UFLOW: visualizing uncertainty in fluid flow
Proceedings of the 7th conference on Visualization '96
Glyphs for Visualizing Uncertainty in Vector Fields
IEEE Transactions on Visualization and Computer Graphics
A Next Step: Visualizing Errors and Uncertainty
IEEE Computer Graphics and Applications
Display of Vector Fields Using a Reaction-Diffusion Model
VIS '04 Proceedings of the conference on Visualization '04
Tracking of Vector Field Singularities in Unstructured 3D Time-Dependent Datasets
VIS '04 Proceedings of the conference on Visualization '04
Vortex Visualization for Practical Engineering Applications
IEEE Transactions on Visualization and Computer Graphics
Probabilistic 4D blood flow mapping
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Uncertain topology of 3D vector fields
PACIFICVIS '11 Proceedings of the 2011 IEEE Pacific Visualization Symposium
Flow Radar Glyphs—Static Visualization of Unsteady Flow with Uncertainty
IEEE Transactions on Visualization and Computer Graphics
Flow Visualization with Quantified Spatial and Temporal Errors Using Edge Maps
IEEE Transactions on Visualization and Computer Graphics
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
Nonparametric models for uncertainty visualization
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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In this paper methods for extraction of local features in crisp vector fields are extended to uncertain fields. While in a crisp field local features are either present or absent at some location, in an uncertain field they are present with some probability. We model sampled uncertain vector fields by discrete Gaussian random fields with empirically estimated spatial correlations. The variability of the random fields in a spatial neighborhood is characterized by marginal distributions. Probabilities for the presence of local features are formulated in terms of low-dimensional integrals over such marginal distributions. Specifically, we define probabilistic equivalents for critical points and vortex cores. The probabilities are computed by Monte Carlo integration. For identification of critical points and cores of swirling motion we employ the Poincaré index and the criterion by Sujudi and Haimes. In contrast to previous global methods we take a local perspective and directly extract features in divergence-free fields as well. The method is able to detect saddle points in a straight forward way and works on various grid types. It is demonstrated by applying it to simulated unsteady flows of biofluid and climate dynamics. © 2012 Wiley Periodicals, Inc.