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Tensor Field Reconstruction Based on Eigenvector and Eigenvalue Interpolation
University of California, USA.ORCID iD: 0000-0001-7285-0483
University of California, USA.
Technical University of Kaiserslautern,Kaiserslautern, Germany.
University of California, USA.
2010 (English)In: Scientific Visualization: Advanced Concepts, Dagstuhl follow-up, 110-123 p.Article in journal (Refereed) Epub ahead of print
Abstract [en]

Interpolation is an essential step in the visualization process. While most data from simulations or experiments are discrete many visualization methods are based on smooth, continuous data approximation or interpolation methods. We introduce a new interpolation method for symmetrical tensor fields given on a triangulated domain. Differently from standard tensor field interpolation, which is based on the tensor components, we use tensor invariants, eigenvectors and eigenvalues, for the interpolation. This interpolation minimizes the number of eigenvectors and eigenvalues computations by restricting it to mesh vertices and makes an exact integration of the tensor lines possible. The tensor field topology is qualitatively the same as for the component wise-interpolation. Since the interpolation decouples the “shape” and “direction” interpolation it is shape-preserving, what is especially important for tracing fibers in diffusion MRI data.

Place, publisher, year, edition, pages
2010. 110-123 p.
Keyword [en]
Tensor Field, Eigenvector, Eigenvalue, Interpolation
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:liu:diva-127687DOI: 10.4230/DFU.SciViz.2010.110OAI: diva2:926383
Available from: 2016-05-06 Created: 2016-05-06 Last updated: 2016-05-12

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Hotz, Ingrid
Computer Vision and Robotics (Autonomous Systems)

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ReferencesLink to record
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