Skip to main content
SpringerLink
Log in

Robust Non-rigid Registration Based on Affine ICP Algorithm and Part-Based Method

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

The classical affine iterative closest point (ICP) algorithm is fast and accurate for affine registration between two point sets, but it is easy to fall into a local minimum. As an extension of the classical affine registration algorithm, this paper first proposes an affine ICP algorithm based on control point guided, and then applies this new method to establish a robust non-rigid registration algorithm based on local affine registration. The algorithm uses a hierarchical iterative method to complete the point set non-rigid registration from coarse to fine. In each iteration, the sub data point sets and sub model point sets are divided, meanwhile, the shape control points of each sub point set are updated. Then we use the control point guided affine ICP algorithm to solve the local affine transformation between the corresponding sub point sets. Next, the local affine transformation obtained by the previous step is used to update the sub data point sets and their shape control point sets. Experimental results demonstrate that the accuracy and convergence of our algorithm are greatly improved compared with the traditional point set non-rigid registration algorithms.

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
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Maiseli B, Gu Y, Gao H (2017) Recent developments and trends in point set registration methods. J Vis Commun Image Represent 46:95–106

    Article  Google Scholar 

  2. Koch R, Pollefeys M, Gool LV (2015) Realistic surface reconstruction of 3D scenes from uncalibrated image sequences. Comput Animat Virtual Worlds 11(3):115–127

    Google Scholar 

  3. Mentges G, Grigat R (2016) Surface reconstruction from image space adjacency of lines using breadth-first plane search. In: IEEE international conference on robotics and automation, pp 995–1002

  4. Powell S, Magnotta VA, Johnson H, Jammalamadaka VK, Andreasen NC, Pierson Ronald (2008) Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures. Neuroimage 39(1):238–247

    Article  Google Scholar 

  5. Chou CR, Frederick B, Mageras G, Chang S, Pizer S (2013) 2D/3D image registration using regression learning. Comput Vis Image Underst 117(9):1095–1106

    Article  Google Scholar 

  6. Du S, Liu J, Liu Y, Zhang X, Xue J (2017) Precise glasses detection algorithm for face with in-plane rotation. Multimedia Syst 23(3):293–302

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Ye M, Wang X, Yang R, Ren L, Pollefeys M (2011) Accurate 3d pose estimation from a single depth image. In: IEEE international conference on computer vision, pp 731–738

  9. Rasoulian A, Rohling R, Abolmaesumi P (2012) Group-wise registration of point sets for statistical shape models. IEEE Trans Med Imaging 31(11):2025–2034

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Wu G, Kim M, Wang Q, Shen D (2015) S-hammer: hierarchical attribute-guided, symmetric diffeomorphic registration for mr brain images. Hum Brain Mapp 35(3):256–267

    Google Scholar 

  12. Weiss A, Hirshberg D, Black MJ (2011) Home 3d body scans from noisy image and range data. In: International conference on computer vision, pp 1951–1958

  13. Peng Y, Lin W, Ying S, Peng J (2013) Soft shape registration under Lie group frame. IET Comput Vis 7(6):437–447

    Article  Google Scholar 

  14. Besl PJ, McKay ND (1993) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256

    Article  Google Scholar 

  15. Zhang Z (1994) Iterative point matching for registration of freeform curves and surfaces. Int J Comput Vis 13(2):119–152

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Gressin A, Mallet C, Demantke J, David N (2013) Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge. ISPRS J Photogram Remote Sens 79(1–3):240–251

    Article  Google Scholar 

  18. Lalonde JF, Vandapel N, Huber DF, Hebert M (2006) Natural terrain classification using three-dimensional ladar data for ground robot mobility. J Field Robot 23(10):839–861

    Article  Google Scholar 

  19. Schall O, Belyaev A, Seidel HP (2005) Robust filtering of noisy scattered point data. In: Proceedings eurographics/IEEE VGTC symposium point-based graphics, pp 71–77

  20. Sanroma G, Alquezar R, Serratosa F, Herrera B (2012) Smooth point-set registration using neighboring constraints. Pattern Recogn Lett 33(15):2029–2037

    Article  Google Scholar 

  21. Torsello A, Rodola E, Albarelli A (2011) Sampling relevant points for surface registration. In: International conference on 3D imaging, modeling, processing, visualization and transmission, vol 7(1), pp 290–295

  22. Ying S, Wang Y, Wen Z, Lin Y (2016) Nonlinear 2D shape registration via thin-plate spline and Lie group representation. Neurocomputing 195:129–136

    Article  Google Scholar 

  23. Wiemann T, Nüchter A, Lingemann K, Stiene S, Hertzberg J (2010) Automatic construction of polygonal maps from point cloud data. In: IEEE international workshop on safety security and rescue robotics, pp 1–6

  24. Jiang J, Shi X (2016) A robust point-matching algorithm based on integrated spatial structure constraint for remote sensing image registration. IEEE Geosci Remote Sens Lett 13(11):1716–1720

    Article  Google Scholar 

  25. Ying S, Wu G, Wang Q, Shen D (2014) Hierarchical unbiased graph shrinkage (HUGS): a novel groupwise registration for large data set. NeuroImage 84(1):626–638

    Article  Google Scholar 

  26. Zhang K, Li X, Zhang J (2014) A robust point-matching algorithm for remote sensing image registration. IEEE Geosci Remote Sens Lett 11(2):469–473

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  29. Sharp GC, Lee SW, Wehe DK (2002) ICP registration using invariant features. IEEE Trans Pattern Anal Mach Intell 24(1):90–102

    Article  Google Scholar 

  30. Silva L, Bellon ORP, Boyer KL (2005) Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms. IEEE Trans Pattern Anal Mach Intell 27(5):762–776

    Article  Google Scholar 

  31. Dong J, Peng Y, Ying S, Hu Z (2014) Lietricp: an improvement of trimmed iterative closest point algorithm. Neurocomputing 140:67–76

    Article  Google Scholar 

  32. Bergström P, Edlund O (2017) Robust registration of surfaces using a refined iterative closest point algorithm with a trust region approach. Numer Algorithms 74(3):755–779

    Article  MathSciNet  Google Scholar 

  33. Ho J, Yang M, Rangarajan A, Vemuri B (2007) A new affine registration algorithm for matching 2D point sets. In: Applications of computer vision, pp 25

  34. Amberg B, Romdhani S, Vetter T (2007) Optimal step non-rigid ICP algorithms for surface registration. In: IEEE conference on computer vision and pattern recognition, pp 1–8

  35. Kou Q, Yang Y, Du S, Luo S, Cai D (2016) A modified non-rigid ICP algorithm for registration of chromosome images. In: International conference on intelligent computing, pp 503–513

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

    Article  Google Scholar 

  37. Hasanbelliu E, Giraldo LS, Principe JC (2011) A robust point matching algorithm for non-rigid registration using the Cauchy–Schwarz divergence. In: IEEE international workshop on machine learning for signal processing, pp 1–6

  38. Chen J, Ma J, Yang C, Ma L, Zheng S (2015) Non-rigid point set registration via coherent spatial mapping. Sig Process 106(C):62–72

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  40. Harris C (1988) A combined corner and edge detector. In: Proceedings of the Alvey vision conference, pp 147–151

  41. Nchter A, Lingemann K, Hertzberg J (2007) Cached k-d tree search for ICP algorithms. In: International conference on 3-d digital imaging and modeling, pp 419–426

  42. Dasgupta S, Sinha K (2015) Randomized partition trees for nearest neighbor search. Algorithmica 72(1):237–263

    Article  MathSciNet  Google Scholar 

  43. Chen H, Lin T (2006) An algorithm to build convex hulls for 3-D objects. J Chin Inst Eng 29(6):945–952

    Article  MathSciNet  Google Scholar 

  44. Alexander C (2009) Delineating tree crowns from airborne laser scanning point cloud data using Delaunay triangulation. Int J Remote Sens 30(14):3843–3848

    Article  Google Scholar 

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

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61573274, 61379104 and 61627811.

Author information

Authors and Affiliations

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

    Lei Xiong & Liyang Wu

  2. Xi’an Jiaotong University, Xi’an, 710049, Shaanxi Province, People’s Republic of China

    Wenting Cui, Sirui Zhang, Guanglin Xu & Huaizhong Hu

Authors
  1. Lei Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liyang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenting Cui
    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. Guanglin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Huaizhong Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huaizhong Hu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiong, L., Wu, L., Cui, W. et al. Robust Non-rigid Registration Based on Affine ICP Algorithm and Part-Based Method. Neural Process Lett 48, 1305–1321 (2018). https://doi.org/10.1007/s11063-017-9760-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-017-9760-x

Keywords

Search

Navigation