Visualizing spatial distribution data sets
VISSYM '03 Proceedings of the symposium on Data visualisation 2003
Detecting critical regions in scalar fields
VISSYM '03 Proceedings of the symposium on Data visualisation 2003
Point-Based Probabilistic Surfaces to Show Surface Uncertainty
IEEE Transactions on Visualization and Computer Graphics
Uncertainty Visualization in Medical Volume Rendering Using Probabilistic Animation
IEEE Transactions on Visualization and Computer Graphics
Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Visualization of gridded scalar data with uncertainty in geosciences
Computers & Geosciences
Uncertainty-Aware Guided Volume Segmentation
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Noodles: A Tool for Visualization of Numerical Weather Model Ensemble Uncertainty
IEEE Transactions on Visualization and Computer Graphics
Matching Visual Saliency to Confidence in Plots of Uncertain Data
IEEE Transactions on Visualization and Computer Graphics
Positional Uncertainty of Isocontours: Condition Analysis and Probabilistic Measures
IEEE Transactions on Visualization and Computer Graphics
Uncertainty and variability in point cloud surface data
SPBG'04 Proceedings of the First Eurographics conference on Point-Based Graphics
Visualization of Global Correlation Structures in Uncertain 2D Scalar Fields
Computer Graphics Forum
Nonparametric models for uncertainty visualization
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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In this paper we revisit the computation and visualization of equivalents to isocontours in uncertain scalar fields. We model uncertainty by discrete random fields and, in contrast to previous methods, also take arbitrary spatial correlations into account. Starting with joint distributions of the random variables associated to the sample locations, we compute level crossing probabilities for cells of the sample grid. This corresponds to computing the probabilities that the well-known symmetry-reduced marching cubes cases occur in random field realizations. For Gaussian random fields, only marginal density functions that correspond to the vertices of the considered cell need to be integrated. We compute the integrals for each cell in the sample grid using a Monte Carlo method. The probabilistic ansatz does not suffer from degenerate cases that usually require case distinctions and solutions of ill-conditioned problems. Applications in 2D and 3D, both to synthetic and real data from ensemble simulations in climate research, illustrate the influence of spatial correlations on the spatial distribution of uncertain isocontours.