1. “Occlusion” Prepared by: Shreya Rawal 1

2. Extending Distortion Viewing from 2D to 3D S. Carpendale, D. J. Cowperthwaite and F. David Fracchia (1997) 2

3. What after developing visualization?  Exploration  Navigation  Interpretation of data  We will be applying techniques which are used in 2D into 3D for exploration/navigation/interpretation. 3

4. Various viewing techniques for 3D data  Viewing angle (rotation)  Viewing position (navigation)  Combination of the two 4

5. Problems associated  Loss of context  Loss of orientation  “Occlusion” 5

6. What is detail-in-context distortion?  You provide details but keep the context intact.  Distortion: Spatial reorganization of an existing representation  Main aim is to minimize occlusion  Applied with Magnification + Displacement  160 Nodes 6

7. Two – dimensional distortion patterns  Stretch orthogonal  Nonlinear orthogonal  Nonlinear radial  Step orthogonal 7

8. Stretch orthogonal Stretching all data on either of the two axes centered a the focus. Compressing the remaining areas uniformly.  2D Displacement + Magnification 8

9. Nonlinear orthogonal Focus is magnified to requested amount. Magnification decreases according to some function. Disadvantages: Limits the magnification in focal region Causes more extreme compression at the edges  2D Displacement + Magnification 9

10. Nonlinear radial Adjacent edges curve away from the focus. Outer rows of the grid is hardly affected.  2D Displacement + Magnification 10

11. Step Orthogonal Data is aligned with the focus unstretched. Less data distortion. Disadvantage: Leaves unused space. Causes grouping of the data.  2D Displacement + Magnification 11

12. Displacement + Magnification  2D  3D 12

13. 2D Displacement + Magnification 2D only Displacement Magnification + Displacement vs. Displacement only in 2D In 2D: Magnification + Displacement has the same effect as Displacement only 13 Stretch Non-Linear Non-linear Step orthogonal orthogonal Radial Orthogonal

14. Magnification + Displacement vs. Displacement only in 3D  Magnification + Displacement  Only- Displacement In 3D: Displacement only had better effects than Magnification + Displacement 14 Stretch Non-Linear Non-linear Step orthogonal orthogonal Radial Orthogonal

15. Visual Access Distortion  Naïve 2D 3D extension still does not solve Occlusion problem completely  Solution  move geometry according to viewpoint  magnify focus only  displace items in a different way (curves vs. straight lines)  Focus + context approach 15

16. Visual Access Distortion 16

17. Single Focus 17

18. Multiple Foci 18

19. Randomly positioned nodes:  Close to real data. 19

20. EdgeLens: An interactive Method for Managing Edge Congestion in Graphs N. Wong, S. Carpendale, S. Greenberg (2003) 20

21. Problems in Graph representation  When dealing with complex and large real world dataset  Many interconnected nodes leads to Edge-congestion  Edge-congestion results in:  obscuring nodes  obscuring individual edges  obscuring visual information 21

22. Managing edge layout 1. Edge density 2. Crossovers 3. Occlusion Airline routes from NorthWest Airlines, November, 2001 22

23. Edge congestion problem  Although position of node add value to visualization they introduce ambiguity (edge occlusion). A simple 3 node graph 23 Possible interpretations

24. Solutions: Edge congestion problem  Layout  Position of nodes have importance.  Curving edges globally 24

25. Solutions: Edge congestion problem  Filtering  Removing unimportant edges  only works where we can distinguish between important and unimportant edges.  you loose the relation of one edge with other edges 25

26. Solutions: Edge congestion problem  Magnification: 26

27. EdgeLens: An interactive technique  It moves edges without detaching it from node  Use displacement only  Respects the semantics of node layout.  Disambiguates edge overlapping  Disambiguates node overlapping  Clarifies details about graph structure 27

28. Two EdgeLens approaches  Bubble Vs Spline a) Bubbleb) Spline 28

29. User Study  16 participants  Task: 8 route finding task (easy, medium-easy, medium and hard)  Post session Questionnaire  Data:  nodes: Canadian cities  edges: Airline routes  Result:  Spline turned out to be better 29

30. Algorithm Decide which edges affected Calculate displacements Calculate spline control points (c 1, c 2 ) Draw curves 30 Curved Edge Original position of edge

31. Features and Demo  Video 31

32. Discussion  Scalability of multiple focus points for technique discussed in 1 st paper (distortion viewing) as compared to EdgeLens.  Distortion viewing (in 1 st paper) can be applied to all kinds of 3D visualizations.  Can Occlusion be completely avoided in 3D?  Deal Occlusion or Get rid of Occlusion?  Detail in context!! (Bubble vs. Spline) 32

33. References  S. Carpendale, D.J. Cowperthwaite, F. David Fracchia. Extending Distortion Viewing from 2D to 3D. IEEE Computer Graphics and Applications, 17(4), pp. 42-51, July / August 1997.  Nelson Wong, Sheelagh Carpendale and Saul Greenberg. EdgeLens: An Interactive Method for Managing Edge Congestion in Graphs. In Proceedings of IEEE Symposium on Information Visualization (InfoVis 2003). IEEE Press, pages 51-58, 2003  http://innovis.cpsc.ucalgary.ca/Research/EdgeLens  http://www.cs.ubc.ca/~tmm/courses/cpsc533c-06-fall/slides/depth- 4x4.pdf 33

34. My Project:  Erlang trace data:  nodes: processes  edges: interaction between processes (message sending and spawning)  Position of nodes does not have any significance  Hence concept of EdgeLens might not be applicable  Yes, node occlusion and edge congestion is an issue 34