Improving Phase-Congruency Based Feature Detection through Automatic Scale-Selection

  • Veit U. B. Schenk
  • Michael Brady
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)


In this paper we present a novel method for computing phase-congruency by automatically selecting the range of scales over which a locally one-dimensional feature exists. Our method is based on the use of local energy computed in a multi-resolution steerable filter framework. We observe the behaviour of phase over scale to determine both the type of the underlying features and the optimal range of scales over which they exist. This additional information can be used to provide a more complete description of image-features which can be utilized in a variety of applications that require high-quality low-level descriptors. We apply our algorithm to both synthetic and real images.


Phase-congruency local energy feature-detection scale-detection steerable filters 


  1. 1.
    Fleet, D.J., Jepson, A.D.: Stability of phase information. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(12), 1253–1268 (1993)CrossRefGoogle Scholar
  2. 2.
    Freeman, W.T., Adelson, E.H.: The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(9), 891–906 (1991)CrossRefGoogle Scholar
  3. 3.
    Jepson, A.D., Fleet, D.J.: Phase singularities in scale-space. Image and Vision Computing 9(5), 338–343 (1991)CrossRefGoogle Scholar
  4. 4.
    Kovesi, P.: Image features from phase congruency. Videre: A Journal of Computer Vision Research 1(3) (1999)Google Scholar
  5. 5.
    Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. International Journal of Computer Vision 30(2), 117–154 (1998)CrossRefGoogle Scholar
  6. 6.
    Morrone, M., Burr, D.: Feature detection in human vision: a phase dependent energy model. In: Proc. Royal Soc. London Bulletin, pp. 221–245 (1988)Google Scholar
  7. 7.
    Morrone, M.C., Owens, R.A.: Feature detection from local energy. Pattern Recognition Letters 6, 303–313 (1987)CrossRefGoogle Scholar
  8. 8.
    Oppenheim, A.V., Lim, J.S.: The importance of phase in signals. In: Proc. of IEEE 69, pp. 529–541 (1981)Google Scholar
  9. 9.
    Schenk, V.U.B.: Visual Identification of Fine Surface Incisions. PhD thesis, University of Oxford, Dept. of Engineering Science (March 2001)Google Scholar
  10. 10.
    Venkatesh, S., Owens, R.A.: On the classification of image features. Pattern Recognition Letters 11, 339–349 (1990)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Veit U. B. Schenk
    • 1
    • 2
  • Michael Brady
    • 2
  1. 1.Laboratory of Computational EngineeringHelsinki University of TechnologyFinland
  2. 2.Dept. of Engineering ScienceRobotics Research GroupOxfordUK

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