Advertisement

Surface Signature-Based Method for Modeling and Recognizing Free-Form Objects

  • H. B. Darbandi
  • M. R. Ito
  • J. Little
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4842)

Abstract

In this paper we propose a new technique for modeling three-dimensional rigid objects by encoding the fluctuation of the surface and the variation of its normal around an oriented surface point, as the surface expands. The surface of the object is encoded into three vectors as the surface signature on each point, and then the collection of signatures is used to model and match the object. The signatures encode the curvature, symmetry, and convexity of the surface around an oriented point. This modeling technique is robust to scale, orientation, sampling resolution, noise, occlusion, and cluttering.

Keywords

Object Recognition Test Object Machine Intelligence Orientation Signature Alignment Error 
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.
    Bennamoun, M., Mamic, G.J.: Object Recognition: Fundamentals and Case Studies. Springer, Heidelberg (2000)Google Scholar
  2. 2.
    Besl, P.J., Jain, R.: Range Image Understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 430–449 (1985)Google Scholar
  3. 3.
    Faugeras, O.D., Herbert, M.: The Representation, Recognition, and Location of 3-D Objects. The International Journal of Robotics Research 5(3), 27–52 (1986)CrossRefGoogle Scholar
  4. 4.
    Wand, J., Cohen, F.S.: Part II: 3-D Object Recognition and Shape Estimation from Image Contours Using B-Splines, Shape Invariant Matching, and Neural Network. IEEE Trans. Pattern Analysis and Machine Intelligence 16(1), 13–23 (1994)CrossRefGoogle Scholar
  5. 5.
    Horn, B.: Extended Gaussian Image. Proceedings of the IEEE 72, 1671–1686 (1984)Google Scholar
  6. 6.
    Kang, S.B., Ikeuchi, K.: The complex EGI: A new representation for 3D pose determination. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(7), 707–721 (1993)CrossRefGoogle Scholar
  7. 7.
    Chua, C.S., Jarvis, R.: Point Signatures: A New Representation for 3D Object Recognition. Int’l J. Computer Vision 25(1), 63–85 (1997)CrossRefGoogle Scholar
  8. 8.
    Mian, A.S., Bennamoun, M., Owens, R.A.: Automatic Correspondence for 3D Modeling: An Extensive Review. Int’l J. Shape Modeling (2005)Google Scholar
  9. 9.
    Stein, F., Medioni, G.: Structural Indexing: Efficient 3D Object Recognition of a Set of Range Views. IEEE transactions on pattern analysis and Machine Intelligence 17(4), 344–359 (1995)CrossRefGoogle Scholar
  10. 10.
    Katsoulas, D., Kosmopoulos, D.I.: Box-like Superquadric Recovery in Range Images by Fusing Region and Boundary Information. Pattern Recognition, 2006. In: ICPR 2006. 18th International Conference on, August 20-24, vol. 1, pp. 719–722 (2006)Google Scholar
  11. 11.
    Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision. Addison-Wesley, Reading (1993)Google Scholar
  12. 12.
    Johnson, A.E.: Spin Image: A Representation for 3D Surface Matching. PhD Thesis, Carnegie Mellon University (1997)Google Scholar
  13. 13.
    Correa, S., Shapiro, L.: A New Signature-Based Method for Efficient 3D Object Recognition. Proc. IEEE Conf. Computer Vision and Pattern Recognition 1, 769–776 (2001)Google Scholar
  14. 14.
    Yamany, S.M., Farag, A.: Freeform Surface Registration Using Surface Signatures. Proc. Int. Conf. on Computer Vision 2, 1098–1104 (1999)CrossRefGoogle Scholar
  15. 15.
    Bennamoun, A.S., Owens, M.: Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes Mian. Pattern Analysis and Machine Intelligence, IEEE Transactions on 28(10), 1584–1601 (2006)CrossRefGoogle Scholar
  16. 16.
    Campbell, R.J., Flynn, P.J.: A Survey of Free-Form Object Representation and Recognition Techniques. Computer Vision and Understanding 81, 166–210 (2001)zbMATHCrossRefGoogle Scholar
  17. 17.
    Carmichael, O., Huber, D., Hebert, M.: Large Data Sets and Confusing Scenes in 3-D Surface Matching and Recognition. In: Proc. Int’l Conf. 3-D Digital Imaging and Modeling, pp. 358–367 (1999)Google Scholar
  18. 18.
    Johnson, A., Herbert, M.: Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes. IEEE Tr. on Pattern Analysis and Machine Intelligence 21(5) (1999)Google Scholar
  19. 19.
  20. 20.
    Nene, S.A., Nayar, S.K.: A simple Algorithm for Nearest Neighbor Search in High Dimensions. IEEE Trans. Pattern Analysis and Machine Intelligence, 999–1003 (1997)Google Scholar
  21. 21.
    Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Prentice-Hall, Englewood Cliffs (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • H. B. Darbandi
    • 1
  • M. R. Ito
    • 1
  • J. Little
    • 2
  1. 1.Electrical and Computer Engineering, University of British Columbia 
  2. 2.Computer Science, University of British Columbia 

Personalised recommendations