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2D Tensor Field Segmentation
Zuse Institute Berlin, Berlin, Germany.
IIIT – Bangalore, Electronics City, Hosur Road, Bangalore, India.
Zuse Institue Berlin, Berlin, Germany.
Zuse Institue Berlin, Berlin, Germany.ORCID iD: 0000-0001-7285-0483
2011 (English)In: Dagstuhl Follow-Ups, E-ISSN 1868-8977, Vol. 2, 17-35 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Dagstuhl, Germany: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik , 2011. Vol. 2, 17-35 p.
Keyword [en]
Tensor field visualization, Segmentation, Topology
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-127675DOI: 10.4230/DFU.Vol2.SciViz.2011.17OAI: oai:DiVA.org:liu-127675DiVA: diva2:926366
Available from: 2016-05-06 Created: 2016-05-06 Last updated: 2017-03-09Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf