

FOLLOWUS
1.First Institute of Oceanography (FIO), Ministry of Natural Resources (MNR), Qingdao 266061, China
2.Laboratory for Regional Oceanography and Numerical Modelling, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
3.National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Qingdao 266061, China
LIU Haixing, E-mail:liuhx@fio.org.cn
SU Tianyun, E-mail:sutiany@fio.org.cn
收稿:2018-09-25,
录用:2018-12-30,
网络首发:2019-02-20,
纸质出版:2019-11
Scan QR Code
Dynamic visual simulation of marine vector field based on LIC-a case study of surface wave field in typhoon condition[J]. 海洋湖沼学报(英文), 2019,37(6):2025-2036.
Zhendong LIU, Haixing LIU, Tianyun SU, et al. Dynamic visual simulation of marine vector field based on LIC-a case study of surface wave field in typhoon condition[J]. Journal of Oceanology and Limnology, 2019, 37(6): 2025-2036.
Dynamic visual simulation of marine vector field based on LIC-a case study of surface wave field in typhoon condition[J]. 海洋湖沼学报(英文), 2019,37(6):2025-2036. DOI: 10.1007/s00343-019-8263-1.
Zhendong LIU, Haixing LIU, Tianyun SU, et al. Dynamic visual simulation of marine vector field based on LIC-a case study of surface wave field in typhoon condition[J]. Journal of Oceanology and Limnology, 2019, 37(6): 2025-2036. DOI: 10.1007/s00343-019-8263-1.
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.
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.
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 https://doi.org/10.1145/166117.166151 ..
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 https://doi.org/10.1109/PACIFICVIS.2015.7156352 ..
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.
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.
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 Neuroradiology, 27(3):263-273, https://doi.org/10.1007/s00062-015-0486-8.
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.
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 https://doi.org/10.1109/VISUAL.1996.567784 ..
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 https://doi.org/10.1109/ICME.2018.8486527 ..
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, colspan=""https://doi.org/10.1109/IGARSS.2016.7729408 https://doi.org/10.1109/IGARSS.2016.7729408 ..
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 https://doi.org/10.1109/IGARSS.2016.7729408 ..
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, http://dx.doi.org/10.2312/VisSym/VisSym02/043-052 http://dx.doi.org/10.2312/VisSym/VisSym02/043-052 ..
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.
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 https://doi.org/10.1145/3284557.3284709 ..
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 http://dx.doi.org/qhttps://doi.org/10.1109/SciVis.2015.7429490 ..
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 https://doi.org/10.1117/12.270314 ..
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.
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.
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 https://doi.org/10.1145/218380.218448 ..
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 https://doi.org/10.1109/VISUAL.2003.1250362 ..
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.
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 https://doi.org/10.1109/CA.1997.601035 ..
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 https://doi.org/10.1109/VISUAL.1997.663897 ..
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.
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.
0
浏览量
3
Downloads
2
CSCD
关联资源
相关文章
相关作者
相关机构

京公网安备11010802024621