Efficient Volume Exploration Using the Gaussian Mixture Model

The multidimensional transfer function is a flexible and effective tool for exploring volume data. However, designing an appropriate transfer function is a trial-and-error process and remains a challenge. In this paper, we propose a novel volume exploration scheme that explores volumetric structures...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2011-11, Vol.17 (11), p.1560-1573
Main Authors: Wang, Yunhai, Chen, Wei, Zhang, Jian, Dong, Tingxing, Shan, Guihua, Chi, Xuebin
Format: Article
Language:English
Subjects:
Adjustment
Aerospace electronics
Algorithms
Estimation
Exploration
Feature extraction
Gaussian
Gaussian mixture model
Histograms
Intervals
Rendering (computer graphics)
Solid modeling
Studies
temporal coherence
time-varying data
Transfer functions
Volume classification
volume rendering
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Summary:The multidimensional transfer function is a flexible and effective tool for exploring volume data. However, designing an appropriate transfer function is a trial-and-error process and remains a challenge. In this paper, we propose a novel volume exploration scheme that explores volumetric structures in the feature space by modeling the space using the Gaussian mixture model (GMM). Our new approach has three distinctive advantages. First, an initial feature separation can be automatically achieved through GMM estimation. Second, the calculated Gaussians can be directly mapped to a set of elliptical transfer functions (ETFs), facilitating a fast pre-integrated volume rendering process. Third, an inexperienced user can flexibly manipulate the ETFs with the assistance of a suite of simple widgets, and discover potential features with several interactions. We further extend the GMM-based exploration scheme to time-varying data sets using an incremental GMM estimation algorithm. The algorithm estimates the GMM for one time step by using itself and the GMM generated from its previous steps. Sequentially applying the incremental algorithm to all time steps in a selected time interval yields a preliminary classification for each time step. In addition, the computed ETFs can be freely adjusted. The adjustments are then automatically propagated to other time steps. In this way, coherent user-guided exploration of a given time interval is achieved. Our GPU implementation demonstrates interactive performance and good scalability. The effectiveness of our approach is verified on several data sets.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2011.97