GET: The Connection Between Monogenic Scale-Space and Gaussian Derivatives

  • Michael Felsberg
  • Ullrich Köthe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3459)

Abstract

In this paper we propose a new operator which combines advantages of monogenic scale-space and Gaussian scale-space, of the monogenic signal and the structure tensor. The gradient energy tensor (GET) defined in this paper is based on Gaussian derivatives up to third order using different scales. These filters are commonly available, separable, and have an optimal uncertainty. The response of this new operator can be used like the monogenic signal to estimate the local amplitude, the local phase, and the local orientation of an image, but it also allows to measure the coherence of image regions as in the case of the structure tensor. Both theoretically and in experiments the new approach compares favourably with existing methods.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Michael Felsberg
    • 1
  • Ullrich Köthe
    • 2
  1. 1.Computer Vision LaboratoryLinköping UniversityLinköpingSweden
  2. 2.Cognitive Systems GroupUniversity of HamburgHamburgGermany

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