IEEE Trans Vis Comput Graph - Efficient Volume Exploration Using the Gaussian Mixture Model.

Tópicos

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Resumo

The multi-dimensional 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 datasets 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.The effectiveness of our approach is verified on several datasets.

Resumo Limpo

multidimension transfer function flexibl effect tool explor volum data howev design appropri transfer function trialanderror process remain challeng paper propos novel volum explor scheme explor volumetr structur featur space model space use gaussian mixtur model gmm new approach three distinct advantag first initi featur separ can automat achiev gmm estim second calcul gaussian can direct map set ellipt transfer function etf facilit fast preintegr volum render process third inexperienc user can flexibl manipul etf assist suit simpl widget discov potenti featur sever interact extend gmmbase explor scheme timevari dataset use increment gmm estim algorithm algorithm estim gmm one time step use gmm generat previous step sequenti appli increment algorithm time step select time interv yield preliminari classif time step addit comput etf can freeli adjust adjust automat propag time stepsth effect approach verifi sever dataset

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