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.
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Mégret, R., Authesserre, JB., Berthoumieu, Y. (2008). The Bi-directional Framework for Unifying Parametric Image Alignment Approaches. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88690-7_30
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DOI: https://doi.org/10.1007/978-3-540-88690-7_30
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