A High-Efficient Infrared Mosaic Algorithm Based on GMS

  • Xia Pei
  • Baolong GuoEmail author
  • Geng Wang
  • Zhe Huang
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 157)


The most important thing in infrared image mosaic technology is image registration technology. In order to adapt to the real-time requirement of the battlefield, the ORB algorithm in this paper is used for feature extraction. In order to obtain high-quality feature matching points, this paper proposes a new IGMS algorithm on the basis of GMS algorithm. The experimental results show that the correct point matching rate is increased by 8%. Using RNASAC to obtain the transformation matrix between the two images, and finally using the fade-in and fade-out fusion algorithm to obtain a complete wide-field military investigation map.


Feature matching Image mosaic IGMS 


  1. 1.
    Shin, J., Tang, Y.: Deghosting for image stitching with automatic content-awareness. Pattern Recognit. 23(26), 26–27Google Scholar
  2. 2.
    Zheng, W., Zhengchao, C., Bing, Z., et al.: CBERS-1 digital images mosaic and mapping of China. 11(6), 787–791Google Scholar
  3. 3.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110CrossRefGoogle Scholar
  4. 4.
    Bay, H., Tuytelaars, T., Van Gool, L.: Surf: speeded up robust features. In: European Conference on Computer Vision, May 2006CrossRefGoogle Scholar
  5. 5.
    Calonder, M., Lepetit, V., Fua, P.: Keypoint signatures for fast learning and recognition. In: European Conference on Computer Vision (2008)Google Scholar
  6. 6.
    Matungka, R., Zheng, Y.F.: Image registration using adaptive polar transform. IEEE Trans. Image Process. 18(10), 2340–2354 (2009)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Schmid G., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 19(5), 530–534 (1997)CrossRefGoogle Scholar
  8. 8.
    Rublee, E., Rabaud, V., Konolige, K., et al.: ORB: An efficient alternative to SIFT or SURF (2011)Google Scholar
  9. 9.
    Yigitsoy, M., Navab, N.: Structure propagation for image registration. IEEE Trans. Med. Imaging 32(9), 1657–1670 (2013)CrossRefGoogle Scholar
  10. 10.
    Bian, J., Lin, W.-Y., Matsushita, Y., Yeung, S.-K., Nguyen, T.D., Cheng, M.-M.: GMS: grid-based motion statistics for fast, ultra-robust feature correspondence. In: IEEE CVPR (2017)Google Scholar
  11. 11.
    Rublee, E., Rabaud, V., Konolige, K.: ORB: an efficient alternative to SIFT or SURF. IEEE Int. Conf. Comput. Vis. 58(11), 2564–2571 (2011)Google Scholar
  12. 12.
    Rosten, E., Porter, R., Drummond, T.: Faster and better: a machine learning approach to corner detection. IEEE Trans. Pattern Anal. Mach. Intell. 32, 105–119 (2010)CrossRefGoogle Scholar
  13. 13.
    Rosin, P.L.: Measuring corner properties. Comput. Vis. Image Underst. 73(2), 291–307 (1999)CrossRefGoogle Scholar
  14. 14.
    Calonder, C., Lepetit, V., Strecha, C.: BRIEF: binary robust independent elementary features. In: European Conference on Computer Vision, pp. 778–792 (2010)CrossRefGoogle Scholar
  15. 15.
    Chen, W.-K., Chen, H.-P., Tso, H.-K.: A friendly and verifiable image sharing method. J. Netw. Intell. 1(1), 46–51 (2016)Google Scholar
  16. 16.
    Shen, W., Hao, S., Qian, J., Li, L.: Blind quality assessment of dehazed images by analyzing information, contrast, and luminance. J. Netw. Intell. 2(1), 139–146 (2017)Google Scholar
  17. 17.
    Hong, S., Wang, A., Zhang, X., Gui, Z.: Low-dose CT image processing using artifact suppressed total generalized variation. J. Netw. Intell. 3(1), 26–49 (2018)Google Scholar
  18. 18.
    Harold, C., Nelta, N.: Blind images quality assessment of distorted screen content images. J. Netw. Intell. 3(2), 91–101 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Aerospace Science and TechnologyXidian UniversityXi’anPeople’s Republic of China

Personalised recommendations