Point set registration is a fundamental problem in many domains of computer vision. In previous work on the registration, the point sets are often represented using Gaussian mixture models and the registration process is represented as a form of a probabilistic solution. For non-rigid point set registration, however, the asymmetric Gaussian (AG) model can capture spatially asymmetric distributions compared with symmetric Gaussian, and the structural feature of the point sets reserve relatively complete and has important significance in registration. In this work, the authors designed a new shape context (SC) descriptor which combines the local and global structures of the point set. Meanwhile, they proposed a non-rigid point set registration algorithm which formulates a registration process as the mixture probability density estimation of the AG mixture model, and the method introduce the structural feature by the new SC. Extensive experiments show that the proposed algorithm has a clear improvement over the state-of-the-art methods.

References

1. 1)
  - 4. Smeulders, A.W.M., Worring, M., Santini, S., et al: ‘Content-based image retrieval at the end of the early years’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (12), pp. 1349–1380.
2. 2)
  - 12. Zheng, Y., Doermann, D.: ‘Robust point matching for nonrigid shapes by preserving local neighborhood structures’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (4), pp. 643–649.
3. 3)
  - 10. Zhang, P.P., Wang, S.Z., Qiao, Y., et al: ‘Affine SoftAssign with bidirectional distance for point matching’. IEEE Int. Conf. on Image Processing, Melbourne, Australia, 2014, pp. 1267–1271.
4. 4)
  - 21. Mateus, D., Horaud, R., Knossow, D., et al: ‘Articulated shape matching using Laplacian eigenfunctions and unsupervised point registration’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, Anchorage, AK, USA, 2008, pp. 1–8.
5. 5)
  - 3. Zitoĺć, B., Flusser, J.: ‘Image registration methods: a survey’, Image Vis. Comput., 2003, 21, (11), pp. 977–1000.
6. 6)
  - 14. Jian, B., Vemuri, B.C.: ‘Robust point set registration using Gaussian mixture models’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (8), pp. 1633–1645.
7. 7)
  - 25. Zhou, F., Torre, F.D.L.: ‘Deformable graph matching’. IEEE Conf. on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013, pp. 2922–2929.
8. 8)
  - 8. Du, S., Zheng, N., Meng, G., et al: ‘Affine registration of point sets using ICP and ICA’, IEEE Signal Process. Lett., 2008, 15, (2008), pp. 689–692.
9. 9)
  - 11. Chui, H., Rangarajan, A.: ‘A new point matching algorithm for non-rigid registration’, Comput. Vis. Image Underst., 2003, 89, (2–3), pp. 114–141.
10. 10)
  - 15. Myronenko, A., Song, X.: ‘Point set registration: coherent point drift’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, (12), pp. 2262–2275.
11. 11)
  - 2. Belongie, S., Malik, J., Puzicha, J.: ‘Shape matching and object recognition using shape contexts’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 4170, (4), pp. 483–507.
12. 12)
  - 6. Makadia, A., Patterson, A.I., Daniilidis, K.: ‘Fully automatic registration of 3D point clouds’. 2006 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, New York, NY, USA, 2006, pp. 1297–1304.
13. 13)
  - 26. Rusu, R.B., Blodow, N., Beetz, M.: ‘Fast point feature histograms (FPFH) for 3D registration’. IEEE Int. Conf. on Robotics and Automation, Kobe, Japan, 2009, pp. 1848–1853.
14. 14)
  - 22. Leordeanu, M., Hebert, M., Sukthankar, R.: ‘An integer projected fixed point method for graph matching and MAP inference’. Int. Conf. on Neural Information Processing Systems, Vancouver, Canada, 2009, pp. 1114–1122.
15. 15)
  - 5. Fitzgibbon, A.W.: ‘Robust registration of 2D and 3D point sets’, Image Vis. Comput., 2002, 21, (13), pp. 1145–1153.
16. 16)
  - 16. Ma, J., Zhao, J., Yuille, A.L.: ‘Non-rigid point set registration by preserving global and local structures’, IEEE Trans. Image Process., 2015, 25, (1), pp. 53–64.
17. 17)
  - 17. Wang, G., Wang, Z., Chen, Y., et al: ‘A robust non-rigid point set registration method based on asymmetric Gaussian representation’, Comput. Vis. Image Underst., 2015, 141, (C), pp. 67–80.
18. 18)
  - 7. Besl, P.J.: ‘A method for registration of 3-D shapes’, IEEE Trans. Pattern Anal. Mach. Intell., 1992, 14, (3), pp. 239–256.
19. 19)
  - 9. Du, S., Zheng, N., Ying, S., et al: ‘Affine iterative closest point algorithm for point set registration’, Pattern Recognit. Lett., 2010, 31, (9), pp. 791–799.
20. 20)
  - 20. Li, X., Yankeelov, T.E., Dawant, B.M.: ‘Constrained non-rigid registration for whole body image registration: method and validation’. Proc. SPIE, San Diego, CA, USA, 2007, 6512.
21. 21)
  - 23. Cho, M., Lee, J., Lee, K.M.: ‘Reweighted random walks for graph matching’. European Conf. on Computer Vision, Berlin, Germany, 2010, pp. 492–505.
22. 22)
  - 18. Kato, T., Omachi, S., Aso, H.: ‘Asymmetric Gaussian and its application to pattern recognition’. Joint IAPR Int. Workshop on Structural, Syntactic, and Statistical Pattern Recognition, London, 2002, pp. 405–413.
23. 23)
  - 19. Belongie, S., Malik, J., Puzicha, J.: ‘Shape context: a new descriptor for shape matching and object recognition’, 2000, pp. 831–837.
24. 24)
  - 13. Ma, J., Zhao, J., Tian, J., et al: ‘Robust estimation of nonrigid transformation for point set registration’. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013, pp. 2147–2154.
25. 25)
  - 24. Torre, F.D.L., Feng, Z.: ‘Factorized graph matching’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 38, (9), pp. 1774–1789.
26. 26)
  - 1. Sun, J., Zheng, N.N., Shum, H.Y.: ‘Stereo matching using belief propagation’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 25, (7), pp. 787–800.

Robust non-rigid point set registration method based on asymmetric Gaussian and structural feature

References

Related content