Phase-Based Local Features

  • Gustavo Carneiro
  • Allan D. Jepson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2350)


We introduce a new type of local feature based on the phase and amplitude responses of complex-valued steerable filters. The design of this local feature is motivated by a desire to obtain feature vectors which are semi-invariant under common image deformations, yet distinctive enough to provide useful identity information. A recent proposal for such local features involves combining differential invariants to particular image deformations, such as rotation. Our approach differs in that we consider a wider class of image deformations, including the addition of noise, along with both global and local brightness variations. We use steerable filters to make the feature robust to rotation. And we exploit the fact that phase data is often locally stable with respect to scale changes, noise, and common brightness changes. We provide empirical results comparing our local feature with one based on differential invariants. The results show that our phase-based local feature leads to better performance when dealing with common illumination changes and 2-D rotation, while giving comparable effects in terms of scale changes.


Image features Object recognition Vision systems engineering and evaluation Invariant local features Local phase information 


  1. 1.
    M. J. Black and A. D. Jepson. Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. In 4th European Conf. on Computer Vision, pages 329–342, Cambridge, April 1996.Google Scholar
  2. 2.
    R. Brooks. Model-based 3-d interpretations of 2-d images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(2):140–150, 1983.Google Scholar
  3. 3.
    D. Fleet. Measurement of Image Velocity. Kluwer Academic Publishers, 1992.Google Scholar
  4. 4.
    D. Fleet, A. D. Jepson, and M. Jenkin. Phase-based disparity measure. In CVGIP: Image Understanding, pages 198–210, 1991.Google Scholar
  5. 5.
    W. T. Freeman and E. H. Adelson. The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(9):891–906, 1991.CrossRefGoogle Scholar
  6. 6.
    W. E. L. Grimson and T. Lozano-Pérez. Localizing overlapping parts by searching the interpretation tree. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(4):469–482, 1987.CrossRefGoogle Scholar
  7. 7.
    C. Harris and M. Stephens. A combined corner and edge detector. In Alvey Vision Conference, 1988.Google Scholar
  8. 8.
    D. J. Heeger. Computational model of cat striate physiology. Technical report, Massachusetts Institute of Technology, October 1989.Google Scholar
  9. 9.
    D. Huttenlocher and S. Ullman. Object recognition using alignment. In International Conference on Computer Vision, pages 102–111, London, UK, 1987.Google Scholar
  10. 10.
    D. Jugessur and G. Dudek. Local appearance for robust object recognition. In IEEE Computer Vision and Pattern Recognition, pages 834–839, Hilton Head, USA, June 2000.Google Scholar
  11. 11.
    A. Leonardis and H. Bischoff. Dealing with occlusions in the eigenspace approach. In IEEE Conference on Computer Vision and Pattern Recognition, pages 453–458, San Francisco, USA, June 1996.Google Scholar
  12. 12.
    D. G. Lowe. Three-dimensional object recognition from single two-dimensional images. Artificial Intelligence, 31(3):355–395, 1987.CrossRefGoogle Scholar
  13. 13.
    D. G. Lowe. Object recognition from local scale-invariant features. In International Conference on Computer Vision, pages 1150–1157, Corfu, Greece, September 1999.Google Scholar
  14. 14.
    D. G. Lowe. Towards a computational model for object recognition in it cortex. In First IEEE International Workshop on Biologically Motivated Computer Vision, pages 20–31, Seoul, Korea, May 2000.Google Scholar
  15. 15.
    H. Murase and S. Nayar. Visual learning and recognition of 3-d objects from appearance. International Journal of Computer Vision, 14(1):5–24, 1995.CrossRefGoogle Scholar
  16. 16.
    R. C. Nelson. Memory-based recognition for 3-d objects. In ARPA Image Understanding Workshop, pages 1305–1310, Palm Springs, USA, February 1996.Google Scholar
  17. 17.
    R.P.N. Rao and D.H. Ballard. Natural basis functions and topographic memory for face recognition. In International Joint Conference on Artificial Intelligence, pages 10–17, 1995.Google Scholar
  18. 18.
    S. K. Nayar S. A. Nene and H. Murase. Columbia object image library (coil-20). Technical report, Department of Computer Science, Columbia University, February 1996.Google Scholar
  19. 19.
    B. Schiele and J.L. Crowley. Object recognition using multidimensional receptive field histograms. In 4th European Conference on Computer Vision, volume 1, pages 610–619, April 1996.Google Scholar
  20. 20.
    C. Schmid and R. Mohr. Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(5):530–535, 1997.CrossRefGoogle Scholar
  21. 21.
    M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1), 1991.Google Scholar
  22. 22.
    M. Turk and A. P. Pentland. Face recognition using eigenfaces. In IEEE Computer Vision and Pattern Recognition, pages 586–591, 1991.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Gustavo Carneiro
    • 1
  • Allan D. Jepson
    • 1
  1. 1.Department of Computer ScienceUniversity of TorontoCanada

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