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A Novel Phase Congruency-Based Image Matching Method for Heterogenous Images

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6GN for Future Wireless Networks (6GN 2023)

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Abstract

Since different imaging principles of different ges lead to imaging results exhibiting nonlinear intensity differences, this phenomenon makes the traditional image alignment methods based on image gradients challenging. To solve this problem, instead of using image pixel intensity as the basis for image features, this paper searches for the relationship between phase and contour structure in digital images and summarizes this relationship as a new method for describing image structure. The method is light and contrast invariant, and furthermore, after phase voting, the method has a more powerful description between structure spaces. The proposed method is effective for image alignment of images from different sources.

This Work Was Supported By the Shanghai Science and Technology Commission Science and Technology Plan Project: 21010501000.

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References

  1. Yu, L., Zhang, D.R., Holden, E.J.: A fast and fully automatic registration approach based on point features for multi-source remotesensing images. Comput. Geosci. 34(7), 838–848 (2008)

    Article  Google Scholar 

  2. Sui, H.G., Xu, C., Liu, J.Y., Hua, F.: Automatic optical-to-SAR image registration by iterative line extraction and Voronoi integrated spectral point matching. IEEE Trans. Geosci. Remote Sens. 53(11), 6058–6072 (2015)

    Article  Google Scholar 

  3. Gonçalves, H., Gonçalves, J., Corte-Real, L.: HAIRIS: a method for automatic image registration through histogram-based image segmentation. IEEE Trans. Image Process. 20(3), 776–789 (2011)

    Article  MathSciNet  Google Scholar 

  4. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  5. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  6. Ye, Y., Shen, L.: Hopc: a novel similarity metric based on geometric structural properties for multi-modal remote sensing image matching. In: ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences; 12–19 July 2016, Prague, pp. 9–16. ISPRS, Gottingen (2016)

    Google Scholar 

  7. Li, Z., Mahapatra, D., Tielbeek, J.A.W., Stoker, J., van Vliet, L.J., Vos, F.M.: Image registration based on autocorrelation of local structure. IEEE Trans. Med. Imag. 35(1), 63–75 (2016)

    Article  Google Scholar 

  8. Rivaz, H., Karimaghaloo, Z., Collins, D.L.: Self-similarity weighted mutual information: a new nonrigid image registration metric. Med. Image Anal. 18(2), 343–358 (2014)

    Article  Google Scholar 

  9. Kovesi, P.: Image features from phase congruency, Videre. J. Comput. Vis. Res. 1(3), 1–26 (1999)

    Google Scholar 

  10. Ye, Y., Shan, J., Bruzzone, L., Shen, L.: Robust registration of multimodal remote sensing images based on structural similarity. IEEE Trans. Geosci. Remote Sens. 55(5), 2941–2958 (2017). https://doi.org/10.1109/TGRS.2017.2656380

    Article  Google Scholar 

  11. Lu, J., Hu, M., Dong, J., Han, S., Su, A.: A novel dense descriptor based on structure tensor voting for multi-modal image matching. Chinese J. Aeronaut. 33(9), 2408–2419 (2020). https://doi.org/10.1016/j.cja.2020.02.002. ISSN 1000-9361

  12. Spacek, L.A.: The Detection of Contours and their Visual Motion. Ph.D. thesis, University of Essex at Colchester (1985)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electronic Information, Shanghai Dianji University, Shanghai, China

    Donghua Li & Minzheng Li

Authors
  1. Donghua Li
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  2. Minzheng Li
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Correspondence to Donghua Li .

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Editors and Affiliations

  1. Shanghai Dianji University, Shanghai, China

    Jingchao Li

  2. Kanagawa University, Yokohama, Japan

    Bin Zhang

  3. Shanghai University of Electric Power, Yangpu, China

    Yulong Ying

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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Li, D., Li, M. (2024). A Novel Phase Congruency-Based Image Matching Method for Heterogenous Images. In: Li, J., Zhang, B., Ying, Y. (eds) 6GN for Future Wireless Networks. 6GN 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 554. Springer, Cham. https://doi.org/10.1007/978-3-031-53404-1_4

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  • DOI: https://doi.org/10.1007/978-3-031-53404-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53403-4

  • Online ISBN: 978-3-031-53404-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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