1 / 15

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

zuriel
Télécharger la présentation

Meeting Minutes

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related