Fabric-Like Visualization of Tensor Field Data on Arbitrary Surfaces in Image Space [chapter]

Sebastian Eichelbaum, Mario Hlawitschka, Bernd Hamann, Gerik Scheuermann
<span title="">2012</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/k6cwcpfsu5gs7bvkoa5pdlbe6q" style="color: black;">Mathematics and Visualization</a> </i> &nbsp;
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
more &raquo; ... 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-self-intersecting 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 results, which improves perception of the major patterns.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-27343-8_4">doi:10.1007/978-3-642-27343-8_4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/b7r7zdto6fcvnccf7bcdljslhy">fatcat:b7r7zdto6fcvnccf7bcdljslhy</a> </span>
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