Advertisement

Depicting Time Evolving Flow with Illustrative Visualization Techniques

  • Wei-Hsien Hsu
  • Jianqiang Mei
  • Carlos D. Correa
  • Kwan-Liu Ma
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 30)

Abstract

Visualization has become an indispensable tool for scientists to extract knowledge from large amounts of data and convey that knowledge to others. Visualization may be exploratory or illustrative. Exploratory visualization generally provides multiple views of the data at different levels of abstraction and should be highly interactive, whereas illustrative visualization is often made offline at high quality with sufficient knowledge about the data and features of interest. Techniques used by professional illustrators may be borrowed to enhance the clarity and aesthetics of the visualization. This paper presents a set of visualization techniques for presenting the evolution of 3D flow. While the spatial features of the data is rendered in 3D space, the temporal behaviors of the flow are depicted using image-based methods. We demonstrate visualization results generated using three data sets obtained from simulations.

Keywords

Volume visualization time-varying data visualization image processing evolution drawing non-photorealistic rendering 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Balabanian, J.-P., Viola, I., Möller, T., Gröller, E.: Temporal styles for time-varying volume data. In: Gumhold, S., Kosecka, J., Staadt, O. (eds.) Proceedings of 3DPVT 2008 - the Fourth International Symposium on 3D Data Processing, Visualization and Transmission, June 2008, pp. 81–89 (2008)Google Scholar
  2. 2.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)CrossRefGoogle Scholar
  3. 3.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Inc., Upper Saddle River (2006)Google Scholar
  4. 4.
    Gooch, B., Sloan, P.-P.J., Gooch, A., Shirley, P., Riesenfeld, R.: Interactive technical illustration. In: I3D 1999: Proceedings of the 1999 symposium on Interactive 3D graphics, pp. 31–38 (1999)Google Scholar
  5. 5.
    Healey, C.G., Tateosian, L., Enns, J.T., Remple, M.: Healey, Laura Tateosian, James T. Enns, and Mark Remple. Perceptually based brush strokes for nonphotorealistic visualization. ACM Trans. Graph. 23(1), 64–96 (2004)CrossRefGoogle Scholar
  6. 6.
    Joshi, A., Rheingans, P.: Illustration-inspired techniques for visualizing time-varying data, pp. 679–686 (October 2005)Google Scholar
  7. 7.
    Lee, T.-Y., Shen, H.-W.: Visualizing time-varying features with tac-based distance fields, April 2009, pp. 1–8 (2009)Google Scholar
  8. 8.
    Lee, Y., Markosian, L., Lee, S., Hughes, J.F.: Line drawings via abstracted shading. In: ACM SIGGRAPH 2007, p. 18 (2007)Google Scholar
  9. 9.
    Lu, A., Shen, H.-W.: Interactive storyboard for overall time-varying data visualization, March 2008, pp. 143–150 (2008)Google Scholar
  10. 10.
    Ma, K.-L.: Visualizing time-varying volume data. Computing in Science and Engg. 5(2), 34–42 (2003)CrossRefGoogle Scholar
  11. 11.
    Mayr, E.: What evolution is. Basic Books, New York (2001)Google Scholar
  12. 12.
    Muelder, C., Ma, K.-L.: Interactive feature extraction and tracking by utilizing region coherency. In: Proceedings of IEEE Pacific Visualization 2009 Symposium (April 2009)Google Scholar
  13. 13.
    Post, F.H., Post, F.J., Van Walsum, T., Silver, D.: Iconic techniques for feature visualization. In: VIS 1995: Proceedings of the 6th conference on Visualization 1995, pp. 288–295 (1995)Google Scholar
  14. 14.
    Silver, D., Wang, X.: Tracking scalar features in unstructured data sets, pp. 79–86 (October 1998)Google Scholar
  15. 15.
    Silver, D., Wang, X.: Volume tracking. In: VIS 1996: Proceedings of the 7th conference on Visualization 1996, pp. 157–164 (1996)Google Scholar
  16. 16.
    Stompel, A., Lum, E., Ma, K.-L.: Feature-enhanced visualization of multidimensional, multivariate volume data using non-photorealistic rendering techniques. In: Proceedings of Pacific Graphics 2002, pp. 1–8. IEEE, Los Alamitos (2002)Google Scholar
  17. 17.
    Svakhine, N.A., Jang, Y., Ebert, D., Gaither, K.: Illustration and photography inspired visualization of flows and volumes, pp. 687–694 (October 2005)Google Scholar
  18. 18.
    Wen, F., Luan, Q., Liang, L., Xu, Y.-Q., Shum, H.-Y.: Color sketch generation. In: NPAR 2006: Proceedings of the 4th international symposium on Non-photorealistic animation and rendering, pp. 47–54 (2006)Google Scholar
  19. 19.
    Woodring, J., Shen, H.-W.: Chronovolumes: a direct rendering technique for visualizing time-varying data. In: VG 2003: Proceedings of the 2003 Eurographics/IEEE TVCG Workshop on Volume graphics, pp. 27–34 (2003)Google Scholar
  20. 20.
    Woodring, J., Wang, C., Shen, H.-W.: High dimensional direct rendering of time-varying volumetric data. In: VIS 2003: Proceedings of the 14th IEEE Visualization 2003 (VIS 2003), pp. 417–424 (2003)Google Scholar
  21. 21.
    Ziou, D., Tabbone, S.: Edge Detection Techniques-An Overview. Pattern Recognition & Image Analysis 8, 537–559 (1998)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2010

Authors and Affiliations

  • Wei-Hsien Hsu
    • 1
  • Jianqiang Mei
    • 1
    • 2
  • Carlos D. Correa
    • 1
  • Kwan-Liu Ma
    • 1
  1. 1.University of California, DavisDavisUSA
  2. 2.Tianjin University, TianjinTianjinP.R. China

Personalised recommendations