License: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license (CC BY-NC-ND 3.0)
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DOI: 10.4230/DFU.Vol2.SciViz.2011.17
URN: urn:nbn:de:0030-drops-32853
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Auer, Cornelia ; Sreevalsan-Nair, Jaya ; Zobel, Valentin ; Hotz, Ingrid

2D Tensor Field Segmentation

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We present a topology-based segmentation as means for visualizing 2D symmetric tensor fields. The segmentation uses directional as well as eigenvalue characteristics of the underlying field to delineate cells of similar (or dissimilar) behavior in the tensor field. A special feature of the resulting cells is that their shape expresses the tensor behavior inside the cells and thus also can be considered as a kind of glyph representation. This allows a qualitative comprehension of important structures of the field. The resulting higher-level abstraction of the field provides valuable analysis. The extraction of the integral topological skeleton using both major and minor eigenvector fields serves as a structural pre-segmentation and renders all directional structures in the field. The resulting curvilinear cells are bounded by tensorlines and already delineate regions of equivalent eigenvector behavior. This pre-segmentation is further adaptively refined to achieve a segmentation reflecting regions of similar eigenvalue and eigenvector characteristics. Cell refinement involves both subdivision and merging of cells achieving a predetermined resolution, accuracy and uniformity of the segmentation. The buildingblocks of the approach can be intuitively customized to meet the demands or different applications. Application to tensor fields from numerical stress simulations demonstrates the effectiveness of our method.

BibTeX - Entry

  author =	{Cornelia Auer and Jaya Sreevalsan-Nair and Valentin Zobel and Ingrid Hotz},
  title =	{{2D Tensor Field Segmentation}},
  booktitle =	{Scientific Visualization: Interactions, Features, Metaphors},
  pages =	{17--35},
  series =	{Dagstuhl Follow-Ups},
  ISBN =	{978-3-939897-26-2},
  ISSN =	{1868-8977},
  year =	{2011},
  volume =	{2},
  editor =	{Hans Hagen},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-32853},
  doi =		{10.4230/DFU.Vol2.SciViz.2011.17},
  annote =	{Keywords: Tensorfield visualization, surface topology}

Keywords: Tensorfield visualization, surface topology
Collection: Scientific Visualization: Interactions, Features, Metaphors
Issue Date: 2011
Date of publication: 26.10.2011

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