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Queensland University of Technology CRICOS No. 00213J Visualisation of complex flows using texture-based techniques D. J. Warne 1,2, J. Young 1, N. A.

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Presentation on theme: "Queensland University of Technology CRICOS No. 00213J Visualisation of complex flows using texture-based techniques D. J. Warne 1,2, J. Young 1, N. A."— Presentation transcript:

1 Queensland University of Technology CRICOS No J Visualisation of complex flows using texture-based techniques D. J. Warne 1,2, J. Young 1, N. A. Kelson 1 1 High Performance Computing and Research Support, QUT 2 School of Electrical Engineering and Computer Science, QUT

2 CRICOS No J a university for the world real R Overview Background Vector Field Visualisation Traditional Techniques Problems for Complex Flows Advantages of Texture-Based Techniques Texture-Based Algorithms Line Integral Convolution Image Based Flow Visualisation Implementation and Application Visualisation Effectiveness Implementation Complexity Computational Aspects Conclusions

3 CRICOS No J a university for the world real R Vector Field Visualisation Vectors are everywhere! “A picture says a thousand words.”

4 CRICOS No J a university for the world real R Traditional Techniques We are all familiar with these: Arrow/Quiver plots. Streamlines/Pathlines. Iso-surfaces. [1] [2] [2] [1]

5 CRICOS No J a university for the world real R Problems for Complex Flows Visual Clutter Choice of seed points Difficult to interpret time-dependent flows [3] [4] J. Ma et. Al. (2011). Streamline Selection and Viewpoint Selection via Information Channel. IEEE VisWeek Poster 2011, Providence, RI, Oct [4] [3]

6 CRICOS No J a university for the world real R Texture-Based Techniques Warp an image by the underlying field Advantages Global/local flow regimes visible No issues with seed points Easily extend to capture time dependent features

7 CRICOS No J a university for the world real R Line-Integral Convolution (LIC) Applies a convolution along streamlines. The final image at point p is the result of a convolution of the kernel k(x) with noise along the streamline s(x,p,t) = p at x = t.

8 CRICOS No J a university for the world real R Line-Integral Convolution (LIC) [4] B. Cabral, and C. Leedom (1993). Imaging vector fields using line integral convolution. SIGGRAPH 93, pp

9 CRICOS No J a university for the world real R Image Based Flow Visualisation (IBFV) Basic extension of LIC. Here, I(x,t) is now a noise image modulated in time. We convolve over a pathline P(x,p,t) rather than streamline.

10 CRICOS No J a university for the world real R Image Based Flow Visualisation (IBFV) [5] A. Telea (2008). Data Visualization: Principles and practice. Wellesley, MA : A K Peters, Ltd, 2008.

11 CRICOS No J a university for the world real R Case Study: Variable-density flow through porous media Aquifer 600m x 200m fully saturated with fresh water. Sitting on top, a region of more dense salt water. Salt water sinks into the aquifer. Causes complex up-welling and down-welling flows.

12 CRICOS No J a university for the world real R Traditional Quiver Plot Animation

13 CRICOS No J a university for the world real R Line Integral Convolution Image

14 CRICOS No J a university for the world real R Image Base Flow Visualisation Animation

15 CRICOS No J a university for the world real R Visualisation Effectiveness (LIC) LIC Strengths Dense Coverage. Spatial Correlation. Clearly identifies extrema. Weaknesses No indicators of direction. No indicators of magnitude. Only applicable for steady-state flows.

16 CRICOS No J a university for the world real R Visualisation Effectiveness (IBFV) IBFV Strengths Dense Coverage. Spatial/Temporal Correlation. Clearly identifies extrema. Identifies motion of extrema. Strong visual cues for flow direction and magnitude. Weaknesses Requires animation. Care is needed to correctly set texture speeds.

17 CRICOS No J a university for the world real R Implementation Comparison LIC Algorithm 1. For each pixel 1.1 Compute forward streamline. 1.2 Compute backward streamline. 1.3 Sum pixel intensities 1.4 Divide by the length 1.5 Assign result to output pixel. IBFV Algorithm 1. Warp mesh by field 2. Render with previous texture 3. Overlay next noise texture and blend 4. Copy buffer.

18 CRICOS No J a university for the world real R Extensions to IBFV Easily extends to advection of multiple textures Scalar data overlays. moviemovie Dye injects (particle traces, similar to streaklines). movie movie Jittered Grid (similar to quiver plot overlay). moviemovie Timelines. moviemovie

19 CRICOS No J a university for the world real R Computational Aspects CPU based LIC can be expensive. Need to implement interpolation. Streamline tracing for every pixel. IBFV naturally implemented on GPU Hardware handles interpolation Convolution is written in terms of blending functions Only mesh nodes need be intergrated LIC  IBFV with I(x,t) = I(x)

20 CRICOS No J a university for the world real R Future Work Improve accessibility to researchers. Integrate into popular tools such as MATLAB.

21 CRICOS No J a university for the world real R Thank you! Questions?


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