An extended method for robust image registration

Show simple item record Spanakis, Constantinus Marias, K. Mathioudakis, Emmanuel N. Kampanis, Nikolaos A. 2014-10-14T15:14:13Z 2014-10-14T15:14:13Z 2014-09-02
dc.identifier.isbn 978-960-8475-22-9
dc.description.abstract Image Registration is the process of transforming sets of data acquired at different time-points, sensors and viewpoints into a single coordinate system. It is widely used in computer vision, medical imaging and satellite image analysis. Although it has been a central research topic in computer vision and medical image analysis for a long time, there are still unresolved issues and success rates seem to be data-dependent. There are many categories of methods that that are able to align images, but usually they are either specialized and accurate for specific types of data or more generic and error-prone, frequently stumbling upon pitfalls. In this work, we present our implementation and results on Maes’ method[1]. By using three different variants of mutual information (used as the similarity measure), we present indicative results from different imaging domains and discuss the drawbacks/pitfalls of the method especially with regard to initial transformation selection and the initial direction vectors. Different starting point or/and different initial direction vectors may lead to different “optimal” alignment registration results which very often are erroneous. In order to solve this problem, we propose an extension of this method by enhancing its global optimization scheme by means of stochastic optimization. en_US
dc.language.iso en en_US
dc.publisher AMCL/TUC en_US
dc.subject Image Registration en_US
dc.subject Mutual Information en_US
dc.subject Genetic Algorithms en_US
dc.title An extended method for robust image registration en_US
dc.type Article en_US

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  • NumAn2014 Proceedings
    Proceedings of the 6th International Conference on Numerical Analysis (NumAn2014)

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