Glyphs for Visualizing Uncertainty in Vector Fields
IEEE Transactions on Visualization and Computer Graphics
Point-Based Probabilistic Surfaces to Show Surface Uncertainty
IEEE Transactions on Visualization and Computer Graphics
Display of Vector Fields Using a Reaction-Diffusion Model
VIS '04 Proceedings of the conference on Visualization '04
Visualizing Spatial Multivalue Data
IEEE Computer Graphics and Applications
Visualization of gridded scalar data with uncertainty in geosciences
Computers & Geosciences
Uncertain topology of 3D vector fields
PACIFICVIS '11 Proceedings of the 2011 IEEE Pacific Visualization Symposium
Positional Uncertainty of Isocontours: Condition Analysis and Probabilistic Measures
IEEE Transactions on Visualization and Computer Graphics
Flow Radar Glyphs—Static Visualization of Unsteady Flow with Uncertainty
IEEE Transactions on Visualization and Computer Graphics
Visualization of Global Correlation Structures in Uncertain 2D Scalar Fields
Computer Graphics Forum
Vortex Analysis in Uncertain Vector Fields
Computer Graphics Forum
Probabilistic Local Features in Uncertain Vector Fields with Spatial Correlation
Computer Graphics Forum
Flow Visualization with Quantified Spatial and Temporal Errors Using Edge Maps
IEEE Transactions on Visualization and Computer Graphics
Volume rendering data with uncertainty information
EGVISSYM'01 Proceedings of the 3rd Joint Eurographics - IEEE TCVG conference on Visualization
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Visualizing the positional and geometrical variability of isosurfaces in uncertain scalar fields
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Closed stream lines in uncertain vector fields
Proceedings of the 27th Spring Conference on Computer Graphics
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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An uncertain (scalar, vector, tensor) field is usually perceived as a discrete random field with a priori unknown probability distributions. To compute derived probabilities, e.g. for the occurrence of certain features, an appropriate probabilistic model has to be selected. The majority of previous approaches in uncertainty visualization were restricted to Gaussian fields. In this paper we extend these approaches to nonparametric models, which are much more flexible, as they can represent various types of distributions, including multimodal and skewed ones. We present three examples of nonparametric representations: (a) empirical distributions, (b) histograms and (c) kernel density estimates (KDE). While the first is a direct representation of the ensemble data, the latter two use reconstructed probability density functions of continuous random variables. For KDE we propose an approach to compute valid consistent marginal distributions and to efficiently capture correlations using a principal component transformation. Furthermore, we use automatic bandwidth selection, obtaining a model for probabilistic local feature extraction. The methods are demonstrated by computing probabilities of level crossings, critical points and vortex cores in simulated biofluid dynamics and climate data.