The Bi-directional Framework for Unifying Parametric Image Alignment Approaches

Authors:
Rémi Mégret;Jean-Baptiste Authesserre;Yannick Berthoumieu
Affiliations:
IMS Laboratory, University of Bordeaux, France;IMS Laboratory, University of Bordeaux, France;IMS Laboratory, University of Bordeaux, France
Venue:
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Year:
2008

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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.