@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 = {<p style="text-align: justify;">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.</p>},
issn = {2617-8710},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/ijnam/770.html}
}