Skip to main content
SpringerLink
Log in

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

  • Published:
Journal of Oceanology and Limnology Aims and scope Submit manuscript

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.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Data Availability Statement

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

References

  • 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 

  • 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 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Chapter  Google Scholar 

  • 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 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

Acknowledgement

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

Author information

Authors and Affiliations

  1. First Institute of Oceanography (FIO), Ministry of Natural Resources (MNR), Qingdao, 266061, China

    Zhendong Liu, Haixing Liu, Tianyun Su, Zhen Jia, Xinfang Li, Lin Zhou & Zhuanling Song

  2. Laboratory for Regional Oceanography and Numerical Modelling, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, China

    Haixing Liu & Tianyun Su

  3. National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Qingdao, 266061, China

    Zhendong Liu, Haixing Liu, Tianyun Su, Zhen Jia, Xinfang Li, Lin Zhou & Zhuanling Song

Authors
  1. Zhendong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haixing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tianyun Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhen Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xinfang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhuanling Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Haixing Liu or Tianyun Su.

Additional information

Supported by the National Key Research and Development Program of China (No. 2016YFC1402000)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Z., Liu, H., Su, T. et al. Dynamic visual simulation of marine vector field based on LIC—a case study of surface wave field in typhoon condition. J. Ocean. Limnol. 37, 2025–2036 (2019). https://doi.org/10.1007/s00343-019-8263-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00343-019-8263-1

Keywords

Search

Navigation