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Laser & Optoelectronics Progress, Volume. 59, Issue 12, 1211002(2022)

Heterogeneous Image Matching Based on Improved SIFT Algorithm

Ziwen Yu, Ning Zhang*, Yue Pan**, Yue Zhang, and Yuxuan Wang
Author Affiliations
  • School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, Jilin , China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (17)
    Equations (24)
    References (19)
    Cited By (6)
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    Figures & Tables(17)
    Flow chart of proposed algorithm

    Fig. 1. Flow chart of proposed algorithm

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    Improved extremum detection

    Fig. 2. Improved extremum detection

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    Process of coordinate rotation

    Fig. 3. Process of coordinate rotation

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    Feature points of visible light image

    Fig. 4. Feature points of visible light image

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    Feature points of infrared image

    Fig. 5. Feature points of infrared image

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    Feature points of visible image. (a) SIFT algorithm；(b）proposed algorithm

    Fig. 6. Feature points of visible image. (a) SIFT algorithm；(b）proposed algorithm

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    Feature points of infrared image. (a) SIFT algorithm; (b) proposed algorithm

    Fig. 7. Feature points of infrared image. (a) SIFT algorithm; (b) proposed algorithm

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    Distribution of 9 subregions

    Fig. 8. Distribution of 9 subregions

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    Group 1 experimental data. (a) Visible light image; (b) infrared image

    Fig. 9. Group 1 experimental data. (a) Visible light image; (b) infrared image

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    Processing results of first group of experimental data by different algorithms. (a)‒(c) Connection diagrams when SIFT algorithm, ASIFT algorithm, and proposed algorithm are matched; (d) transform of infrared and visible images and contour overlap of matching results of proposed algorithm

    Fig. 10. Processing results of first group of experimental data by different algorithms. (a)‒(c) Connection diagrams when SIFT algorithm, ASIFT algorithm, and proposed algorithm are matched; (d) transform of infrared and visible images and contour overlap of matching results of proposed algorithm

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    Group 2 experimental data. (a) Visible light image; (b) infrared image

    Fig. 11. Group 2 experimental data. (a) Visible light image; (b) infrared image

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    Processing results of second group of experimental data by different algorithms. (a)‒(c) Connection diagrams when SIFT algorithm, ASIFT algorithm, and proposed algorithm are matched; (d) transform of infrared and visible images and contour overlap of matching results of proposed algorithm

    Fig. 12. Processing results of second group of experimental data by different algorithms. (a)‒(c) Connection diagrams when SIFT algorithm, ASIFT algorithm, and proposed algorithm are matched; (d) transform of infrared and visible images and contour overlap of matching results of proposed algorithm

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    Group 3 experimental data. (a) Visible light image; (b) infrared image

    Fig. 13. Group 3 experimental data. (a) Visible light image; (b) infrared image

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    Processing results of third group of experimental data by different algorithms. (a)‒(c) Connection diagrams when SIFT algorithm, ASIFT algorithm, and proposed algorithm are matched; (d) transform of infrared and visible images and contour overlap of matching results of proposed algorithm

    Fig. 14. Processing results of third group of experimental data by different algorithms. (a)‒(c) Connection diagrams when SIFT algorithm, ASIFT algorithm, and proposed algorithm are matched; (d) transform of infrared and visible images and contour overlap of matching results of proposed algorithm

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    Group 4 experimental data. (a) Visible light image; (b) infrared image

    Fig. 15. Group 4 experimental data. (a) Visible light image; (b) infrared image

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    Processing results of fourth group of experimental data by different algorithms. (a)‒(c) Connection diagrams when SIFT algorithm, ASIFT algorithm, and proposed algorithm are matched; (d) transform of infrared and visible images and contour overlap of matching results of proposed algorithm

    Fig. 16. Processing results of fourth group of experimental data by different algorithms. (a)‒(c) Connection diagrams when SIFT algorithm, ASIFT algorithm, and proposed algorithm are matched; (d) transform of infrared and visible images and contour overlap of matching results of proposed algorithm

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    • Table 1. Comparison of registration results

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      Table 1. Comparison of registration results

      Experimental dataAlgorithmNumber of feature pointsCorrect matching pointRMSE /pixelTime /s
      Reference imagesImages to be registered
      Data 1SIFT55631781844.327.83
      ASIFT2350022727435.819.941
      Proposed algorithm548716421351.526.58
      Data 2SIFT2319820102.317.893
      ASIFT106048982484.211.833
      Proposed algorithm2258918631.315.62
      Data 3SIFT5994181310Mismatch28.32
      ASIFT2112014279294.815.602
      Proposed algorithm532113691851.224.88
      Data 4SIFT529912532Mismatch23.38
      ASIFT22112153420Mismatch15.761
      Proposed algorithm510211241161.723.42
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    Ziwen Yu, Ning Zhang, Yue Pan, Yue Zhang, Yuxuan Wang. Heterogeneous Image Matching Based on Improved SIFT Algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1211002

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    Paper Information

    Category: Imaging Systems

    Received: Jun. 24, 2021

    Accepted: Aug. 31, 2021

    Published Online: May. 23, 2022

    The Author Email: Ning Zhang (zhangning@cust.edu.cn), Yue Pan (panyue@cust.edu.cn)

    DOI:10.3788/LOP202259.1211002

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology