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Meeting Minutes

Meeting Minutes. T.-Y. Lee 01/25/2008. Overview. Previous TO-DO’s New progress TSI vs. DTW Create feature vector field and visualize it by streamlines Try distance field of multiple features Hunting for datasets Future Work. Previous TO-DO’s. Feature vector field Multi-resolution

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Meeting Minutes

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  1. Meeting Minutes T.-Y. Lee 01/25/2008

  2. Overview • Previous TO-DO’s • New progress • TSI vs. DTW • Create feature vector field and visualize it by streamlines • Try distance field of multiple features • Hunting for datasets • Future Work

  3. Previous TO-DO’s • Feature vector field • Multi-resolution • Interpolation among TACs • Put more streamline seeds (done) • Distance field • Try different TACs/Featrues (done) • Smooth the distance field (done) • Dataset • Check Vis’04 Contest’s dataset (done) • Ask Jon and Thomas (in progress) • Survey • Compare w/ Joshi and Rheingans’ illustrative visualization in VIS’05

  4. TSI vs. DTW (1/2) • Cons of TSI • TSI cannot handle TAC whose feature’s magnitude is smaller than non-interested parts • So DTW replaced the TSI • Result: (see next slide) • Pros: DTW is more stable than TSI • Cons: too slow • 3 minutes are taken on MATLAB while TSI only requires less than 1 minute

  5. TSI vs. DTW (2/2) TSI DTW

  6. Feature Vector Field (1/4) • Convert the original dataset into a vector field based on distance to neighboring pixels • Test: • A Gaussian dist. was move along the diagonal • 2 vector fields are created • Gradient: least-similar direction • Anti-gradient: most-similar direction • Different speed were tested • Result • The field and the streamlines are shown in next 2 slides

  7. Feature Vector Field (2/4) Result: Gradient Field

  8. Feature Vector Field (3/4) Result: Anti-Gradient Field

  9. Feature Vector Field (4/4) Discussion • After apply DTW, a more continuous gradient/anti-gradient is obtained • Longer streamline is therefore created • But the streamlines are hard to be interpreted

  10. Contour in Distance Field (1/4) • Visualization of datasets that contains 2 different features • Test-1 • A feature’s magnitude is positive while that of the other is negative • Two features are moving • Test-2 • A feature’s TACs are increasing while the other’s TACs are decreasing

  11. Contour in Distance Field (2/4) Test – 1: Result 2 different features are in the same dataset 2 reference TACs are chosen to construct the distance field.

  12. Contour in Distance Field (3/4) Test – 2: Result Features stay in the same locations with different trends

  13. Contour in Distance Field (3/4) Discussion • Test – 1: • The region w/ similar features were successfully segmented • Test – 2: • The feature is not moved but only changed at the same location, then contour line itself is not enough • A solution could be that a temporal transfer function is created in order to capture the change of magnitude other than chance of pattern

  14. Hunting for Datasets • Check Vis’04 Contest’s dataset • I mirrored the pages and documents of Vis’04 Contest on ATLAS http://atlas/WWW/viscontest/2004/ • Ask Jon and Thomas • I already contacted w/ Thomas

  15. Future Works • Short term goals • Combine the contour lines with color to visualize both spatial coherence and temporal evolution • Test other datasets • Long term goals • Compare this algorithm with Joshi and Rheingans’ method* • Decide the system architecture before Feb. and implement the system on C/OpenGL for faster interaction • Paper writing * Joshi and Rheingans,Illustration-inspired Techniques for Visualizing Time-varying Data, VIS’05

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