iView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization

1. iView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization Ziyi Zheng, Nafees Ahmed, Klaus Mueller Visual Analytics and Imaging (VAI) Lab Center of Visual Computing Stony Brook University

2. Outline • Objective: suggesting interesting views in volume rendering • Interactive exploration of transfer functions • Approach • Multi-dimensional clustering & cluster-based entropy • Set-cover problem solver • Results • Case study & user study • Conclusions

3. View Selection – Previous Methods • View selection approach Bordoloi 2005,Takahashi 2005,Chan 2008 • User specify a 1D transfer function (TF) / segmentation • Algorithms automatic select good views • User repeat 1 if needed • Potential pitfalls • Long waiting time if change 1D TF / segmentation (re-run step 2) Restricted TF / segmentation exploration • Can not capture high-dimensional features. Do not support 2D TF. • Difficult to adapt to recently-developed high dimensional/ advanced TF (size-based, occlusion-based, visibility-based, …)

4. View Suggestion – Our Approach • This paper: view suggestion approach • User specify a multi-dimensional feature descriptor • Algorithms suggest promising views in dependent of TF • User-interactive TF design • Repeat 1,2 if needed • Advantages • Suggest interesting views before transfer-function design. Remove the burden of rendering TF. Enable multiple TFs for multiple images. Support advanced TFs • Fully support user interactive exploration • Further improvement: progressively suggest a set of views. Automatic suggest optimal views by solving the set-cover problem

5. View Suggestion – Our Approach • Pipeline • Multi-dimensional feature descriptor • Multi-dimensional clustering • Shading-based visibility test • Updating navigation sphere • Set-cover problem solver

6. Feature Descriptor • Normal perturbation • Similar to a 3D Laplacian filter • Other feature descriptor can be readily applied according to user’s preference • Threshold need be applied before to remove noise • User can interactively validate this step and refine it

7. Multi-Dimensional Clustering • K-Means clustering algorithm • GPU-Accelerated • A parameter to extract multi-resolution features • Larger K, features with coarser resolution • Smaller K, features with finer resolution • User can specify K is given by a slider and look at the clusters

8. Clustering Results with Cluster-Gradient • Each cluster stores its mean gradient • Gradients / Normals are used later in visibility test Clusters of a cube Clusters of a cube with text

9. Visibility Test • Eye-ray vs normal angle • Eye-ray is facing normal good • Eye-ray is perpendicular to normal  not good • Visibility independent of TF  only depend on shading • 45 degree as shading effect criteria

10. Viewing Quality: Information Theory • Entropy • Measure the diversity/uncertainty of a signal • Volume rendering adaptation • Signal X is the volume which is unknown to receiver (user) • User get understanding the signal, then reduce the remaining entropy (uncertainty) after one view vi • Based on the Chain Rule, to maximize means to maximize

11. Cluster-Based Entropy • View entropy for a certain view is: • VCj(vi) is the visibility of cluster j in view i • is the noteworthiness of cluster j, is defined as: • pj represents the probability of cluster j • nj is the number of cluster j

12. User Interaction • Color mapping the entropy • A 2D global map and a track ball • Red: potentially more interesting view positions • Green: less interesting information • Blue: no interesting information • Entropy map guide user to promising view • User interaction • Parameterize the camera position on a sphere • The center of the sphere facing user is the current camera position. Rotate the sphere will rotate the viewing camera accordingly.

13. User Interaction: Progressive Updating • Progressively mark the region has been visited • We do not normalize the color mapping during the exploration, in order to see color fading from red to blue

14. Suggesting Best Combination of Views • Set-cover problem (SCP) formulation • clusters are elements and views are sets • minimum number of views cover all clusters • minimum number of sets cover all elements • Ant colony optimization for SCP • each virtual ant find a solution using greedy heuristic • each virtual ant deposit pheromone on its solution • each virtual ant make choice base on • previous ant’s pheromone • greedy heuristic • Russian roulette 4 9 5 20 1 11 View 1 View 2 View 3 View 4 View 5 …… View 7 3 0 5 2 9 1 heuristic: number of additional visible clusters Pheromone: other ants visited before

15. CSP Solver Case Study • Tooth • Entropy • SCP solver give 7 views

16. Some Test Cases

17. Cube • Entropy • SCP solver 4 views

18. Cube with Text • Entropy • SCP solver 5 views

20. User Study • Comparison between with and without view suggestion tool • Dataset: tooth and carp • User pick fewer views without navigation tool • With navigation tool, user show optimized view positions

21. Conclusions • Multi-dimensional feature clustering • Act before transfer function design • Progressive suggest a set of views • Providing optimal solutions by solve set-cover problem

22. Future Work • More feature descriptor • suggestive contours, multi-scale Harris Detector, SIFT • Flow visualization • GPU-based ant colony algorithm

23. THANKS • Volume rendering engine • ImageVis3D, Tuvok • Dataset providers • Colleagues • VAI lab, CVC lab • Reviewers

24. Q & A

25. Motivation • Volume data visualization • Map 3D data into a 2D image • Transfer-Function Exploration • RGBA + 1D transfer-function O(n4) space • RGBA + 2D transfer-function O(n8) space • Viewpoint Exploration • O(n2) space • Totally O(n6~n8) space • Challenging task for non-expert user

26. Performance

27. Performance