Skip to main content
SpringerLink
Log in

Fast LIC Image Generation Based on Significance Map

  • Conference paper
  • First Online:
High Performance Computing (ISHPC 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1940))

Included in the following conference series:

Abstract

Although texture-based methods provide a very promising way to visualize 3D vector fields, they are very time-consuming. In this paper, we introduce the notion of “significance map”, and describe how significance values are derived from the intrinsic properties of a vector field. Based on the significance map, we propose techniques to accelerate the generation of a line integral convolution (LIC) texture image, to highlight important structures in a vector field, and to generate an LIC texture image with different granularities. Also, we describe how to implement our method in a parallel environment. Experimental results illustrate the feasibility of our method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cabral, B., Leedom, C.: Image Vector Field Using Line Integral Convolution. In: Computer Graphics Proceedings, Annual Conference Series. ACM SIGGRAPH, New York (1993) 263–272

    Google Scholar 

  2. Stalling, D., Hege, H.: Fast and Resolution Independent Line Integral Convolution. In: Computer Graphics Proceedings, Annual Conference Series. ACM SIGGRAPH, New York (1995) 249–256

    Google Scholar 

  3. Forssell, L. K., Cohen, S. D.: Using Line Integral Convolution for Flow Visualization: Curvilinear Grids, Variable-speed Animation and Unsteady Flows. IEEE Transactions on Visualization and Computer Graphics, Vol.1, No.2 (1995) 133–141

    Article  Google Scholar 

  4. Mao, X., et al.: Line Integral Convolution for Arbitrary 3D Surfaces Through Solid Texturing. In: Lefer, W., Grave, M. (eds.): Visualization in Scientific Computing’ 97. Springer-Verlag, Wien (1997) 57–69

    Google Scholar 

  5. Shen, H.-W., Johnson, C., Ma, K.-L.: Visualizing Vector Fields Using Line Integral Convolution and Dye Advection. In: Proceedings of 1996 Symposium on Volume Visualization. ACM SIGGRAPH, New York (1996) 63–70

    Google Scholar 

  6. Shen, H.-W., Kao, D.: UFLIC: Line Integral Convolution Algorithm for Visualizing Unsteady Flows. In: Proceedings of IEEE Visualization’ 97. ACM Press, New York (1997) 317–322

    Google Scholar 

  7. Okada, A., Kao, D.: Enhanced Line Integral Convolution with Flow Feature Detection. In: SPIE, Vol. 3017, Visual Data Exploration and Analysis IV (1997) 206–217

    Google Scholar 

  8. Liu, M.-H., Banks, D. C.: Multi-Frequency Noise for LIC. In Proceedings of IEEE Visualization’ 96. ACM Press, New York (1996) 121–126

    Google Scholar 

  9. Wegenkittl, R., Groller, E., Purgathofer, W.: Animating Flow Fields: Rendering of Oriented Line Integral Convolution. In: Proceedings of Computer Animation’97. IEEE Computer Society Press, Los Alamitos (1997) 15–21

    Google Scholar 

  10. Verma, V., Kao, D., Pang, A.: PLIC: Bridging the Gap Between Streamlines and LIC. In: Proceedings of IEEE Visualization’99.ACM Press, New York (1999) 341–348

    Google Scholar 

  11. Chong, M. S., Perry, A. E., Cantwell, B. J.: A General Classification of 3D Flow Fields. Physics of Fluids Ann., Vol. 2, No.5 (1990) 765–777

    Article  MathSciNet  Google Scholar 

  12. Helman, J., Hesselink, L.: Visualizing Vector Field Topology in Fluid Flows.IEEE Computer Graphics and Applications, Vol.11, No.3 (1993) 36–46

    Article  Google Scholar 

  13. Sujudi, D., Haimes, R.: Identification of Swirling Flow in 3-D Vector Fields. AIAA-95-1715 (1995)

    Google Scholar 

  14. Kenwright, D.: Automatic Vortex Core Detection. IEEE Computer Graphics and Applications, Vol. 18, No. 4 (1998) 70–74

    Article  Google Scholar 

  15. Roth, M., Peikert, R.: A Higher-Order Method for Finding Vortex Core Lines. In: Proceedings of IEEE Visualization’ 98. ACM Press, New York (1998) 143–150

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Organization for Information Science & Technology, 1-18-16 Hamamatsucho, 105-0013, Minato-ku, Tokyo, Japan

    Li Chen & Issei Fujishiro

  2. Department of Information Sciences, Faculty of Science, Ochanomizu University, 2-1-1 Otsuka, 112-8610, Bunkyo-ku, Tokyo, Japan

    Issei Fujishiro

  3. State Key Lab. of CAD&CG, Zhejiang University, 310027, Hangzhou, P. R. China

    Li Chen & Qunsheng Peng

Authors
  1. Li Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Issei Fujishiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qunsheng Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Departamento de Arquitectura de Computadores, Universidad Politecnica de Catalunya, Spain

    Mateo Valero

  2. Department of Information and Computer Sciences, Nara Women’s University, Japan

    Kazuki Joe

  3. Institute of Industrial Science Center for Conceptual Information Processing Research, University of Tokyo, Japan

    Masaru Kitsuregawa

  4. Graduate School of Engineering Electrical Engineering Department, University of Tokyo, Japan

    Hidehiko Tanaka

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chen, L., Fujishiro, I., Peng, Q. (2000). Fast LIC Image Generation Based on Significance Map. In: Valero, M., Joe, K., Kitsuregawa, M., Tanaka, H. (eds) High Performance Computing. ISHPC 2000. Lecture Notes in Computer Science, vol 1940. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39999-2_52

Download citation

  • DOI: https://doi.org/10.1007/3-540-39999-2_52

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41128-4

  • Online ISBN: 978-3-540-39999-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation