Journal of Visualization

, Volume 20, Issue 2, pp 393–404 | Cite as

Information-theoretic exploration for texture-based visualization

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Abstract

In this paper, we present a novel texture-based visualizing algorithm. This algorithm can be used as a robust and effective descriptor based on view-dependent information-theoretic statistical analysis of flow data. We calculate local entropy values to measure the distinct feature structures in the underlying field. Volume rendering is used to visualize the extracted flow patterns automatically. Due to the high computational expense, texture construction and rendering skip all empty and unimportant blocks. And an efficient GPU implementation is integrated together for high interactive frame rates. We show the key components of our approach with detailed analysis, and demonstrate that our method can visualize 2D and 3D flow data effectively.

Graphical abstract

Keywords

Information theory Texture-based visualization Sparse noise design Volume LIC Volume rendering 

Notes

Acknowledgments

The authors would like to thank the reviewers for their valuable comments. Thanks to Prof. Roger Crawfis from Ohio State University for kindly providing the Tornado data set, and thanks to Wang Z.F. from the Institute of Atmospheric Physics in Chinese Academy of Sciences for providing the atmospheric pollution data set in Pearl River Delta region. This work is supported and funded by the National Natural Science Foundation of China (No. 61173067, No. 61272322, No. 61303157, No. 61379085).

References

  1. Bordoloi UD, Shen HW (2005) View selection for volume rendering. In: Proceedings of IEEE Visualization Conference’05, Minneapolis, MN, USA, October 2005, pp 487–494Google Scholar
  2. Crawfis R, Max N (1992) Direct volume visualization of three-dimensional vector fields. In: Proceedings of the 1992 Workshop Volume Visualization, pp 55–60Google Scholar
  3. Daniel R (2009) An algorithm for computing Voronoi diagrams of general generators in general normed spaces. In: Proceedings of the sixth International Symposium on Voronoi Diagrams in science and engineering (ISVD 2009), pp 144–152Google Scholar
  4. Daniel R (2011) The geometric stability of Voronoi diagrams with respect to small changes of the sites, Full version: arXiv 1103.4125 (2011). Extended abstract in Proceedings of the 27th Annual ACM Symposium on Computational Geometry (SoCG 2011), pp 254–263Google Scholar
  5. Falk M, Weiskopf D (2008) Output-sensitive 3d line integral convolution. IEEE Trans Vis Comput Graph 14(4):820–834CrossRefGoogle Scholar
  6. Helgeland A, Andreassen O (2004) Visualization of vector fields using seed LIC and volume rendering. IEEE Trans Vis Comput Graph 10(6):673–682CrossRefGoogle Scholar
  7. Interrante V (1997) Illustrating surface shape in volume data via principal direction-driven 3d line integral convolution. In: Computer Graphics Proceedings, Annual Conference Series, Aug 1997, pp 109–116Google Scholar
  8. Interrante V, Grosch C (1998) Visualizing 3d flow. IEEE Comput Graph Appl 18(4):49–53CrossRefGoogle Scholar
  9. Jänicke H, Wiebel A, Scheuermann G, Kollmann W (2007) Multifield visualization using local statistical complexity. IEEE Trans Vis Comput Graph 13(6):1384–1391CrossRefGoogle Scholar
  10. Jänicke H, Böttinger M, Tricoche X, Scheuermann G (2008) Automatic detection and visualization of distinctive structures in 3d unsteady multi-fields. Comput Graph Forum 27(3):767–774CrossRefGoogle Scholar
  11. Jänicke H, Scheuermann G (2010) Visual analysis of flow features using information theory. IEEE Trans Vis Comput Graph 30(1):40–49CrossRefGoogle Scholar
  12. Jobard B, Lefer W (1997) Creating evenly-spaced streamlines of arbitrary density. In: Proceedings of the Eurographics Workshop Visualization in Scientific Computing, pp 45–55Google Scholar
  13. Li GS, Bordoloi U, Shen HW (2003) Chameleon: an interactive texture-based rendering framework for visualizing three-dimensional vector fields. In: Proceedings of the IEEE Visualization, pp 241–248Google Scholar
  14. Liu Z, Moorhead RJ (2008) II. Interactive view-driven evenly spaced streamline placement. In: Proceedings of the IS&T/SPIE Conference on Visualization and Data Analysis (VDA ’08), pp 1–12Google Scholar
  15. Lu DY, Zhu DM, Wang ZQ (2016) Efficient level of detail for texture-based flow visualization. Comput Animat Virtual Worlds 27(2):123–140CrossRefGoogle Scholar
  16. Mao X, Kikukawa M, Fujita N, Imamiya A (1997) Line integral convolution for 3d surfaces. In: EG Workshop on Visualization’97, pp 57–70Google Scholar
  17. Max N (1995) Optical models for direct volume rendering. IEEE Trans Vis Comput Graph 1(2):99–108CrossRefGoogle Scholar
  18. Post FH, Vrolijk B, Hauser H, Laramee RS, Doleisch H (2003) The state of the art in flow visualisation: feature extraction and tracking. Comput Graph Forum 22(4):775–792CrossRefGoogle Scholar
  19. Reza FM (1961, 1994) An introduction to information theory. Dover Publications, Inc., New York. ISBN 0-486-68210-2Google Scholar
  20. Rezk-Salama C, Hastreiter P, Teitzel C, Ertl T (1999) Interactive exploration of volume line integral convolution based on 3d-texture mapping. In: Proceedings of the IEEE Visualization, pp 233–240Google Scholar
  21. Shalizi CR, Haslinger R, Rouquier J-B, Klinkner KL, Moore C (2006) Automatic filters for the detection of coherent structure in spatiotemporal systems. Phys Rev E 73:036104MathSciNetCrossRefGoogle Scholar
  22. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423MathSciNetCrossRefMATHGoogle Scholar
  23. Shen HW, Li GS, Bordoloi U (2004) Interactive visualization of three-dimensional vector fields with flexible appearance control. IEEE Trans Vis Comput Graph 10(4):434–445CrossRefGoogle Scholar
  24. Takahashi S, Fujishiro I, Takeshima Y, Nishita T (2005) A feature-driven approach to locating optimal viewpoints for volume visualization. In: Proceedings of IEEE Visualization Conference’05, Minneapolis, MN, USA, October 2005, pp 495–502Google Scholar
  25. Verma V, Kao DT, Pang A (2000) A flow-guided streamline seeding strategy. In: Vis’00: Proceedings of the IEEE Visualization 2000, pp 163–170Google Scholar
  26. Wang C, Yu H, Ma KL (2008) Importance-driven time-varying data visualization. IEEE Trans Vis Comput Graph. 14(6):1547–1554CrossRefGoogle Scholar
  27. Wang C, Shen HW (2006) LOD map—a visual interface for navigating multiresolution volume visualization. IEEE Trans Vis Comput Graph 12(5):1029–1036CrossRefGoogle Scholar
  28. Weiskopf D, Ertl T (2004) A hybrid physical/device-space approach for spatio-temporally coherent interactive texture advection on curved surfaces. In: GI’04 Proceedings of Graphics Interface, pp 263–270Google Scholar
  29. Xu L, Lee TY, Shen HW (2010) An information-theoretic framework for flow visualization. IEEE Trans Vis Comput Graph 16(6):1216–1224CrossRefGoogle Scholar
  30. Ye X, Kao DT, Pang A (2005) Strategy for seeding 3d streamlines. In: Vis ’05: Proceedings of the IEEE Visualization 2005, pp 471–478Google Scholar

Copyright information

© The Visualization Society of Japan 2016

Authors and Affiliations

  1. 1.College of SoftwareQufu Normal UniversityQufuPeople’s Republic of China
  2. 2.Institute of Computing TechnologyChinese Academy of SciencesBeijingPeople’s Republic of China

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