Advertisement

A Robust Subset-ICP Method for Point Set Registration

  • Junfen Chen
  • Bahari Belaton
  • Zheng Pan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8237)

Abstract

Iterative Closest Point (ICP) is a popular point set registration method often used for rigid registration problems. Because of all points in ICP-based method are processed at each iteration to find their correspondences, the method’s performance is bounded by this constraint. This paper introduces an alternative ICP-based method by considering only subset of points whose boundaries are determined by the context of the inputs. These subsets can be used to sufficiently derive spatial mapping of point’s correspondences between the source and target set even if points have been missing or modified slightly in the target set. A brief description of this method is followed by a comparative analysis of its performance against two ICP-based methods, followed by some experiments on its subset’s sensitivity and robustness against noise.

Keywords

Iterative Closest Point (ICP) Correspondences Transformation Registration error Subset Expectation Maximization (EM) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Myronenko, A., Song, X.: Point Set Registration: Coherent Point Drift. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(12), 2262–2275 (2010)CrossRefGoogle Scholar
  2. 2.
    Besl, P.J., McKay, N.D.: A Method for Registration of 3-D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar
  3. 3.
    Chen, Y., Medioni, G.: Object Modeling by Registration of Multiple Range Images. Image and Vision Computing 10(3), 145–155 (1992)CrossRefGoogle Scholar
  4. 4.
    Ezra, E., Sharir, M., Efrat, A.: On the performance of the ICP algorithm. Computational Geometry 41, 77–93 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Jost, T., Hügli, H.: A Multi-Resolution ICP with Heuristic Closest Point Search for Fast and Robust 3D Registration of Range Images. In: Proceedings of the 4th International Conference on 3-D Digital Imaging and Modeling, pp. 427–433 (2003)Google Scholar
  6. 6.
    Zinßer, T., Schmidt, J., Niemann, H.: A Refined ICP Algorithm for Robust 3-D Correspondence Estimation. In: Proceedings of the IEEE International Conference on Image Processing, pp. 695–698 (2003)Google Scholar
  7. 7.
    Santamaría, J., Cordón, O., Damas, S.: A comparative study of state-of-the-art evolutionary image registration methods for 3D Modeling. Computer Vision and Image Understanding 115(9), 1340–1354 (2011)CrossRefGoogle Scholar
  8. 8.
    Granger, S., Pennec, X.: Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part IV. LNCS, vol. 2353, pp. 418–432. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Liu, Y.: Automatic registration of overlapping 3D point clouds using closest points. Image and Vision Computing 24(7), 762–781 (2006)CrossRefGoogle Scholar
  10. 10.
    Chen, J., Liao, I.Y., Belaton, B., Zaman, M.: A Neural Network-Based Registration Method for 3D Rigid Face Image. World Wide Web (2013), doi:10.1007/s11280-013-0213-9Google Scholar
  11. 11.
    Du, S., Zheng, N., Ying, S., Liu, J.: Affine iterative closest point algorithm for point set registration. Pattern Recognition Letters 31, 791–799 (2010)CrossRefGoogle Scholar
  12. 12.
    Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP Algorithm. In: Proceedings of the 3th International Conference on 3-D Digital Imaging and Modeling, pp. 1–8 (2001)Google Scholar
  13. 13.
    Rasoulian, A., Rohling, R., Abolmaesumi, P.: Group-Wise Registration of Point Sets for Statistical Shape Models. IEEE Transactions on Medical Imaging 31(11), 2025–2034 (2012)CrossRefGoogle Scholar
  14. 14.
    Gold, S., Rangarajan, A., Lu, C.-P., Mjolsness, E.: New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence. Pattern Recognition 31, 957–964 (1997)Google Scholar
  15. 15.
    Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. Computer Vision and Image Understanding 89, 114–141 (2003)CrossRefzbMATHGoogle Scholar
  16. 16.
    Chen, J., Belaton, B.: An Improved Iterative Closest Point Algorithm for Rigid Point Registration. In: Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), TianJin, pp. 481–485 (2013)Google Scholar
  17. 17.
    Latecki, L.J., Lakämper, R., Eckhardt, U.: Shape descriptors for non rigid shapes with a single closed contour. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 424–429 (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Junfen Chen
    • 1
  • Bahari Belaton
    • 1
  • Zheng Pan
    • 1
  1. 1.School of Computer SciencesUniversiti Sains MalaysiaPenangMalaysia

Personalised recommendations