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A Visual Approach to Analysis of Stress Tensor Fields
Zuse Institute Berlin.
Zuse Institue Berlin.
Zuse Institue Berlin.ORCID iD: 0000-0001-7285-0483
2011 (English)In: Scientific Visualization: Interactions, Features, Metaphors, Dagstuhl Follow-Ups, ISSN 1868-8977, Vol. 2, 188-211 p.Article in journal (Refereed) Published
Abstract [en]

We present a visual approach for the exploration of stress tensor fields. In contrast to common tensor visualization methods that only provide a single view to the tensor field, we pursue the idea of providing various perspectives onto the data in attribute and object space. Especially in the context of stress tensors, advanced tensor visualization methods have a young tradition. Thus, we propose a combination of visualization techniques domain experts are used to with statistical views of tensor attributes. The application of this concept to tensor fields was achieved by extending the notion of shape space. It provides an intuitive way of finding tensor invariants that represent relevant physical properties. Using brushing techniques, the user can select features in attribute space, which are mapped to displayable entities in a three-dimensional hybrid visualization in object space. Volume rendering serves as context, while glyphs encode the whole tensor information in focus regions. Tensorlines can be included to emphasize directionally coherent features in the tensor field. We show that the benefit of such a multi-perspective approach is manifold. Foremost, it provides easy access to the complexity of tensor data. Moreover, including well-known analysis tools, such as Mohr diagrams, users can familiarize themselves gradually with novel visualization methods. Finally, by employing a focus-driven hybrid rendering, we significantly reduce clutter, which was a major problem of other three-dimensional tensor visualization methods. 

Place, publisher, year, edition, pages
2011. Vol. 2, 188-211 p.
Keyword [en]
Tensor Field, Visualization and Analysis
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-127674DOI: 10.4230/DFU.Vol2.SciViz.2011.188OAI: oai:DiVA.org:liu-127674DiVA: diva2:926365
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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