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.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:32502 |
| Date | 14 December 2018 |
| Creators | Eichelbaum, Sebastian, Hlawitschka, Mario, Hamann, Bernd, Scheuermann, Gerik |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:bookPart, info:eu-repo/semantics/bookPart, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 978-3-642-27342-1 |
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