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The Bi-directional Framework for Unifying Parametric Image Alignment Approaches

  • Rémi Mégret
  • Jean-Baptiste Authesserre
  • Yannick Berthoumieu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5304)

Abstract

In this paper, a generic bi-directional framework is proposed for parametric image alignment, that extends the classification of [1]. Four main categories (Forward, Inverse, Dependent and Bi-directional) form the basis of a consistent set of subclasses, onto which state-of-the-art methods have been mapped. New formulations for the ESM [2] and the Inverse Additive [3] algorithms are proposed, that show the ability of this framework to unify existing approaches. New explicit equivalence relationships are given for the case of first-order optimization that provide some insights into the choice of an update rule in iterative algorithms.

Keywords

Image Alignment Inverse Additive Reference Coordinate Frame Inverse Compositional Forward Additive 
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 2008

Authors and Affiliations

  • Rémi Mégret
    • 1
  • Jean-Baptiste Authesserre
    • 1
  • Yannick Berthoumieu
    • 1
  1. 1.IMS LaboratoryUniversity of BordeauxFrance

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