Automatic Scale Selection of Superimposed Signals

  • Oliver Fleischmann
  • Gerald Sommer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7476)


This work introduces a novel method to estimate the characteristic scale of low-level image structures, which can be modeled as superpositions of intrinsically one-dimensional signals. Rather than being a single scalar quantity, the characteristic scale of the superimposed signal model is an affine equivariant regional feature. The estimation of the characteristic scale is based on an accurate estimation scheme for the orientations of the intrinsically one-dimensional signals. Using the orientation estimations, the characteristic scales of the single intrinsically one-dimensional signals are obtained. The single orientations and scales are combined into a single affine equivariant regional feature describing the characteristic scale of the superimposed signal model. Being based on convolutions with linear shift invariant filters and one-dimensional extremum searches it yields an efficient implementation.


Characteristic Scale Interest Point Orientation Estimation Single Orientation Dimensional Signal 
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.


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  1. 1.
    Aach, T., Mota, C., Stuke, I., Mühlich, M., Barth, E.: Analysis of Superimposed Oriented Patterns. IEEE Transactions on Image Processing 15(12), 3690–3700 (2006)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bracewell, R.: The Fourier Transform and its Applications. McGraw-Hill (1978)Google Scholar
  3. 3.
    Förstner, W., Gülch, E.: A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Centres of Circular Features. In: Proc. Intercommission Conference on Fast Processing of Photogrammetric Data (ISPRS), pp. 281–305 (1987)Google Scholar
  4. 4.
    Freeman, W., Adelson, E.: The Design and Use of Steerable Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(9), 891–906 (1991)CrossRefGoogle Scholar
  5. 5.
    Harris, C., Stephens, M.: A Combined Corner and Edge Detector. In: Alvey Vision Conference, pp. 147–152 (1988)Google Scholar
  6. 6.
    Kanatani, K.: Statistical Optimization for Geometric Computation: Theory and Practice. Elsevier Science Inc., New York (1996)zbMATHGoogle Scholar
  7. 7.
    Lindeberg, T.: Feature Detection with Automatic Scale Selection. International Journal of Computer Vision 30(2), 79–116 (1998)CrossRefGoogle Scholar
  8. 8.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  9. 9.
    Mikolajczyk, K., Schmid, C.: Scale & Affine Invariant Interest Point Detectors. International Journal of Computer Vision 60(1), 63–86 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Oliver Fleischmann
    • 1
  • Gerald Sommer
    • 1
  1. 1.Cognitive Systems Group, Department of Computer ScienceKiel UniversityGermany

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