In this paper, we present a new 3D object normalization technique based on Independent Component Analysis (ICA). Translation and scale are eliminated by first using standard PCA whitening. ICA and the third order moments are then employed for rotation and reflection normalization. The performance of the proposed approach has been tested with range data subjected to noise and other uncertainties. Our method can be used either as a preprocessing for object modelling, or it can directly be used for 3D recognition.


Independent Component Analysis Principle Component Analysis Independent Component Analysis Iterative Close Point Rigid Transformation 
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  1. 1.
    Faugeras, O.R., Hebert, M.: The Representation, Recognition, and Locating of 3-D Objects. The International Journal of Robotics Research 5(3) (1986)Google Scholar
  2. 2.
    Faber, T., Stokely, E.: Orientation of 3-D structures in medical images. IEEE Trans. on Pattern Analysis and Machine Intelligence 10(5), 626–634 (1988)CrossRefGoogle Scholar
  3. 3.
    Cyganski, D., Orr, J.: Applications of tensor theory to object recognition and orientation determination. IEEE Trans. on Pattern Analysis and Machine Intelligence 7(6), 662–674 (1985)CrossRefGoogle Scholar
  4. 4.
    Haralick, R., Joo, H., Lee, C., Zhuang, X., Vaidya, V., Kim, M.: Pose estimation from corresponding point data. IEEE Trans. on Pattern Analysis and Machine Intelligence 19, 1426–1446 (1989)Google Scholar
  5. 5.
    Besl, P., McKay, N.: A method for registration of 3D shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar
  6. 6.
    Zhang, Z.: Iterative Point Matching for Registration of Free-Form Curves and Surfaces. International J. Computer Vision 13(2), 119–152 (1994)CrossRefGoogle Scholar
  7. 7.
    Ferrie, F.P., Levine, M.D.: Integrating Information from Multiple Views. In: Proc. IEEE Workshop on Computer Vision, Miami Beach, Fla, pp. 117–122 (1987)Google Scholar
  8. 8.
    Blais, G., Levine, M.D.: Registering Multiview Range Data to Create 3D Computer Objects. IEEE Trans. on Pattern Analysis and Machine Intelligence 17(8), 820–824 (1995)CrossRefGoogle Scholar
  9. 9.
    Lee, T.W.: Independent Component Analysis- Theory and Applications. Kluwer Academic Publishers, Dordrecht (1998)MATHGoogle Scholar
  10. 10.
    Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. On Neural Networks 10(3), 626–634 (1999)CrossRefGoogle Scholar
  11. 11.
    Yi, X., Camps, O.C.: Robust occluding contour detection using the Hausdorff distance. In: Proc. of IEEE Conf. on Vision and Pattern Recognition (CVPR 1997), San Juan, PR, pp. 962–967 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sait Sener
    • 1
  • Mustafa Unel
    • 2
  1. 1.Department of Computer ScienceIstanbul Technical UniversityIstanbulTurkey
  2. 2.Faculty of Engineering and Natural SciencesSabanci UniversityIstanbulTurkey

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