Advertisement

Rotation Invariant Non-rigid Shape Matching in Cluttered Scenes

  • Wei Lian
  • Lei Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)

Abstract

This paper presents a novel and efficient method for locating deformable shapes in cluttered scenes. The shapes to be detected may undergo arbitrary translational and rotational changes, and they can be non-rigidly deformed, occluded and corrupted by clutters. All these problems make the accurate and robust shape matching very difficult. By using a new shape representation, which involves a powerful feature descriptor, the proposed method can overcome the above difficulties successfully, and it possesses the property of global optimality. The experiments on both synthetic and real data validated that the proposed algorithm is robust to various types of disturbances. It can robustly detect the desired shapes in complex and highly cluttered scenes.

Keywords

Rotation Invariant Point Match Viterbi Algorithm Point Correspondence Rest Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. Computer Vision and Image Understanding 89, 114–141 (2003)zbMATHCrossRefGoogle Scholar
  2. 2.
    Veltkamp, R.C., Hagedoorn, M.: State of the art in shape matching, pp. 87–119 (2001)Google Scholar
  3. 3.
    Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE Trans. Pattern Analysis and Machine Intelligence 14, 239–256 (1992)CrossRefGoogle Scholar
  4. 4.
    Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. International Journal of Computer Vision 13, 119–152 (1994)CrossRefGoogle Scholar
  5. 5.
    Stewart, C.V., Tsai, C.L., Roysam, B.: The dual-bootstrap iterative closest point algorithm with application to retinal image registration. IEEE Trans. Medical Imaging 22, 1379–1394 (2003)CrossRefGoogle Scholar
  6. 6.
    Fitzgibbon, A.W.: Robust registration of 2d and 3d point sets. Image and Vision Computing 21, 1145–1153 (2003); British Machine Vision Computing 2001 (2001) CrossRefGoogle Scholar
  7. 7.
    Yuille, A.L., Kosowsky, J.J.: Statistical physics algorithms that converge. Neural Comput. 6, 341–356 (1994)CrossRefGoogle Scholar
  8. 8.
    Lian, W., Zhang, L., Liang, Y., Pan, Q.: A quadratic programming based cluster correspondence projection algorithm for fast point matching. Computer Vision and Image Understanding 114, 322–333 (2010)CrossRefGoogle Scholar
  9. 9.
    Sofka, M., Yang, G., Stewart, C.V.: Simultaneous covariance driven correspondence (cdc) and transformation estimation in the expectation maximization framework. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  10. 10.
    Tsin, Y., Kanade, T.: A correlation-based approach to robust point set registration. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3023, pp. 558–569. Springer, Heidelberg (2004)Google Scholar
  11. 11.
    Jian, B., Vemuri, B.C.: A robust algorithm for point set registration using mixture of gaussians. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1246–1251 (2005)Google Scholar
  12. 12.
    Silva, L., Bellon, O.R., Boyer, K.L.: Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 762–776 (2005)CrossRefGoogle Scholar
  13. 13.
    Sandhu, R., Dambreville, S., Tannenbaum, A.: Point set registration via particle and stochastic dynamics. IEEE Trans. Pattern Analysis and Machine Intelligence 32, 1459–1473 (2010)CrossRefGoogle Scholar
  14. 14.
    Li, H., Shen, T., Huang, X.: Global optimization for alignment of generalized shapes. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 856–863 (2009)Google Scholar
  15. 15.
    Taylor, C.J., Bhusnurmath, A.: Solving image registration problems using interior point methods. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 638–651. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Jiang, H., Drew, M.S., Li, Z.N.: Matching by linear programming and successive convexification. IEEE Trans. Pattern Analysis and Machine Intelligence 29, 959–975 (2007)CrossRefGoogle Scholar
  17. 17.
    Jiang, H., Yu, S.X.: Linear solution to scale and rotation invariant object matching. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 2474–2481 (2009)Google Scholar
  18. 18.
    Kaick, O.v., Hamarneh, G., Zhang, H., Wighton, P.: Contour correspondence via ant colony optimization. In: PG 2007: Proceedings of the 15th Pacific Conference on Computer Graphics and Applications, pp. 271–280 (2007)Google Scholar
  19. 19.
    Scott, C., Nowak, R.D.: Robust contour matching via the order-preserving assignment problem. IEEE Trans. Image Processing 15, 1831–1838 (2006)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Wang, J., Athitsos, V., Sclaroff, S., Betke, M.: Detecting objects of variable shape structure with hidden state shape models. IEEE Trans. Pattern Analysis and Machine Intelligence 30, 477–492 (2008)CrossRefGoogle Scholar
  21. 21.
    Felzenszwalb, P.F.: Representation and detection of deformable shapes. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 208–220 (2005)CrossRefGoogle Scholar
  22. 22.
    Coughlan, J.M., Ferreira, S.J.: Finding deformable shapes using loopy belief propagation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 453–468. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  23. 23.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Analysis and Machine Intelligence 24, 509–522 (2002)CrossRefGoogle Scholar
  24. 24.
    Zheng, Y., Doermann, D.: Robust point matching for nonrigid shapes by preserving local neighborhood structures. IEEE Trans. Pattern Analysis and Machine Intelligence 28, 643–649 (2006)CrossRefGoogle Scholar
  25. 25.
  26. 26.
    Thayananthan, A., Stenger, B., Torr, P.H.S., Cipolla, R.: Shape context and chamfer matching in cluttered scenes. In: IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 127–133 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Wei Lian
    • 1
  • Lei Zhang
    • 2
  1. 1.Dept. of Computer ScienceChangzhi UniversityChangzhiChina
  2. 2.Biometric Research Center, Dept. of ComputingThe Hong Kong Polytechnic UniversityHong Kong

Personalised recommendations