Fast stitching method for dense repetitive structure images based on grid-based motion statistics algorithm and optimal seam

doi:10.11772/j.issn.1001-9081.2019061045

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (1): 239-244.DOI: 10.11772/j.issn.1001-9081.2019061045

• Virtual reality and multimedia computing • Previous Articles Next Articles

Fast stitching method for dense repetitive structure images based on grid-based motion statistics algorithm and optimal seam

MU Qi^1,2, TANG Yang¹, LI Zhanli¹, LI Hong'an¹

1. College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China;
2. College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China

Received:2019-06-20 Revised:2019-08-07 Online:2020-01-10 Published:2019-10-15
Contact: 牟琦
Supported by:
This work is partially supported by the China Postdoctoral Science Foundation (2016M602941XB).

基于网格运动统计算法和最佳缝合线的密集重复结构图像快速拼接方法

牟琦^1,2, 唐洋¹, 李占利¹, 李洪安¹

1. 西安科技大学计算机科学与技术学院, 西安 710054;
2. 西安科技大学机械工程学院, 西安 710054

作者简介:牟琦(1974-),女,陕西西安人,副教授,博士研究生,主要研究方向:人工智能、计算机视觉、图像处理;唐洋(1994-),男,湖北孝感人,硕士研究生,主要研究方向:计算机视觉、图像处理;李占利(1964-),男,陕西周至人,教授,博士,主要研究方向:计算机图形学、图像处理;李洪安(1978-),男,山东武城人,副教授,博士,主要研究方向:图形图像处理、计算机视觉。
基金资助:
中国博士后科学基金资助项目（2016M602941XB）。

Abstract

Abstract: For the images with dense repetitive structure, the common algorithms will lead to a large number of false matches, resulting in obvious ghosting in final image and high time consumption. To solve the above problems, a fast stitching method for dense repetitive structure images was proposed based on Grid-based Motion Statistics (GMS) algorithm and optimal seam algorithm. Firstly, a large number of coarse matching points were extracted from the overlapping regions. Then, the GMS algorithm was used for precise matching, and the transformation model was estimated based on the above. Finally, the dynamic-programming-based optimal seam algorithm was adopted to complete the image stitching. The experimental results show that, the proposed method can effectively stitch images with dense repetitive structures. Not only ghosting is effectively suppressed, but also the stitching time is significantly reduced, the average stitching speed is 7.4 times and 3.2 times of the traditional Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) algorithms respectively, 4.1 times as fast as the area-blocking-based SIFT algorithm, 1.4 times as fast as the area-blocking-based SURF algorithm. The proposed algorithm can effectively eliminate the ghosting of dense repetitive structure splicing and shorten the stitching time.

Key words: image stitching, Grid-based Motion Statistics (GMS) algorithm, precise feature matching, optimal seam, image fusion

摘要： 针对常用的图像拼接算法对具有密集重复结构的图像会产生大量误匹配点从而出现明显鬼影且耗时较长的问题，将网格运动统计（GMS）算法与最佳缝合线算法相结合，提出了一种密集重复结构的图像快速拼接方法。首先，在图像的重叠区域提取大量粗匹配点；接着，采用GMS算法进行精匹配，然后在此基础上估计变换模型；最后，采用基于动态规划思想的最佳缝合线算法完成图像拼接。实验结果表明，将所提算法应用于两组具有密集重复结构的图像上，不仅可以有效消除鬼影，得到理想的拼接效果，而且显著减少了拼接时间；平均拼接速度分别是传统尺度不变特征变换（SIFT）和加速稳健特征（SURF）算法的7.4倍和3.2倍，分别是结合区域分块的SIFT算法和SURF算法的4.1倍和1.4倍。所提算法能够有效地消除密集重复结构拼接时的鬼影，同时缩短了拼接时间。

关键词: 图像拼接, 网格加速统计算法(GMS), 特征精匹配, 最佳缝合线, 图像融合

CLC Number:

MU Qi, TANG Yang, LI Zhanli, LI Hong'an. Fast stitching method for dense repetitive structure images based on grid-based motion statistics algorithm and optimal seam[J]. Journal of Computer Applications, 2020, 40(1): 239-244.

牟琦, 唐洋, 李占利, 李洪安. 基于网格运动统计算法和最佳缝合线的密集重复结构图像快速拼接方法[J]. 计算机应用, 2020, 40(1): 239-244.

References

[1] KANKONKAR J T G, NAIK N. Image security using image encryption and image stitching[C]//Proceedings of the 2017 International Conference on Computing Methodologies and Communication. Piscataway:IEEE, 2017:151-154.
[2] LI Z, ISLER V. Large scale image mosaic construction for agricultural applications[J]. IEEE Robotics and Automation Letters, 2016, 1(1):295-302.
[3] 韩李涛,刘海龙,孔巧丽,等.基于多核计算环境的地貌晕渲并行算法[J].计算机应用,2017,37(7):1911-1915,1920.(HAN L T, LIU H L, KONG Q L, et al. Parallel algorithm for hillshading under multi-core computing environment[J]. Journal of Computer Applications, 2017, 37(7):1911-1915, 1920.)
[4] WANG G, ZHAI Z, XU B, et al. A parallel method for aerial image stitching using ORB feature points[C]//Proceedings of the 2017 IEEE/ACIS 16th International Conference on Computer and Information Science. Piscataway:IEEE, 2017:769-773.
[5] LI N, XU Y, WANG C. Quasi-homography warps in image stitching[J]. IEEE Transactions on Multimedia, 2018, 20(6):1365-1375.
[6] UYTTENDAELE M, EDEN A, SKELISKI R. Eliminating ghosting and exposure artifacts in image mosaics[C]//Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2001:509-516.
[7] LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2):91-110.
[8] BAY H, ESS A, TUYTELAARS T, et al. Speeded-Up Robust Features (SURF)[J]. Computer Vision and Image Understanding, 2008, 110(3):346-359.
[9] 蔡怀宇,武晓宇,卓励然,等.结合边缘检测的快速SIFT图像拼接方法[J].红外与激光工程,2018,47(11):441-447.(CAI H Y, WU X Y, ZHUO L R, et al. Fast SIFT image stitching algorithm combining edge detection[J]. Infrared and Laser Engineering, 2018, 47(11):441-447.)
[10] 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.
[11] 李玉峰,李广泽,谷绍湖,等.基于区域分块与尺度不变特征变换的图像拼接算法[J].光学精密工程,2016,24(5):1197-1205.(LI Y F, LI G Z, GU S H, et al. Image mosaic algorithm based on area blocking and SIFT[J]. Optics and Precision Engineering, 2016, 24(5):1197-1205.)
[12] ZHOU X, ZHANG H, WANG Y. A multi-image stitching method and quality evaluation[C]//Proceedings of the 2017 4th International Conference on Information Science and Control Engineering. Piscataway:IEEE, 2017:46-50.
[13] 何宾,陶丹,彭勃.高实时性F-SIFT图像拼接算法[J].红外与激光工程,2013,42(S2):440-444.(HE B, TAO D, PENG B. High real-time F-SIFT image mosaic algorithm[J]. Infrared and Laser Engineering, 2013, 42:440-444.)
[14] PATEL M S, PATEL N M, HOLIA M S. Feature based multi-view image registration using SURF[C]//Proceedings of the 2015 International Symposium on Advanced Computing and Communication. Piscataway:IEEE, 2015:213-218.
[15] RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB:an efficient alternative to SIFT or SURF[C]//Proceedings of the 2011 International Conference on Computer Vision. Piscataway:IEEE, 2011:2564-2571.
[16] WANG M, NIU S, YANG X. A novel panoramic image stitching algorithm based on ORB[C]//Proceedings of the 2017 International Conference on Applied System Innovation. Piscataway:IEEE, 2017:818-821.
[17] 邹承明,侯小碧,马静.基于几何学图像配准的SIFT图像拼接算法[J].华中科技大学学报(自然科学版),2016,44(4):32-36.(ZOU C M, HOU X B, MA J. SIFT image stitching based on geometric image registration solution[J]. Journal of Huazhong University of Science and Technology (Nature Science Edition), 2016, 44(4):32-36.)
[18] LIN W, LIU S, JIANG N, et al. RepMatch:robust feature matching and pose for reconstructing modern cities[C]//Proceedings of the 2016 European Conference on Computer Vision, LNCS 9905. Cham:Springer, 2016:562-579.
[19] LIN W-Y D, CHENG M-M, LU J, et al. Bilateral functions for global motion modeling[C]//Proceedings of the 2014 European Conference on Computer Vision, LNCS 8692. Cham:Springer, 2014:341-356.
[20] BIAN J, LIN W, MATSUSHITA Y. GMS:grid-based motion statistics for fast, ultra-robust feature correspondence[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2017:2828-2837.
[21] DUPLAQUET M-L. Building large image mosaics with invisible seam lines[J]. Proceedings of SPIE, 1998, 3387:369-377.
[22] 方贤勇,潘志庚,徐丹.图像拼接的改进算法[J]. 计算机辅助设计与图形学学报,2003,15(11):1362-1365.(FANG X Y, PAN Z G, XU D. An improved algorithm for image mosaics[J]. Journal of Computer-Aided Design and Computer Graphics, 2003, 15(11):1362-1365.)

Fast stitching method for dense repetitive structure images based on grid-based motion statistics algorithm and optimal seam

基于网格运动统计算法和最佳缝合线的密集重复结构图像快速拼接方法

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