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A Fast and Accurate Point Pattern Matching Algorithm Based on Multi-Hilbert Scans

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Pattern Recognition (ACPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13189))

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Abstract

This paper proposes a novel distance measurement using multi-Hilbert scans for matching point patterns on images. A modified Hausdorff distance has been widely used for point pattern matching, recognition tasks, and evaluation of medical image segmentation. However, the computation cost increases sharply with the number of feature points or the increase of data sets. Multi-Hilbert Scanning Distance (MHSD) based on sets of one-dimensional points using Hilbert scans is introduced to overcome this problem. MHSD consists of a combination of four directional Hilbert scans and diagonally shifted Hilbert scans. The proposed method was tested on vehicle images and compared with Hausdorff distance, partial Hausdorff distance, and modified Hausdorff distance. Experimental results show that the proposed method outperforms the compared methods.

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References

  1. Hu, X.P., Ahuja, N.: Matching point features with ordered geometric, rigidity, and disparity constraints. IEEE Trans. Pattern Anal. Mach. Intell. 16(10), 1041–1049 (1994)

    Article  Google Scholar 

  2. Wen, Y., Zhou, M., Zhang, P., Geng, G.: Matching method of cultural relic fragments constrained by thickness and contour feature. IEEE Access 8, 25892–25904 (2020)

    Article  Google Scholar 

  3. Meng, X., et al.: Multi-feature fusion: a driver-car matching model based on curve comparison. IEEE Access 7, 83526–83535 (2019)

    Article  Google Scholar 

  4. Czovny, R.K., Bellon, O.R.P., Silva, L., Costa, H.S.G.: Minutia matching using 3D pore clouds. In: Proceedings of the International Conference on Pattern Recognition, pp. 3138–3143. IAPR (2018)

    Google Scholar 

  5. Dubuisson, M.P., Jain, A.K.: A modified Hausdorff distance for object matching. In: Proceedings of the International Conference on Pattern Recognition, pp. 566–568. IAPR (1994)

    Google Scholar 

  6. Yu, C.B., Qin, H.F., Cui, Y.Z., Hu, X.Q.: Finger-vein image recognition combining modified Hausdorff distance with minutiae feature matching. J. Biomed. Sci. Eng. 2(4), 261–272 (2009)

    Article  Google Scholar 

  7. Shao, F., Cai, S., GU, J.: A modified Hausdorff distance based algorithm for 2-dimensional spatial trajectory matching. In: Proceedings of the International Conference on Computer Science and Education, pp. 166–172. IEEE (2010)

    Google Scholar 

  8. Schhutze, O., Esquivel, X., Lara, A., Coello, C.A.C.: Using the averaged Hausdorff distance as a performance measure in evolutionary multiobjective optimization. IEEE Trans. Evol. Comp. 16(4), 504–522 (2012)

    Article  Google Scholar 

  9. Kim, J., Kim, M., Kim, T.: Recognition of face orientation angle using modified hausdorff distance. In: Proceedings of International Symposium on Consumer Electronics. IEEE (2014)

    Google Scholar 

  10. Sarangi, P.P., Panda, M., Mishra, B.S.P., Dehuri, S.: An automated ear localization technique based on modified hausdorff distance. In: Proceedings of the International Conference on Computer Vision and Image Processing, pp. 229–240. IAPR (2016)

    Google Scholar 

  11. Shamai, G., Kimmel, R.: Geodesic distance descriptors. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, pp. 6410–6418. IEEE (2017)

    Google Scholar 

  12. Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15(29), 1–28 (2015)

    Google Scholar 

  13. Taha, A.A., Hanbury, A.: An efficient algorithm for calculating the exact Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 37(11), 2153–2163 (2015)

    Article  Google Scholar 

  14. Jhang, D., Jou, L., Chen, Y., He, F.: Efficient and accurate Hausdorff distance computation based on diffusion search. IEEE Access 6, 1350–1361 (2018)

    Article  Google Scholar 

  15. Ryu, J., Kamata, S.: An efficient computational algorithm for Hausdorff distance based on points-ruling-out and systematic random sampling. Pattern Recogn. 114(107857), 1–12 (2021)

    Google Scholar 

  16. Kamata, S., Eason, R.O., Bandou, Y.: A new algorithm for n-dimensional Hilbert scanning. IEEE Trans. Image Pro. 8(7), 964–973 (1999)

    Article  MathSciNet  Google Scholar 

  17. Hao, P., Kamata, S.: Hilbert scan based bag-of-features for image retrieval. IEICE Trans. Inf. Syst. E94-D(6), 1260–1268 (2011)

    Google Scholar 

  18. Zhang, J, Kamata, S., Ueshige, Y.: A pseudo-Hilbert scan for arbitrarily-sized arrays. IEICE Trans. Inf. Syst. E90-A(3), 682–690 (2007)

    Google Scholar 

  19. Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15, 850–863 (1993)

    Article  Google Scholar 

  20. Gonzlez, R.C., Woods, R.E.: Digital Image Processing. Second edn., Prectice Hall, Upper Saddle River (2002)

    Google Scholar 

  21. Comaniciu, D., Meer, P.: Edge detection with embedded confidence. IEEE Trans. Pattern Anal. Mach. Intell. 23(12), 1351–1365 (2001)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Information, Production and Systems Research Center, Waseda University, Kitakyushu, Japan

    Jegoon Ryu

  2. Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, Japan

    Sei-ichiro Kamata

Authors
  1. Jegoon Ryu
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  2. Sei-ichiro Kamata
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Corresponding author

Correspondence to Jegoon Ryu .

Editor information

Editors and Affiliations

  1. Korea University, Seoul, Korea (Republic of)

    Christian Wallraven

  2. Nanjing University, Nanjing, China

    Qingshan Liu

  3. Osaka University, Osaka, Japan

    Hajime Nagahara

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Ryu, J., Kamata, Si. (2022). A Fast and Accurate Point Pattern Matching Algorithm Based on Multi-Hilbert Scans. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13189. Springer, Cham. https://doi.org/10.1007/978-3-031-02444-3_42

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  • DOI: https://doi.org/10.1007/978-3-031-02444-3_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02443-6

  • Online ISBN: 978-3-031-02444-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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