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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Myronenko, A., Song, X.: Point Set Registration: Coherent Point Drift. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(12), 2262–2275 (2010)
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)
Chen, Y., Medioni, G.: Object Modeling by Registration of Multiple Range Images. Image and Vision Computing 10(3), 145–155 (1992)
Ezra, E., Sharir, M., Efrat, A.: On the performance of the ICP algorithm. Computational Geometry 41, 77–93 (2008)
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)
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)
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)
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)
Liu, Y.: Automatic registration of overlapping 3D point clouds using closest points. Image and Vision Computing 24(7), 762–781 (2006)
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/s11280-013-0213-9
Du, S., Zheng, N., Ying, S., Liu, J.: Affine iterative closest point algorithm for point set registration. Pattern Recognition Letters 31, 791–799 (2010)
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)
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)
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)
Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. Computer Vision and Image Understanding 89, 114–141 (2003)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Chen, J., Belaton, B., Pan, Z. (2013). A Robust Subset-ICP Method for Point Set Registration. In: Zaman, H.B., Robinson, P., Olivier, P., Shih, T.K., Velastin, S. (eds) Advances in Visual Informatics. IVIC 2013. Lecture Notes in Computer Science, vol 8237. Springer, Cham. https://doi.org/10.1007/978-3-319-02958-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-02958-0_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02957-3
Online ISBN: 978-3-319-02958-0
eBook Packages: Computer ScienceComputer Science (R0)