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. Kelson 1 1 High Performance Computing
and Research Support, QUT 2 School of Electrical Engineering and
Computer Science, QUT
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CRICOS No. 00213J 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
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CRICOS No. 00213J a university for the world real R Vector
Field Visualisation Vectors are everywhere! A picture says a
thousand words.
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CRICOS No. 00213J a university for the world real R Traditional
Techniques We are all familiar with these: Arrow/Quiver plots.
Streamlines/Pathlines. Iso-surfaces. [1]
http://www.mathworks.com.au/help/matlab/ref/quiver.html [2]
http://www.mathworks.com.au/help/matlab/visualize/visualizing-vector-volume-data.html#f5-7374
[2] [1]
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CRICOS No. 00213J a university for the world real R Problems
for Complex Flows Visual Clutter Choice of seed points Difficult to
interpret time-dependent flows [3]
http://rgm2.lab.nig.ac.jp/RGM2/func.php?rd_id=CircSpatial:PlotVectors
[4] J. Ma et. Al. (2011). Streamline Selection and Viewpoint
Selection via Information Channel. IEEE VisWeek Poster 2011,
Providence, RI, Oct 2011. [4] [3]
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CRICOS No. 00213J 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
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CRICOS No. 00213J 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.
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CRICOS No. 00213J 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. 263-270.
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CRICOS No. 00213J 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.
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CRICOS No. 00213J 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.
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CRICOS No. 00213J 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.
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CRICOS No. 00213J a university for the world real R Traditional
Quiver Plot Animation
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CRICOS No. 00213J a university for the world real R Line
Integral Convolution Image
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CRICOS No. 00213J a university for the world real R Image Base
Flow Visualisation Animation
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CRICOS No. 00213J 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.
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CRICOS No. 00213J 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.
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CRICOS No. 00213J 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.
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CRICOS No. 00213J 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
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CRICOS No. 00213J 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)
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CRICOS No. 00213J a university for the world real R Future Work
Improve accessibility to researchers. Integrate into popular tools
such as MATLAB.
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CRICOS No. 00213J a university for the world real R Thank you!
Questions?