Skip to main content
Log in

A refined coherent point drift (CPD) algorithm for point set registration

  • Research Papers
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

The coherent point drift (CPD) algorithm is a powerful approach for point set registration. However, it suffers from a serious problem-there is a weight parameter w that reflects the assumption about the amount of noise and number of outliers in the Gaussian mixture model, and its value has an influence on the point set registration performance In the original CPD algorithm, the value of w is set manually, and hence an improper value will lead to poor registration results. To solve this problem, a fully automatic algorithm for the selection of an optimal weight parameter is proposed using a hybrid optimization scheme that combines the genetic algorithm with the Nelder-Mead simplex method. The experiments show that the refined CPD algorithm is more effective and extends the original CPD algorithm in its methodology and applications.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Besl P J, McKay N D. A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell, 1992, 14: 239–256

    Article  Google Scholar 

  2. Chetverikov D, Stepanov D, Krsek P. Robust euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm. Image Vision Comput, 2005, 23: 299–309

    Article  Google Scholar 

  3. Sharp G C, Lee S W, Wehe D K, et al. ICP registration using invariant features. IEEE Trans Pattern Anal Mach Intell, 2002, 24: 90–102

    Article  Google Scholar 

  4. Fitzgibbon A W. Robust registration of 2D and 3D point sets. Image Vision Comput, 2003, 21: 1145–1153

    Article  Google Scholar 

  5. Jian B, Vemuri B C. A robust algorithm for point set registration using mixture of Gaussians. In: Proceedings of the 10th IEEE International Conference on Computer Vision, Beijing, China, 2005. 1246–1251

  6. Jian B, Vemuri B C. Robust point set registration using Gaussian mixture models. IEEE Trans Pattern Anal Mach Intell, 2011, 33: 1633–1645

    Article  Google Scholar 

  7. Chui H, Rangarajan A. A feature registration framework using mixture models. In: Proceedings of IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, Hilton Head Island, SC, USA, 2000. 190–197

  8. Chui H, Rangarajan A, Zhang J, et al. Unsupervised learning of an atlas from unlabeled point-sets. IEEE Trans Pattern Anal Mach Intell, 2004, 26: 160–172

    Article  Google Scholar 

  9. Myronenko A, Song X, Miguel A, et al. Non-rigid point set registration: coherent point drift. Adv Neural Inf Process Syst, 2006, 19: 1009–1016

    Google Scholar 

  10. Myronenko A, Song X. Point set registration: Coherent point drifts. IEEE Trans Pattern Anal Mach Intell, 2010, 32: 2262–2275

    Article  Google Scholar 

  11. Horaud R, Forbes F, Yguel M, et al. Rigid and articulated point registration with expectation conditional maximization. IEEE Trans Pattern Anal Mach Intell, 2011, 33: 587–602

    Article  Google Scholar 

  12. Goldberg D E. Genetic Algorithms in Search, Optimization, and Machine Learning. Boston: Addison-wesley, 1989

    MATH  Google Scholar 

  13. Lagarias J C, Reeds J A, Wright M H, et al. Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM J Optim, 1999, 9: 112–147

    Article  MathSciNet  Google Scholar 

  14. Fraley C, Raftery A E. Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc, 2002, 97: 611–631

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Peng Wang or Ping Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, P., Wang, P., Qu, Z. et al. A refined coherent point drift (CPD) algorithm for point set registration. Sci. China Inf. Sci. 54, 2639–2646 (2011). https://doi.org/10.1007/s11432-011-4465-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-011-4465-7

Keywords

Navigation