IView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization Ziyi...
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![Page 1: IView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization Ziyi Zheng, Nafees Ahmed, Klaus Mueller Visual Analytics.](https://reader035.fdocuments.us/reader035/viewer/2022062515/56649cec5503460f949b8cb4/html5/thumbnails/1.jpg)
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 Computing
Stony Brook University
![Page 2: IView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization Ziyi Zheng, Nafees Ahmed, Klaus Mueller Visual Analytics.](https://reader035.fdocuments.us/reader035/viewer/2022062515/56649cec5503460f949b8cb4/html5/thumbnails/2.jpg)
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
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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 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, …)
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View Suggestion – Our Approach
• This paper: view suggestion approach1. 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
![Page 5: IView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization Ziyi Zheng, Nafees Ahmed, Klaus Mueller Visual Analytics.](https://reader035.fdocuments.us/reader035/viewer/2022062515/56649cec5503460f949b8cb4/html5/thumbnails/5.jpg)
View Suggestion – Our Approach
• Pipeline1. Multi-dimensional feature descriptor
2. Multi-dimensional clustering
3. Shading-based visibility test
4. Updating navigation sphere
5. Set-cover problem solver
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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
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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
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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
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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
![Page 10: IView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization Ziyi Zheng, Nafees Ahmed, Klaus Mueller Visual Analytics.](https://reader035.fdocuments.us/reader035/viewer/2022062515/56649cec5503460f949b8cb4/html5/thumbnails/10.jpg)
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
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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
![Page 12: IView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization Ziyi Zheng, Nafees Ahmed, Klaus Mueller Visual Analytics.](https://reader035.fdocuments.us/reader035/viewer/2022062515/56649cec5503460f949b8cb4/html5/thumbnails/12.jpg)
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.
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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
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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
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CSP Solver Case Study
• Tooth• Entropy• SCP
solver
give
7 views
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Some Test Cases
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Cube
• Entropy• SCP solver
4 views
![Page 18: IView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization Ziyi Zheng, Nafees Ahmed, Klaus Mueller Visual Analytics.](https://reader035.fdocuments.us/reader035/viewer/2022062515/56649cec5503460f949b8cb4/html5/thumbnails/18.jpg)
Cube with Text
• Entropy• SCP solver
5 views
![Page 19: IView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization Ziyi Zheng, Nafees Ahmed, Klaus Mueller Visual Analytics.](https://reader035.fdocuments.us/reader035/viewer/2022062515/56649cec5503460f949b8cb4/html5/thumbnails/19.jpg)
![Page 20: IView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization Ziyi Zheng, Nafees Ahmed, Klaus Mueller Visual Analytics.](https://reader035.fdocuments.us/reader035/viewer/2022062515/56649cec5503460f949b8cb4/html5/thumbnails/20.jpg)
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
![Page 21: IView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization Ziyi Zheng, Nafees Ahmed, Klaus Mueller Visual Analytics.](https://reader035.fdocuments.us/reader035/viewer/2022062515/56649cec5503460f949b8cb4/html5/thumbnails/21.jpg)
Conclusions
• Multi-dimensional feature clustering• Act before transfer function design• Progressive suggest a set of views• Providing optimal solutions by solve set-cover problem
![Page 22: IView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization Ziyi Zheng, Nafees Ahmed, Klaus Mueller Visual Analytics.](https://reader035.fdocuments.us/reader035/viewer/2022062515/56649cec5503460f949b8cb4/html5/thumbnails/22.jpg)
Future Work
• More feature descriptor • suggestive contours, multi-scale Harris Detector, SIFT
• Flow visualization• GPU-based ant colony algorithm
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THANKS
• Volume rendering engine• ImageVis3D, Tuvok
• Dataset providers• Colleagues• VAI lab, CVC lab
• Reviewers
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Q & A
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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
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Performance
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Performance