can be avoided. Our approach first uses a small sample of features to estimate the fundamental matrix between images and leverages it for guiding feature matching based the epipolar constraint model. Then we use the statistical optimization method based on scale and main direction information of feature points to further eliminate mismatches and get the final matching results. Experimental results show that our algorithm also has good robustness against image rotation and scale transformation, and the matching precision and number have been greatly improved. Our algorithm is also effective for image matching with repetitive structures.

Key words： feature matching; epipolar geometry; main direction; scale; Sampson distance

Cite this article

ZHAO Chunhui , FAN Bin , TIAN Limin , HU Jinwen , PAN Quan . Statistical optimization feature matching algorithm based on epipolar geometry[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2018 , 39(5) : 321727 -321727 . DOI: 10.7527/S1000-6893.2018.21727

References

[1] HARTLEY R, ZISSERMAN A. Multiple view geometry in computer vision[M]. Cambridge:Cambridge University Press, 2003:191-215.
[2] 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.
[3] ARYA S, MOUNT D M. Approximate nearest neighbor queries in fixed dimensions[C]//SODA'93:Proceedings of the Fourth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA:Society for Industrial and Applied Mathematics, 1993:271-280.
[4] LOWE D G. Distinctive image features from scale-invariant key points[J]. International Journal of Computer Vision, 2004, 60(2):91-110.
[5] MUJA M, LOWE D G. Fast approximate nearest neighbors with automatic algorithm configuration[J]. International Conference on Computer Vision Theory and Applications (VISAPP), 2009, 2(1):331-340.
[6] KUSHNIR M, SHIMSHONI I. Epipolar geometry estimation for urban scenes with repetitive structures[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(12):2381-2395.
[7] ZHANG W, KOSECKA J. Generalized RANSAC framework for relaxed correspondence problems[C]//Third International Symposium on 3D Data Processing, Visualization, and Transmission. Piscataway, NJ:IEEE Press, 2006:854-860.
[8] SUR F, NOURY N, BERGER M O. Image point correspondences and repeated patterns[J]. Computer Vision & Pattern Recognition, 2011, 8(2):216-243.
[9] FISCHLER M A, BOLLES R C. Random sample consensus:A paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6):381-395.
[10] CHIN T J, YU J, SUTER D. Accelerated hypothesis generation for multi-structure data via preference analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(4):625-638.
[11] 王云丽, 张鑫, 高超, 等. 航拍视频拼图中基于特征匹配的全局运动估计方法[J]. 航空学报, 2008, 29(5):1218-1225. WANG Y L, ZHANG X, GAO C, et al. The global motion estimation method based on feature matching in video[J]. Acta Aeronautica et Astronautica Sinica, 2008, 29(5):1218-1225(in Chinese).
[12] 罗楠, 孙权森, 陈强, 等. 针对重复模式图像的成对特征点匹配[J]. 中国图象图形学报, 2015, 20(1):113-124. LUO N, SUN Q S, CHEN Q, et al. Pair-wise feature points based matching algorithm for repetitive patterns images[J]. Journal of Image and Graphics, 2015, 20(1):113-124(in Chinese).
[13] 刘威, 赵文杰, 李德军, 等. 一种基于ORB检测的特征点匹配算法[J]. 激光与红外, 2015, 45(11):1380-1384. LIU W, ZHAO W J, LI D J, et al. Feature points matching algorithm based on ORB detection[J]. Laser and Infrared, 2015, 45(11):1380-1384(in Chinese).
[14] SHAH R, SRIVASTAVA V, NARAYANAN P J. Geometry-aware feature matching for structure from motion applications[C]//2015 IEEE Winter Conference on Applications of Computer Vision (WACV). Piscataway, NJ:IEEE Press, 2015:278-285.
[15] SHAH R, DESHPANDE A, NARAYANAN P J. Multistage SFM:Revisiting incremental structure from motion[C]//20142nd International Conference on 3D Vision (3DV). Piscataway, NJ:IEEE Press, 2014:417-424.
[16] ZHANG Z, DERICHE R, FAUGERAS O, et al. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry[J]. Artificial Intelligence, 1995, 78(1-2):87-119.
[17] 陈洁, 高志强, 密保秀, 等. 引入极线约束的SURF特征匹配算法[J]. 中国图象图形学报, 2016, 21(8):1048-1056. CHEN J, GAO Z Q, MI B X, et al. SURF feature matching based on epipolar constraint[J]. Journal of Image and Graphics, 2016, 21(8):1048-1056(in Chinese).
[18] 李立春, 张小虎, 傅丹, 等. 基于极线局部校正的特征匹配方法[J]. 光学技术, 2008, 34(2):285-288. LI L C, ZH X H, FU D, et al. Feature matching algorithm based on rectification of epipolar-line region[J]. Optical Technique, 2008, 34(2):285-288(in Chinese).
[19] 张培耘, 华希俊, 夏乐春, 等. 基于RANSAC算法的极线约束立体视觉匹配方法研究[J]. 组合机床与自动化加工技术, 2013(11):20-22. ZHANG P G, HUA X J, XIA L C, et al. Stereo matching with epipolar line constraints based on RANSAC algorithm[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2013(11):20-22(in Chinese).
[20] 单欣, 王耀明, 董建萍. 基于RANSAC算法的基本矩阵估计的匹配方法[J]. 上海电机学院学报, 2006, 9(4):66-69. SHAN X, WANG Y M, DONG J P. The matching method based on RANSAC algorithm for estimation of the fundamental matrix[J]. Journal of Shanghai Dianji University, 2006, 9(4):66-69(in Chinese).
[21] 田谨思, 苏剑波. 基于Sampson误差计算单应的立体匹配[J]. 计算机工程与应用, 2005, 41(3):7-9. TIAN J S, SU J P. Stereo matching with epipolar line constraints based on RANSAC algorithm[J]. Computer Engineering and Applications, 2005, 41(3):7-9(in Chinese).
[22] STURM J, ENGELHARD N, ENDRES F, et al. A benchmark for the evaluation of RGB-D SLAM systems[C]//2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway, NJ:IEEE Press, 2012:573-580.
[23] AANAES H, DAHL A L, PEDERSEN K S. Interesting interest points[J]. International Journal of Computer Vision, 2012, 97(1):18-35.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References