Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Precise iterative closest point algorithm with corner point constraint for isotropic scaling registration

  • Special Issue Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

The traditional iterative closest point (ICP) algorithm could register two point sets well, but it is easily affected by local dissimilarity. To deal with this problem, this paper proposes an isotropic scaling ICP algorithm with corner point constraint. First of all, because the corner points can preserve the similarity of the whole shapes, an objective function based on least square error is proposed under the guidance of the corner points. Second, a new ICP algorithm is proposed to complete the isotropic scaling registration. At each iterative step of this new algorithm, the correspondence is built based on the closest point searching, and then a closed-form solution of the transformation is computed. This new algorithm converges monotonically to a local minimum from any given initial scaling transformation. To obtain the expected minimum, the traditional scaling ICP algorithm is applied to compute the initial transformation. The experimental results demonstrate that our algorithm can prevent the influence of the local dissimilarity and improve the registration precision compared with the traditional ICP algorithm.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Yang, Y., Wang, X., Liu, Q., Xu, M., Yu, L.: A bundled-optimization model of multi-view dense depth map synthesis for dynamic scene reconstruction. Inf. Sci. 320, 306–319 (2015)

    Article  Google Scholar 

  2. Gao, Y., Wang, M., Tao, D., Ji, R., Dai, Q.: 3D object retrieval and recognition with hypergraph analysis. IEEE Trans. Image Process. 21(9), 4290–4303 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  3. Du, S., Guo, Y., Sanroma, G., Ni, D., Wu, G., Shen, D.: Building dynamic population graph for accurate correspondence detection. Med. Image Anal. 26(1), 256–267 (2015)

    Article  Google Scholar 

  4. Zhang, J., Gao, Y., Munsell, B., Shen, D.: Detecting anatomical landmarks for fast Alzheimer’s disease diagnosis. IEEE Trans. Med. Imaging 35(12), 2524–2533 (2016)

    Article  Google Scholar 

  5. Besl, P.J., McKay, H.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Article  Google Scholar 

  6. Chen, Y., Gerard, M.: Object modelling by registration of multiple range images. Image Vis. Comput. 10(3), 145–155 (1992)

    Article  Google Scholar 

  7. Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. Int. J. Comput. Vis. 13(2), 119–152 (1994)

    Article  Google Scholar 

  8. Ezra, E., Sharir, M., Efrat, A.: On the performance of the ICP algorithm. Comput. Geom. 41(1–2), 77–93 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  9. Du, S., Liu, J., Zhang, C., Zhu, J., Li, K.: Probability iterative closest point algorithm for m-D point set registration with noise. Neurocomputing 157, 187–198 (2015)

    Article  Google Scholar 

  10. Ying, S., Peng, J., Du, S., Qiao, H.: A scale stretch method based on ICP for 3D data registration. IEEE Trans. Autom. Sci. Eng. 6(3), 559–565 (2009)

    Article  Google Scholar 

  11. Du, S., Liu, J., Bi, B., Zhu, J., Xue, J.: New iterative closest point algorithm for isotropic scaling registration of point sets with noise. J. Vis. Commun. Image Represent. 38, 207–216 (2016)

    Article  Google Scholar 

  12. Umeyama, S.: Least-squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Anal. Mach. Intell. 13(4), 376–380 (1991)

    Article  Google Scholar 

  13. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th alvey vision conference, pp. 147–151 (1988)

  14. Shi, J., Tomasi, C.: Good features to track. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 593–600 (1994)

  15. Nuchter, A., Lingemann, K., Hertzberg, J.: Cached k-d tree search for ICP algorithms. In: Proc. sixth international conference on 3-D digital imaging and modeling (3DIM), pp. 419–426 (2007)

  16. Barber, C.B., Dobkin, D.P., Huhdanpaa, H.: The quickhull algorithm for convex hulls. ACM Trans. Math. Softw. 22(4), 469–483 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  17. Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-Dpoint sets. IEEE Trans. Pattern Anal. Mach. Intell. 9(5), 698–700 (1987)

    Article  Google Scholar 

  18. Du, S., Zheng, N., Ying, S., Liu, J.: Affine iterative closest point algorithm for point set registration. Pattern Recogn. Lett. 31(9), 791–799 (2010)

    Article  Google Scholar 

  19. Latecki, L.J., Lakamper, R., Eckhardt, T.: Shape descriptors for non-rigid shapes with a single closed contour. In: Proc. IEEE conference on computer vision and pattern recognition (CVPR), pp. 424–429 (2000)

  20. Cao, F., Huang, H.K., Pietka, E., Gilsanz, V., Dey, P.S., Gertych, A., Pospiech-Kurkowska, S.: Image database for digital hand atlas. In: Proceedings of medical imaging. PAC sand integrated medical information systems. Design and evaluation, pp. 461–470 (2003)

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61573274 and 61627811, the Fundamental Research Funds for the Central Universities under Grant No. xjj2017005, and the Program of Introducing Talents of Discipline to University under Grant No. B13043.

Author information

Authors and Affiliations

  1. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, People’s Republic of China

    Shaoyi Du, Xuetao Zhang & Guanglin Xu

  2. School of Software Engineering, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, People’s Republic of China

    Wenting Cui & Sirui Zhang

  3. Air Force Engineering University, Xi’an, 710038, Shaanxi, People’s Republic of China

    Liyang Wu

  4. The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710004, Shaanxi, People’s Republic of China

    Meifeng Xu

Authors
  1. Shaoyi Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenting Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liyang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sirui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xuetao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Guanglin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Meifeng Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meifeng Xu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Du, S., Cui, W., Wu, L. et al. Precise iterative closest point algorithm with corner point constraint for isotropic scaling registration. Multimedia Systems 25, 119–126 (2019). https://doi.org/10.1007/s00530-017-0573-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-017-0573-6

Keywords

Search

Navigation