Abstract
A variational framework for the registration of tensor-valued images is presented. The underlying energy functional consists of four terms: a data term modelled on a tensor constancy constraint, a compatibility term which couples domain deformations and tensor reorientation on the basis of a physical model, and regularity terms imposing smoothness of deformation and tensor reorientation fieldss in space. A specific feature of our model is the separation of data and compatibility terms which eases an adaptation to different physical models of tensor deformation. A multiscale gradient descent is used to minimise the energy functional with repect to both transformation fields involved. The viability and potential of the approach in the registration of tensor-valued images is demonstrated by experiments.
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Barbieri, S., Welk, M., Weickert, J. (2009). A Variational Approach to the Registration of Tensor-Valued Images. In: Aja-Fernández, S., de Luis GarcÃa, R., Tao, D., Li, X. (eds) Tensors in Image Processing and Computer Vision. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84882-299-3_3
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DOI: https://doi.org/10.1007/978-1-84882-299-3_3
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