Nonlinear Analysis of Multi-Dimensional Signals: Local Adaptive Estimation of Complex Motion and Orientation Patterns

  • Christoph S. Garbe
  • Kai Krajsek
  • Pavel Pavlov
  • Björn Andres
  • Matthias Mühlich
  • Ingo Stuke
  • Cicero Mota
  • Martin Böhme
  • Martin Haker
  • Tobias Schuchert
  • Hanno Scharr
  • Til Aach
  • Erhardt Barth
  • Rudolf Mester
  • Bernd Jähne
Part of the Understanding Complex Systems book series (UCS)

Abstract

We consider the general task of accurately detecting and quantifying orientations in n-dimensional signals s. The main emphasis will be placed on the estimation of motion, which can be thought of as orientation in spatiotemporal signals. Associated problems such as the optimization of matched kernels for deriving isotropic and highly accurate gradients from the signals, optimal integration of local models, and local model selection will also be addressed.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Christoph S. Garbe
    • 1
  • Kai Krajsek
    • 2
  • Pavel Pavlov
    • 1
  • Björn Andres
    • 1
  • Matthias Mühlich
    • 2
    • 4
  • Ingo Stuke
    • 6
  • Cicero Mota
    • 2
    • 5
  • Martin Böhme
    • 5
  • Martin Haker
    • 5
  • Tobias Schuchert
    • 3
  • Hanno Scharr
    • 3
  • Til Aach
    • 4
  • Erhardt Barth
    • 5
  • Rudolf Mester
    • 2
  • Bernd Jähne
    • 1
  1. 1.Interdisciplinary Center for Scientific Computing (IWR)University of HeidelbergHeidelbergGermany
  2. 2.Visual Sensorics and Information Processing Lab (VSI)Goethe University FrankfurtFrankfurt/M.Germany
  3. 3.Institute for Chemistry and Dynamics of the Geosphere, Institute 3: PhytosphereResearch Center Jülich GmbHJülichGermany
  4. 4.Institute of Imaging & Computer VisionRWTH Aachen UniversityAachenGermany
  5. 5.Institute for Neuro- and BioinformaticsUniversity of LübeckLübeckGermany
  6. 6.Institute for Signal ProcessingUniversity of LübeckLübeckGermany

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