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Optimal Parameters Selection for Non-parametric Image Registration Methods

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4179))

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Abstract

Choosing the adequate registration and simulation parameters in non-parametric image registration methods is an open question. There is no agreement about which are the optimal values (if any) for these parameters, since they depend on the images to be registered. As a result, in the literature the parameters involved in the registration process are arbitrarily fixed by the authors. The present paper is intended to address this issue. A two-step method is proposed to obtain the optimal values of these parameters, in terms of achieving in a minimum number of iterations the best trade-off between similarity of the images and smoothness of the transformation. These optimal values minimize the joint energy functional defined in a variational framework. We focus on the specific formulation of diffusion and curvature registration, but the exposed methodology can be directly applied to other non-parametric registration schemes. The proposed method is validated over different registration scenarios.

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Larrey-Ruiz, J., Morales-Sánchez, J. (2006). Optimal Parameters Selection for Non-parametric Image Registration Methods. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_52

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  • DOI: https://doi.org/10.1007/11864349_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44630-9

  • Online ISBN: 978-3-540-44632-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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