Segmentation and Visualization of Multivariate Features Using Feature-Local Distributions

  • Kenny Gruchalla
  • Mark Rast
  • Elizabeth Bradley
  • Pablo Mininni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)

Abstract

We introduce an iterative feature-based transfer function design that extracts and systematically incorporates multivariate feature-local statistics into a texture-based volume rendering process. We argue that an interactive multivariate feature-local approach is advantageous when investigating ill-defined features, because it provides a physically meaningful, quantitatively rich environment within which to examine the sensitivity of the structure properties to the identification parameters. We demonstrate the efficacy of this approach by applying it to vortical structures in Taylor-Green turbulence. Our approach identified the existence of two distinct structure populations in these data, which cannot be isolated or distinguished via traditional transfer functions based on global distributions.

Keywords

Transfer Function Vorticity Vector Vorticity Magnitude IEEE Visualization Direct Volume Rendering 
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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References

  1. 1.
    Mininni, P.D., Alexakis, A., Pouquet, A.: Nonlocal interactions in hydrodynamic turbulence at high reynolds numbers: the slow emergence of scaling laws. Physical review. E, Statistical, nonlinear, and soft matter physics 77 (2008)Google Scholar
  2. 2.
    Silver, D., Zabusky, N.J.: Quantifying visualizations for reduced modeling in nonlinear science: Extracting structures from data sets. Journal of Visual Communication and Image Representation 4, 46–61 (1993)CrossRefGoogle Scholar
  3. 3.
    Silver, D., Wang, X.: Tracking and visualizing turbulent 3d features. IEEE Transactions on Visualization and Computer Graphics 3, 129–141 (1997)CrossRefGoogle Scholar
  4. 4.
    Ebling, J., Scheuermann, G.: Clifford convolution and pattern matching on vector fields. In: Proceedings of IEEE Visualization, pp. 193–200 (2003)Google Scholar
  5. 5.
    Heiberg, E., Ebbers, T., Wigstrom, L., Karlsson, M.: Three-dimensional flow characterization using vector pattern matching. IEEE Transactions on Visualization and Computer Graphics 9, 313–319 (2003)CrossRefGoogle Scholar
  6. 6.
    Helman, J.L., Hesselink, L.: Representation and display of vector field topology in fluid flow data sets. Computer 22, 27–36 (1989)CrossRefGoogle Scholar
  7. 7.
    Theisel, H., Weinkauf, T., Hege, H.C., Seidel, H.P.: Saddle connectors - an approach to visualizing the topological skeleton of complex 3d vector fields. In: Proceedings of IEEE Visualization, pp. 225–232. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  8. 8.
    Scheuermann, G., Tricoche, X.: Topological methods for flow visualization. In: Hansen, C., Johnson, C. (eds.) Visualization Handbook. Academic Press, London (2005)Google Scholar
  9. 9.
    Post, F.H., Vrolijk, B., Hauser, H., Laramee, R.S., Doleisch, H.: The state of the art in flow visualisation: Feature extraction and tracking. Computer Graphics Forum 22, 775–792 (2003)CrossRefGoogle Scholar
  10. 10.
    Suzuki, K., Horibia, I., Sugie, N.: Linear-time connected-component labeling based on sequential local operations. Computer Vision and Image Understanding 89, 1–23 (2003)CrossRefMATHGoogle Scholar
  11. 11.
    Wong, P.C., Bergeron, R.D.: 30 years of multidimensional multivariate visualization, pp. 3–33. IEEE Computer Society Press, Los Alamitos (1997)Google Scholar
  12. 12.
    Bürger, R., Hauser, H.: Visualization of multi-variate scientific data. In: Proceedings of EuroGraphics 2007 (State of the Art Reports), pp. 117–134 (2007)Google Scholar
  13. 13.
    Jänicke, H., Wiebel, A., Scheuermann, G., Kollmann, W.: Multifield visualization using local statistical complexity. IEEE Transactions on Visualization and Computer Graphics 13, 1384–1391 (2007)CrossRefGoogle Scholar
  14. 14.
    Sauber, N., Theisel, H., Seidel, H.P.: Multifield-graphs: An approach to visualizing correlations in multifield scalar data. IEEE Transactions on Visualization and Computer Graphics 12, 917–924 (2006)CrossRefGoogle Scholar
  15. 15.
    Kniss, J., Kindlmann, G., Hansen, C.: Multidimensional transfer functions for interactive volume rendering. IEEE Transactions on Visualization and Computer Graphics 8, 270–285 (2002)CrossRefGoogle Scholar
  16. 16.
    Park, S.W., Budge, B., Linsen, L., Hamann, B., Joy, K.I.: Multi-dimensional transfer functions for interactive 3d flow visualization. In: Proceedings of the 12th Pacific Conference on (PG 2004) Computer Graphics and Applications, pp. 177–185. IEEE Computer Society, Los Alamitos (2004)Google Scholar
  17. 17.
    Bajaj, C.L., Pascucci, V., Schikore, D.R.: The contour spectrum. In: Proceedings of the 8th conference on Visualization 1997, pp. 167–175. IEEE Computer Society Press, Los Alamitos (1997)Google Scholar
  18. 18.
    Tenginakai, S., Machiraju, R.: Statistical computation of salient iso-values. In: Proceedings of the Symposium on Data Visualisation 2002, pp. 19–24. Eurographics Association, Barcelona (2002)Google Scholar
  19. 19.
    Correa, C., Ma, K.L.: Size-based transfer functions: A new volume exploration technique. IEEE Transactions on Visualization and Computer Graphics 14, 1380–1387 (2008)CrossRefGoogle Scholar
  20. 20.
    Lundström, C., Ljung, P., Ynnerman, A.: Local histograms for design of transfer functions in direct volume rendering. IEEE Transactions on Visualization and Computer Graphics 12, 1570–1579 (2006)CrossRefGoogle Scholar
  21. 21.
    Clyne, J., Mininni, P.D., Norton, A., Rast, M.: Interactive desktop analysis of high resolution simulations: application to turbulent plume dynamics and current sheet formation. New Journal of Physics 9 (2007)Google Scholar
  22. 22.
    Gruchalla, K., Rast, M., Bradley, E., Clyne, J., Mininni, P.: Visualization-driven structural and statistical analysis of turbulent flows. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 321–332. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  23. 23.
    Kindlmann, G., Durkin, J.W.: Semi-automatic generation of transfer functions for direct volume rendering. In: Proceedings of IEEE Visualization, pp. 79–86 (1998)Google Scholar
  24. 24.
    Kniss, J., Premoze, S., Ikits, M., Lefohn, A., Hansen, C., Praun, E.: Gaussian transfer functions for multi-field volume visualization. In: Proceedings of IEEE Visualization, pp. 497–504. IEEE Computer Society, Los Alamitos (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kenny Gruchalla
    • 1
    • 2
  • Mark Rast
    • 2
    • 3
  • Elizabeth Bradley
    • 2
  • Pablo Mininni
    • 4
    • 3
  1. 1.National Renewable Energy LaboratoryGoldenUSA
  2. 2.University of ColoradoBoulderUSA
  3. 3.National Center for Atmospheric ResearchBoulderUSA
  4. 4.Universidad de Buenos AiresArgentina

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