Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models

Athawale, Tushar M.; Johnson, Chris R.; Sane, Sudhanshu; Pugmire, David

doi:10.1109/tvcg.2022.3209424

Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models

Journal Article · Mon Sep 26 00:00:00 EDT 2022 · IEEE Transactions on Visualization and Computer Graphics

DOI:https://doi.org/10.1109/tvcg.2022.3209424· OSTI ID:1991680

Athawale, Tushar M. ^[1]; ^[2]; Sane, Sudhanshu ^[3]; ^[1]

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Univ. of Utah, Salt Lake City, UT (United States). Scientific Computing and Imaging (SCI) Inst.
Luminary Cloud, Inc., Redwood City, CA (United States)

Visualization and analysis of multivariate data and their uncertainty are top research challenges in data visualization. Constructing fiber surfaces is a popular technique for multivariate data visualization that generalizes the idea of level-set visualization for univariate data to multivariate data. Here, in this paper, we present a statistical framework to quantify positional probabilities of fibers extracted from uncertain bivariate fields. Specifically, we extend the state-of-the-art Gaussian models of uncertainty for bivariate data to other parametric distributions (e.g., uniform and Epanechnikov) and more general nonparametric probability distributions (e.g., histograms and kernel density estimation) and derive corresponding spatial probabilities of fibers. In our proposed framework, we leverage Green's theorem for closed-form computation of fiber probabilities when bivariate data are assumed to have independent parametric and nonparametric noise. Additionally, we present a nonparametric approach combined with numerical integration to study the positional probability of fibers when bivariate data are assumed to have correlated noise. For uncertainty analysis, we visualize the derived probability volumes for fibers via volume rendering and extracting level sets based on probability thresholds. We present the utility of our proposed techniques via experiments on synthetic and simulation datasets.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC); National Institutes of Health (NIH)

Grant/Contract Number:: AC05-00OR22725; FE0031880

OSTI ID:: 1991680

Journal Information:: IEEE Transactions on Visualization and Computer Graphics, Journal Name: IEEE Transactions on Visualization and Computer Graphics Journal Issue: 1 Vol. 29; ISSN 1077-2626

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models

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