Tensor Visualization and Defect Detection for Nematic Liquid Crystals using Shape Characteristics

  • T. J. Jankun-Kelly
  • Song Zhang
  • A. C. Callan-Jones
  • Robert A. Pelcovits
  • V. A. Slavin
  • David H. Laidlaw
Part of the Mathematics and Visualization book series (MATHVISUAL)


Two alternate sets of tensor shape characteristics are introduced for the study of nematic liquid crystals, a little studied problem in tensor visualization. One set of characteristics are based on the physics of the liquid crystal system (a real, symmetric, traceless tensor); the other set is an application of the well known Westin DT-MRI shape characteristics. These shape metrics are used both for direct tensor visualization and for detection of defects within the liquid crystal matrix.


Liquid Crystal Defect Detection Nematic Liquid Crystal Topological Defect Defect Core 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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We thank Ketan Mehta for his work on unstructured grid-based disclination detection and Gordon Kindlmann for our in-depth discussions. Rajendran Mohanraj and Huangli Wu, formerly of the Mississippi State SimCenter, provided the human Ig anitibody simulation data. This work was partially funded through the National Science Foundation EPSCoR program via award No. 0132618.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • T. J. Jankun-Kelly
    • 1
  • Song Zhang
    • 1
  • A. C. Callan-Jones
    • 2
  • Robert A. Pelcovits
    • 3
  • V. A. Slavin
    • 4
  • David H. Laidlaw
    • 4
  1. 1.Department of Computer Science and Engineering, Bagley College of EngineeringMississippi State UniversityMississippi StateUSA
  2. 2.Institut Curie, UMR CNRS 168Paris Cedex 05France
  3. 3.Department of PhysicsBrown UniversityProvidenceUSA
  4. 4.Department of Computer ScienceBrown UniversityProvidenceUSA

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