Year: 2009
International Journal of Numerical Analysis and Modeling, Vol. 6 (2009), Iss. 2 : pp. 311–334
Abstract
Image registration has many real life applications. Affine image registration is one of the commonly-used parametric models. Iterative solution methods for the underlying least squares problem suffer from convergence problems whenever good initial guesses are not available. Variational models are non-parametric deformable models that have been proposed based on least squares fitting and regularization. The fast iterative solution methods often require a reliable parametric (affine) method in a pre-registration step. In this paper we first survey and study a class of methods suitable for providing the good initial guesses for the affine model and a diffusion based variational model. It appears that these initialization methods, while useful for many cases, are not always reliable. Then we propose a regularized affine least squares approach that can overcome the convergence problems associated with existing methods. Combined with a cooling idea in a multiresolution setting, it can ensure robustness and selection of the optimal coupling parameter efficiently. Numerical examples are given to demonstrate the effectiveness of our proposed approach.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2009-IJNAM-770
International Journal of Numerical Analysis and Modeling, Vol. 6 (2009), Iss. 2 : pp. 311–334
Published online: 2009-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 24
Keywords: Image registration affine transformation regularization Newton method.