Laser & Optoelectronics Progress, Volume. 57, Issue 12, 121014(2020)

Image Registration Algorithm for Optimizing Grid Motion Statistics

Qianghan Jia1, Zhifeng Zhou1、*, and Liduan Wang2
Author Affiliations
  • 1Faculty of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
  • 2Shanghai Sinan Satellite Navigation Technology Co., Ltd., Shanghai 201801, China
  • show less
    References(18)

    [2] Xu J J, Zhang Y, Zhang H. Fast image registration algorithm based on improved Harris-SIFT descriptor[J]. Journal of Electronic Measurement and Instrumentation, 29, 48-54(2015).

    [3] Tran Q H, Chin T J, Carneiro G et al. In defence of RANSAC for outlier rejection in deformable registration[M]. ∥ Fitzgibbon A, Lazebnik S, Perona P, et al. Computer Vision -ECCV 2012. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 7576, 274-287(2012).

    [4] Wang M S, Niu S Z, Yang X. A novel panoramic image stitching algorithm based on ORB. [C]∥2017 International Conference on Applied System Innovation, May 13-17, 2017, Sapporo, Japan. New York: IEEE, 818-821(2017).

    [5] Dai X M, Lang L, Chen M Y. Research of image feature point matching based on improved ORB algorithm[J]. Journal of Electronic Measurement and Instrumentation, 30, 233-240(2016).

    [7] Tian W, Wang H Y, Xu F et al. Enhanced RANSAC with adaptive pre-verification[J]. Journal of Image and Graphics, 14, 973-977(2009).

    [8] Dong Q, Liu J H, Wang C et al. Image mosaic algorithm based on improved BRISK[J]. Journal of Electronics & Information Technology, 39, 444-450(2017).

    [9] Zhao T, Kang H L, Zhang Z P. Fast image mosaic algorithm based on area blocking and BRISK[J]. Laser & Optoelectronics Progress, 55, 031055(2018).

    [10] Leutenegger S, Chli M, Siegwart R Y. BRISK: binary robust invariant scalable keypoints. [C]∥2011 International Conference on Computer Vision, November 6-13, 2011, Barcelona, Spain. New York: IEEE, 2548-2555(2011).

    [11] Bian J W, Lin W Y, Matsushita Y et al. GMS: grid-based motion statistics for fast, ultra-robust feature correspondence. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, 2017, Honolulu, HI. New York: IEEE, 2828-2837(2017).

    [14] Rublee E, Rabaud V, Konolige K et al. ORB: an efficient alternative to SIFT or SURF. [C]∥ 2011 International Conference on Computer Vision, November 6-13, 2011, Barcelona, Spain. New York: IEEE, 2564-2571(2011).

    [15] Lin W Y D, Cheng M M, Lu J et al. Bilateral functions for global motion modeling[M]. ∥Pajdla T, Schiele B, Tuytelaars T, et al. Computer Vision-ECCV 2014. Lecture Notes in Computer Science. Cham: Springer, 8692, 341-356(2014).

    [16] Wang C, Wang L, Liu L Q. Density maximization for improving graph matching with its applications[J]. IEEE Transactions on Image Processing, 24, 2110-2123(2015).

    [17] Wang C H. A BF fast matching algorithm structure for reducing false positive rate. C]∥China Proceedings of the 2010 National Conference on Communication Security, China Communications Society Communication Security Technical Committee: China Communications Society, 285-290(2010).

    [18] Kwon O S, Ha Y H. Panoramic video using scale-invariant feature transform with embedded color-invariant values[J]. IEEE Transactions on Consumer Electronics, 56, 792-798(2010).

    Tools

    Get Citation

    Copy Citation Text

    Qianghan Jia, Zhifeng Zhou, Liduan Wang. Image Registration Algorithm for Optimizing Grid Motion Statistics[J]. Laser & Optoelectronics Progress, 2020, 57(12): 121014

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Image Processing

    Received: Sep. 5, 2019

    Accepted: Nov. 2, 2019

    Published Online: Jun. 3, 2020

    The Author Email: Zhou Zhifeng (zhousjtu@126.com)

    DOI:10.3788/LOP57.121014

    Topics