Issue No. 11 - November (2011 vol. 17)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2011.97
Yunhai Wang , Chinese Academy of Sciences, Beijing
Wei Chen , Zhejiang University, Hangzhou
Jian Zhang , Chinese Academy of Sciences, Beijing
Tingxing Dong , The University of Tennessee, Knoxville
Guihua Shan , Chinese Academy of Sciences, Beijing
Xuebin Chi , Chinese Academy of Sciences, Beijing
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.
Volume classification, volume rendering, Gaussian mixture model, time-varying data, temporal coherence.
G. Shan, X. Chi, J. Zhang, Y. Wang, T. Dong and W. Chen, "Efficient Volume Exploration Using the Gaussian Mixture Model," in IEEE Transactions on Visualization & Computer Graphics, vol. 17, no. , pp. 1560-1573, 2011.