Advertisement

Journal of Oceanology and Limnology

, Volume 37, Issue 6, pp 2025–2036 | Cite as

Dynamic visual simulation of marine vector field based on LIC—a case study of surface wave field in typhoon condition

  • Zhendong Liu
  • Haixing LiuEmail author
  • Tianyun SuEmail author
  • Zhen Jia
  • Xinfang Li
  • Lin Zhou
  • Zhuanling Song
Article

Abstract

Line integral convolution (LIC) is a useful visualization technique for a vector field. However, the output image produced by LIC has many problems in a marine vector field. We focus on the visual quality improvement when LIC is applied in the ocean steady and unsteady flow field in the following aspects. When a white noise is used as the input in a steady flow field, interpolation is used to turn the discrete white noise into continuous white noise to solve the problem of discontinuity. The “cross” high-pass filtering is used to enhance the textures of streamlines to be more concentrated and continuity strengthened for each streamline. When a sparse noise is used as the input in a steady flow field, we change the directions of background sparse noise according to the directions of vector field to make the streamlines clearer and brighter. In addition, we provide a random initial phase for every streamline to avoid the pulsation effect during animation. The velocities of vector field are encoded in the speed of the same length streamlines so that the running speed of streamlines can express flow rate. Meanwhile, to solve the problem of obvious boundaries when stitching image, we change the streamline tracking constraints. When a white noise is used as an input in an unsteady flow field, double value scattering is used to enhance the contrast of streamlines; moreover, the “cross” high-pass filtering is also adopt instead of two-dimensional high-pass filtering. Finally, we apply the above methods to a case of the surface wave field in typhoon condition. Our experimental results show that applying the methods can generate high-quality wave images and animations. Therefore, it is helpful to understand and study waves in typhoon condition to avoid the potential harm of the waves to people’s lives and property.

Keywords

line integral convolution (LIC) wave data visualization steady and unsteady marine flow field 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgement

We thank Professor LIU Zhanping of the Old Dominion University for providing technical assistance.

References

  1. Berger S, Gröller E. 2000. Color-table animation of fast oriented line integral convolution for vector field visualization. In: Proceedings of the 8th International Conference in Central Europe on Computers Graphics. Research Division of Computer Graphics Research Publications, Bohemia, Plzen. p.4–11.Google Scholar
  2. Cabral B, Leedom L C. 1993. Imaging vector fields using line integral convolution. In: Proceedings of the 20th Annual Conference on Computer Graphics and Interactive Techniques. ACM, Anaheim, CA. p.263–270,  https://doi.org/10.1145/166117.166151.Google Scholar
  3. Ding Z A, Liu Z P, Yu Y, Chen W. 2015. Parallel unsteady flow line integral convolution for high-performance dense visualization. In: Proceedings of 2015 IEEE Pacific Visualization Symposium. IEEE, Hangzhou, China. p.25–30,  https://doi.org/10.1109/PACIFICVIS.2015.7156352.CrossRefGoogle Scholar
  4. Forssell L K, Cohen S D. 1995. Using line integral convolution for flow visualization: curvilinear grids, variable-speed animation, and unsteady flows. IEEE Transactions on Visualization and Computer Graphics, 1(2): 133–141,  https://doi.org/10.1109/2945.468406.CrossRefGoogle Scholar
  5. Hege H C, Stalling D. 1998. Fast LIC with piecewise polynomial filter kernels. In: Hege H C, Polthier K eds. Mathematical Visualization. Springer, Heidelberg, Berlin. p.295–314.CrossRefGoogle Scholar
  6. Höller M, Ehricke H H, Synofzik M, Klose U, Groeschel S. 2017. Clinical application of fiber visualization with LIC maps using multidirectional anisotropic glyph samples (A-Glyph LIC). Clinical N euroradiology, 27(3): 263–273,  https://doi.org/10.1007/s00062-015-0486-8.CrossRefGoogle Scholar
  7. Höller M, Klose U, Gröschel S, Otto K M, Ehricke H H. 2016. Visualization of MRI diffusion data by a multi-kernel LIC approach with anisotropic glyph samples. In: Linsen L, Hamann B, Hege H C eds. Visualization in Medicine and Life Sciences III. Springer, Cham, Switzerland. p.157–177.CrossRefGoogle Scholar
  8. Kiu M H, Banks D C. 1996. Multi-frequency noise for LIC. In: Proceedings of the Seventh Annual IEEE Visualization 1996. IEEE, San Francisco, CA, USA. p.121–126,  https://doi.org/10.1109/VISUAL.1996.567784.Google Scholar
  9. Kong Q Y, Sheng Y, Zhang G X. 2018. Hybrid noise for LICbased pencil hatching simulation. In: Proceedings of 2018 IEEE International Conference on Multimedia and Expo (ICME). IEEE, San Diego, CA, USA. p.1–6,  https://doi.org/10.1109/ICME.2018.8486527.Google Scholar
  10. Li G S, Tricoche X, Hansen C. 2006. GPUFLIC: interactive and accurate dense visualization of unsteady flows. In: Eurographics/IEEE-VGTC Symposium on Visualization. IEEE, Lisboa, Portugal. p.29–34,  https://doi.org/10.2312/VisSym/EuroVis06/029-034.Google Scholar
  11. Li P K, Zang Y, Wang C, Li J, Cheng M, Luo L, Yu Y. 2016. Road network extraction via deep learning and line integral convolution. In: Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, Beijing, China,  https://doi.org/10.1109/IGARSS.2016.7729408.Google Scholar
  12. Liu Z P, Moorhead II R J. 2002. AUFLIC: an accelerated algorithm for unsteady flow line integral convolution. In: Proceedings of IEEE TCVG Symposium on Visualization. Eurographics Association, Barcelona, Spain. p.43–52,  https://doi.org/10.2312/VisSym/VisSym02/043-052.Google Scholar
  13. Liu Z P, Moorhead II R J. 2005. Accelerated unsteady flow line integral convolution. IEEE Transactions on Visualization and Computer Graphics, 11(2): 113–125,  https://doi.org/10.1109/TVCG.2005.21.CrossRefGoogle Scholar
  14. Ma Y Y, Guo Y F. 2018. Visualization of vector field using line integral convolution based on visual perception. In: Proceedings of the 2nd International Symposium on Computer Science and Intelligent Control. ACM, Stockholm, Sweden,  https://doi.org/10.1145/3284557.3284709.Google Scholar
  15. Matvienko V, Krüger J. 2015. Explicit frequency control for high-quality texture-based flow visualization. In: Proceedings of 2015 IEEE Scientific Visualization Conference (SciVis). IEEE, Chicago, IL, USA. p.41–48,  https://doi.org/10.1109/SciVis.2015.7429490.CrossRefGoogle Scholar
  16. Okada A, Kao D L. 1997. Enhanced line integral convolution with flow feature detection. In: Proceedings of SPIE 3017, Visual Data Exploration and Analysis IV. SPIE, San Jose, CA, United States. p.206–217,  https://doi.org/10.1117/12.270314.Google Scholar
  17. Shen H W, Kao D L. 1997. UFLIC: A line integral convolution algorithm for visualizing unsteady flows. In: Proceedings of Visualization’ 97 (Cat. No. 97CB36155). IEEE, Phoenix, AZ, USA. p.317–323.CrossRefGoogle Scholar
  18. Shen H W, Kao D L. 1998. A new line integral convolution algorithm for visualizing time-varying flow fields. IEEE Transactions on Visualization and Computer Graphics, 4(2): 98–108,  https://doi.org/10.1109/2945.694952.CrossRefGoogle Scholar
  19. Stalling D, Hege H C. 1995. Fast and resolution independent line integral convolution. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques. ACM, Los Angeles, CA, USA. p.249–256,  https://doi.org/10.1145/218380.218448.Google Scholar
  20. Urness T, Interrante V, Marusic I, Longmire E, Ganapathisubramani B. 2003. Effectively visualizing multi-valued flow data using color and texture. In: Proceedings of the 14th IEEE Visualization 2003. IEEE, Seattle, WA, USA. p.115–122,  https://doi.org/10.1109/VISUAL.2003.1250362.Google Scholar
  21. van Wijk J J. 1991. Spot noise texture synthesis for data visualization. ACM SIGGRAPH Computer Graphics, 25(4): 309–318,  https://doi.org/10.1145/127719.122751.CrossRefGoogle Scholar
  22. Wegenkittl R, Gröller E, Purgathofer W. 1997. Animating flow fields: rendering of oriented line integral convolution. In: Proceedings of Computer Animation 1997. IEEE, Geneva, Switzerland. p.15–21,  https://doi.org/10.1109/CA.1997.601035.Google Scholar
  23. Wegenkittl R, Gröller E. 1997. Fast oriented line integral convolution for vector field visualization via the Internet. In: Proceedings of IEEE Visualization 1997. IEEE, Phoenix, AZ, USA. p.309–316,  https://doi.org/10.1109/VISUAL.1997.663897.Google Scholar
  24. Weiskopf D. 2009. Iterative twofold line integral convolution for texture-based vector field visualization. In: Möller T, Hamann B, Russell R D eds. Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration. Springer, Heidelberg, Berlin. p.191–211.CrossRefGoogle Scholar
  25. Zheng Y H, Ma K, Wang S F, Sun J. 2018. Line integral convolution-based non-local structure tensor. International Journal of Computational Science and Engineering, 16(1): 98–105,  https://doi.org/10.1504/IJCSE.2018.089601.CrossRefGoogle Scholar

Copyright information

© Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Zhendong Liu
    • 1
    • 3
  • Haixing Liu
    • 1
    • 2
    • 3
    Email author
  • Tianyun Su
    • 1
    • 2
    • 3
    Email author
  • Zhen Jia
    • 1
    • 3
  • Xinfang Li
    • 1
    • 3
  • Lin Zhou
    • 1
    • 3
  • Zhuanling Song
    • 1
    • 3
  1. 1.First Institute of Oceanography (FIO)Ministry of Natural Resources (MNR)QingdaoChina
  2. 2.Laboratory for Regional Oceanography and Numerical ModellingQingdao National Laboratory for Marine Science and TechnologyQingdaoChina
  3. 3.National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application TechnologyQingdaoChina

Personalised recommendations