Publicou 6 edições por ano
ISSN Imprimir: 2152-5080
ISSN On-line: 2152-5099
Indexed in
AN UNCERTAINTY VISUALIZATION TECHNIQUE USING POSSIBILITY THEORY: POSSIBILISTIC MARCHING CUBES
RESUMO
This paper opens the discussion about using fuzzy measure theory for isocontour/isosurface extraction in the field of uncertainty visualization. Specifically, we propose an uncertain marching cubes algorithm in the framework of possibility theory, called possibilistic marching cubes. The proposed algorithm uses the dual measures−possibility and necessity−to represent the uncertainty in the spatial location of isocontour/isosurface, which is propagated from the uncertainty in ensemble data. In addition, a novel parametric way of constructing marginal possibility distribution is proposed so that the epistemic uncertainty due to the limited size of the ensemble is considered. The effectiveness of the proposed possibilistic marching cubes algorithm is demonstrated using 2D and 3D examples.
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Fullér Robert, Harmati István Á., On Possibilistic Dependencies: A Short Survey of Recent Developments, in Soft Computing Based Optimization and Decision Models, 360, 2018. Crossref
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Gillmann Christina, Wischgoll Thomas, Hamann Bernd, Hagen Hans, Accurate and reliable extraction of surfaces from image data using a multi-dimensional uncertainty model, Graphical Models, 99, 2018. Crossref
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Harmati István Á., Fullér Robert, On the Lower Limit for Possibilistic Correlation Coefficient with Identical Marginal Possibility Distributions, in Interactions Between Computational Intelligence and Mathematics Part 2, 794, 2019. Crossref
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Gillmann Christina, Wischgoll Thomas, Hamann Bernd, Ahrens James, Modeling and Visualization of Uncertainty-Aware Geometry Using Multi-variate Normal Distributions, 2018 IEEE Pacific Visualization Symposium (PacificVis), 2018. Crossref
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Dutta Soumya, Shen Han-Wei, Chen Jen-Ping, In Situ Prediction Driven Feature Analysis in Jet Engine Simulations, 2018 IEEE Pacific Visualization Symposium (PacificVis), 2018. Crossref
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Gillmann Christina, Saur Dorothee, Wischgoll Thomas, Scheuermann Gerik, Uncertainty‐aware Visualization in Medical Imaging ‐ A Survey, Computer Graphics Forum, 40, 3, 2021. Crossref