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Volume 6, Issue 2
A Robust Affine Image Registration Method

N. Chumchob & K. Chen

Int. J. Numer. Anal. Mod., 6 (2009), pp. 311-334.

Published online: 2009-06

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  • 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.

  • AMS Subject Headings

62B5, 94A08, 47A52, 90C53

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{IJNAM-6-311, author = {N. Chumchob and K. Chen}, title = {A Robust Affine Image Registration Method}, journal = {International Journal of Numerical Analysis and Modeling}, year = {2009}, volume = {6}, number = {2}, pages = {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.

}, issn = {2617-8710}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/ijnam/770.html} }
TY - JOUR T1 - A Robust Affine Image Registration Method AU - N. Chumchob & K. Chen JO - International Journal of Numerical Analysis and Modeling VL - 2 SP - 311 EP - 334 PY - 2009 DA - 2009/06 SN - 6 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/ijnam/770.html KW - Image registration, affine transformation, regularization, Newton method. AB -

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.

N. Chumchob and K. Chen. (2009). A Robust Affine Image Registration Method. International Journal of Numerical Analysis and Modeling. 6 (2). 311-334. doi:
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