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

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

View Selection – Previous Methods View selection approach Bordoloi 2005,Takahashi 2005,Chan User specify a 1D transfer function (TF) / segmentation 2.Algorithms automatic select good views 3.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, …)

View Suggestion – Our Approach This paper: view suggestion approach 1.User specify a multi-dimensional feature descriptor 2.Algorithms suggest promising views in dependent of TF 3.User-interactive TF design 4.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

View Suggestion – Our Approach Pipeline 1.Multi-dimensional feature descriptor 2.Multi-dimensional clustering 3.Shading-based visibility test 4.Updating navigation sphere 5.Set-cover problem solver

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

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

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

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

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 v i Based on the Chain Rule, to maximize means to maximize

Cluster-Based Entropy View entropy for a certain view is: VC j (v i ) is the visibility of cluster j in view i is the noteworthiness of cluster j, is defined as: p j represents the probability of cluster j n j is the number of cluster j

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.

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

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 View 1View 7View 5 ……View 4View 3View 2 heuristic: number of additional visible clusters Pheromone: other ants visited before

CSP Solver Case Study Tooth Entropy SCP solver give 7 views

Some Test Cases

Cube Entropy SCP solver 4 views

Cube with Text Entropy SCP solver 5 views

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

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

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

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

Q & A

Motivation Volume data visualization Map 3D data into a 2D image Transfer-Function Exploration RGBA + 1D transfer-function O(n 4 ) space RGBA + 2D transfer-function O(n 8 ) space Viewpoint Exploration O(n 2 ) space Totally O(n 6 ~n 8 ) space Challenging task for non-expert user

Performance