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

Ziyi Zheng, Nafees Ahmed, Klaus MuellerVisual Analytics and Imaging (VAI) Lab

Center of Visual ComputingStony 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 2008

1. User specify a 1D transfer function (TF) / segmentation 2. Algorithms automatic select good views3. 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 approach1. User specify a multi-dimensional feature descriptor2. Algorithms suggest promising views in dependent of TF3. User-interactive TF design4. 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

• Pipeline1. Multi-dimensional feature descriptor2. Multi-dimensional clustering3. Shading-based visibility test4. Updating navigation sphere5. 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 vi

• Based on the Chain Rule, to maximize means to maximize

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

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 1 View 7View 5 ……View 4View 3View 2

heuristic: number of additional visible clusters

3 5 2 90 1

Pheromone: other ants visited before

9 11415 20

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(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

Performance

Performance