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Fabric-like Visualization of Tensor Field Data on Arbitrary Surfaces in Image SpaceEichelbaum, Sebastian, Hlawitschka, Mario, Hamann, Bernd, Scheuermann, Gerik 14 December 2018 (has links)
Tensors are of great interest to many applications in engineering and in medical imaging, but a proper analysis and visualization remains challenging. It already has been shown that, by employing the metaphor of a fabric structure, tensor data can be visualized precisely on surfaces where the two eigendirections in the plane are illustrated as thread-like structures. This leads to a continuous visualization of most
salient features of the tensor data set. We introduce a novel approach to compute such a visualization from tensor field data that is motivated by image-space line integral convolution (LIC). Although our
approach can be applied to arbitrary, non-selfintersecting surfaces, the main focus lies on special surfaces following important features, such as surfaces aligned to the neural pathways in the human brain. By adding a postprocessing step, we are able to enhance the visual quality of the of the results, which improves perception of the major patterns.
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