Abstract
The following paper presents a method that allows for a parallel implementation of the most computationally expensive element of the deformable template paradigm, which is a grid-matching procedure. Cellular Neural Network Universal Machine has been selected as a framework for the task realization. A basic idea of deformable grid matching is to guide node location updates in a way that minimizes dissimilarity between an image and grid-recorded information, and that ensures minimum grid deformations. The proposed method provides a parallel implementation of this general concept and includes a novel approach to grid’s elasticity modeling. The method has been experimentally verified using two different analog hardware environments, yielding high execution speeds and satisfactory processing accuracy.
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© 2007 Springer Berlin Heidelberg
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S̀lot, K., Korbel, P., Kim, H., Lee, M., Ko, S. (2007). Parallel Implementation of Elastic Grid Matching Using Cellular Neural Networks. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics Collaboration Techniques. MIRAGE 2007. Lecture Notes in Computer Science, vol 4418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71457-6_43
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DOI: https://doi.org/10.1007/978-3-540-71457-6_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71456-9
Online ISBN: 978-3-540-71457-6
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