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Information-theoretic framework for flow visualization

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A summary of the Vis paper, "A Information-Theoretic Framework for Flow Visualization"

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Information-theoretic framework for flow visualization

1. 1. An Information-Theoretic Frameworkfor Flow VisualizationVis20102010/12/14ked
2. 2. Authors Lijie Xu Teng-Yok Lee Han-Wei Shen
3. 3. Flow visualization “Flow visualization is the art of making flowpatterns visible.” – wiki
4. 4. Vector field
5. 5. Streamline Streamlines are a family of curves that areinstantaneously tangent to the velocity vectorof the flow.
6. 6. Streamline Different streamlines do not intersect. because a fluid particle cannot have two differentvelocities at the same point.
7. 7. Streamline placement algorithmevenly-spacedseeding methodfarthest-pointseeding method
8. 8. Streamline placement algorithmevenly-spacedseeding methodfarthest-pointseeding method
9. 9. Information-aware streamline placement
10. 10. Information-aware streamline placement
11. 11. 1. Detect local maxima in the entropy field2. Discard points whose entropy are too small3. Place initial seeds The seed are distributed using diamond shapetemplateTemplate based seed selection
12. 12. Entropy field Shannon’s entropy: A histogram is create from vectors:
13. 13. Entropy field Shannon’s entropy: A histogram is create from vectors:5.79 5.822.42 4.36
14. 14. Entropy field
15. 15. Information-aware streamline placement
16. 16. Important-based seed sampling1. Compute conditional entropy, h(x, y)2. Place seeds in high conditional entropy
17. 17. Conditional entropy0.56
18. 18. Interpolation Streamline diffusion Generate a vector field Y(x) with respect to thefield that minimize the energy function
19. 19. Streamline diffusion
20. 20. Information-aware streamline placement
21. 21. Redundant streamline pruning Low entropy region: Fewer streamlines are needed Large distance threshold High entropy region: Smaller distance threshold If a streamline have a neighboring streamlinethat is closer than threshold, the streamline ispruned.
22. 22. 2D resultsinitial seeds 1stseeding resultconditionalentropy
23. 23. 2D resultsinitial seeds 1stseeding resultconditionalentropy
24. 24. 3D resultsinitial seeds 50 streamlines 200 streamlinesconditionalentropyentropy field
25. 25. 3D resultsinitial seeds 50 streamlines 200 streamlinesconditionalentropyentropy field
26. 26. 3D results
27. 27. Performancein seconds
28. 28. Limitations and feature work Entropy measures consider the statisticalproperties but not spatial distribution
29. 29. Limitations and feature work Entropy measures consider the statisticalproperties but not spatial distribution A region with high error magnitudes can stillhave a low conditional entropy
30. 30. Limitations and feature work Entropy measures consider the statisticalproperties but not spatial distribution A region with high error magnitudes can stillhave a low conditional entropy The magnitude of vectors are not considered
31. 31. Thx.