Image stitching enhancement method with symmetrical constraint of local feature points

被引：0

作者：

Zhong M. ^{[1
]}

Pei J. ^{[1
]}

机构：

[1] College of Electronics and Information Engineering, Shenzhen University, Shenzhen

来源：

Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2022年 / 43卷

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

computer vision; feature point matching; image stitching; internal parameter matrix; local constraint;

D O I：

10.7527/S1000-6893.2022.26948

中图分类号：

学科分类号：

摘要：

In this paper，an image mosaic enhancement method with symmetrical constraint of local feature points is proposed. Firstly，according to the internal and external parameter matrix of the camera，the forward local constraint region of the feature points is calculated，and the set of positive matching points is obtained. Then，the inverse local constraint region is calculated，and the set of inverse matching points is obtained. The intersection in the set of forward and reverse matching point pairs is found，and the final correct matching point pair set which satisfies the symmetric constraint condition is obtained. Combining this enhancement method with the existing advanced stitching model learning algorithm，the optimal image transformation is obtained，and finally the stitched image is obtained according to the image transformation model. This method effectively overcomes the problem that the threshold of the RANSAC algorithm needs to be manually adjusted when learning different models，thus reducing the parameter sensitivity of the algorithm，and improving the image stitching accuracy. Experiments show that our method is superior to existing methods both qualitatively and quantitatively. © 2022 AAAS Press of Chinese Society of Aeronautics and Astronautics. All rights reserved.

引用

共 23 条

[1] BROWN M, LOWE D G., Automatic panoramic image stitching using invariant features, International Journal of Computer Vision, 74, 1, pp. 59-73, (2007)
[2] SZELISKI R., Image alignment and stitching: A tutorial, Foundations and Trends in Computer Graphics and Vision, 2, 1, pp. 1-104, (2007)
[3] KUMAR R, ANANDAN P, IRANI M, Et al., Representation of scenes from collections of images, Proceedings IEEE Workshop on Representation of Visual Scenes (In Conjunction with ICCV'95), pp. 10-17, (1995)
[4] ZHAO J F, CHEN Y T, FENG H J, Et al., Infrared image enhancement through saliency feature analysis based on multi-scale decomposition, Infrared Physics & Technology, 62, pp. 86-93, (2014)
[5] ZARAGOZA J, CHIN T J, TRAN Q H, Et al., As-projective-as-possible image stitching with moving DLT, IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 7, pp. 1285-1298, (2014)
[6] CHANG C H, SATO Y, CHUANG Y Y., Shape-preserving half-projective warps for image stitching, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3254-3261, (2014)
[7] LIN K M, JIANG N J, CHEONG L F, Et al., SEAGULL: Seam-guided local alignment for parallax-tolerant image stitching, Computer Vision-ECCV 2016, pp. 370-385, (2016)
[8] ZHANG F, LIU F., Parallax-tolerant image stitching, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3262-3269, (2014)
[9] LOWE D G., Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, 60, 2, pp. 91-110, (2004)
[10] FISCHLER M A, BOLLES R C., A paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM-CACM, pp. 381-395, (1981)

← 1 2 3 →