Skip to main content
SpringerLink
Log in

Point-pattern matching based on point pair local topology and probabilistic relaxation labeling

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

This paper presents a robust point-pattern matching (PPM) algorithm, in which the invariant feature and probabilistic relaxation labeling are combined to improve the assignment accuracy and efficiency. A local feature descriptor, namely, point pair local topology (PPLT), is proposed first. The feature descriptor is defined by histogram which is constructed using the weighting of distance measures and angle measures based on local point pair. We use the matching scores of point pair local topology descriptor’s statistic test to define new compatibility coefficients. Then, the robust support functions are constructed based on the obtained compatibility coefficients. Finally, according to the relaxed iterations of matching probability matrix and the mapping constraints required by the bijective correspondence, the correct matching results are obtained. A number of comparison and evaluation experiments on both synthetic point sets and real-world data demonstrate that the proposed algorithm performs better in the presence of outliers and positional jitter. In addition, it achieves the superior performance under similarity and even nonrigid transformation among point sets in the meantime compared with state-of-the-art approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Xiong, Z., Zhang, Y.: A novel interest-point-matching algorithm for high-resolution satellite images. IEEE Trans. Geosci. Remot. Sens. 47(12), 4189–4200 (2009)

    Article  Google Scholar 

  2. Jain, K. A., Lee, J., Jin, R., Gregg, N.: Content-based image retrieval: An application to tattoo images. In: The 16th IEEE International Conference on Image Processing (ICIP), pp. 2745-2748, Cairo, Egypt (2009)

  3. Brown, L.M.: A survey of image registration techniques. ACM Comp. Surv. 24(4), 325–376 (1992)

    Article  Google Scholar 

  4. Mckeon, R., Flynn, P.J.: Three-dimensional facial imaging using a Static Light Screen (SLS) and a dynamic subject. IEEE Trans. Patt. Anal. Mach. Intell. 59(4), 774–783 (2010)

    Google Scholar 

  5. Ma, J., Zhou, H., Zhao, J., Gao, Y., Jiang, J., Tian, J.: Robust feature matching for remote sensing image registration via locally linear transforming. IEEE Trans. Geosci. and Remot. Sens. 53(12), 6469–6481 (2015)

    Article  Google Scholar 

  6. Li, H., Kim, E., Huang, X., He, L.: Object matching with a locally affine-invariant constraint. In: The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, California, USA (2010)

  7. Jiang, T., Jurie, F., Schmid, C.: Learning shape prior models for object matching. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 848-855, Miami, FL, USA (2009)

  8. Sfikas, K., Theoharis, T.: Non-rigid 3D object retrieval using topological information guided by conformal factors. Vis. Comp. 28(9), 943–955 (2012)

    Article  Google Scholar 

  9. Dou, H., Barker, L. M., Ju, T.: Graph-based deformable matching of 3D line with application in protein fitting. Vis. Comp. 967–977 (2015)

  10. Veltkamp, R., Hagedoorn, M.: State-of-the-art in shape matching [J]. Utrecht University, Tech. Rep. UU-CS-1999-27 (1999)

  11. Besel, J.P., Mckay, H.D.: A method for registration of 3-D shapes. IEEE Trans. Patt. Anal. Mach. Intel. 14(2), 239–256 (1992)

    Article  Google Scholar 

  12. Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. Comp. Vision Image Underst. 89(2), 114–141 (2003)

    Article  MATH  Google Scholar 

  13. Ma, J., Zhao, J., Tian, J., Yuille, A.L., Tu, Z.: Robust point matching via vector field consensus. IEEE Trans. Image Process. 23(4), 1706–1721 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  14. Myronenko, A., Song, X.: Point set registration: coherent point drift. IEEE Trans. Patt. Anal. Mach. Intell. 32(12), 2262–2275 (2010)

    Article  Google Scholar 

  15. Ma, J., Zhao, J., Yuille, A.L.: Non-rigid point set registration by preserving global and local structures. IEEE Trans. Image Process. 25(1), 53–64 (2016)

    Article  MathSciNet  Google Scholar 

  16. Shapiro, L.S., Brady, M.: Feature-based correspondence: an eigenvector approach. Image Vision Comp. 10(5), 283–288 (1992)

    Article  Google Scholar 

  17. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Patt. Anal. Mach. Intel. 24(4), 509–522 (2002)

    Article  Google Scholar 

  18. Ma, J., Qiu, W., Zhao, J., Ma, Y., Yuille, A.L., Tu, Z.: Robust L2E estimation of transformation for non-rigid registration. IEEE Trans. Signal Process. 63(5), 1115–1129 (2015)

    Article  MathSciNet  Google Scholar 

  19. Zheng, Y., Doermann, D.: Robust point matching for nonrigid shapes by preserving local neighborhood structures. IEEE Trans. Patt. Anal. Mach. Intel. 28(4), 643–649 (2006)

    Article  Google Scholar 

  20. Zhao, J., Sun, J. X., Zhou, S. L, Li, Z.Y.: Inexact Point Pattern Matching Algorithm Based On Relative Shape Context and Probabilistic Relaxation Labelling. In: 3rdIEEE International Conference on Computer Research and Development (ICCRD), pp. 508–512, Shanghai, China (2011)

  21. Lee, J.H., Won, C.T.: Topology Preserving Relaxation Labeling for Non-rigid Point Matching. IEEE Trans. Patt. Anal. Match. Intel. 33(2), 427–432 (2011)

    Article  Google Scholar 

  22. Wang, H., Hancock, E.R.: Probabilistic relaxation labelling using the Fokker-Planck equation. Patt. Recogn. 41(3), 3393–3411 (2008)

    Article  MATH  Google Scholar 

  23. Kittler, J.: Probabilistic relaxation and the Hough transform. 33(4), 705–714 (2000)

  24. Leordeanu, M., Hebert, M.: A spectral technique for correspondence problems using pairwise constraints. In: The Tenth IEEE International Conference on Computer Vision, Beijing, China, 1482–1489 (2005)

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

    Article  Google Scholar 

  26. Matas, J.: Robust wide-baseline stereo from maximally stable external regions. In: 13th British Machine Vision Conference, 384–3939 (2002)

  27. Caetano, T.S., Mcauley, J., Cheng, L., Le, Q.V.: Learning graph matching. IEEE Trans. Pat. Anal. Mach. Intell. 36(6), 1048–1058 (2009)

    Article  Google Scholar 

Download references

Acknowledgments

We would like to express our gratitude to the associate editor and the reviewers for their time spending and the helpful suggestions, which improves the paper greatly.

Author information

Authors and Affiliations

  1. National University of Defense Technology, No.109, Deya Road, Yanwachi Street, Kaifu District, Changsha, Hunan, 410073, China

    Wanxia Deng, Huanxin Zou, Fang Guo, Lin Lei & Shilin Zhou

Authors
  1. Wanxia Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huanxin Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fang Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lin Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shilin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huanxin Zou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, W., Zou, H., Guo, F. et al. Point-pattern matching based on point pair local topology and probabilistic relaxation labeling. Vis Comput 34, 55–65 (2018). https://doi.org/10.1007/s00371-016-1311-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-016-1311-3

Keywords

Search

Navigation