|Title||Local Data Models for Probabilistic Transfer Function Design
(In Proceedings) |
|in||Eurographics Conference on Visualization (EuroVis 2013) Short Papers|
Harald Obermaier, Kenneth I. Joy |
|Keyword(s)||Gaussian Mixture Models, Volume Rendering, Volume Classification, Interaction, Local Data, Probabilistic Classification|
A central component of expressive volume rendering is the identification of tissue or material types and their respective boundaries. To perform appropriate data classification, transfer functions can be defined in high-dimensional histograms, removing restrictions of purely 1D scalar value classification. The presented work aims at alleviating the problems of interactive multi-dimensional transfer function design by coupling high-dimensional, probabilistic, data-centric segmentation with interaction in the natural 3D space of the volume. We fit variable Gaussian Mixture Models to user specified subsets of the data set, yielding a probabilistic data model of the identified material type and its sources. The resulting classification allows for efficient transfer function design and multi-material volume rendering as demonstrated in several benchmark data sets.