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Automatic Scale Selection of Superimposed Signals

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

Abstract

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.

Keywords

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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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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