Доступ предоставлен для: Guest
International Journal for Uncertainty Quantification
Главный редактор: Habib N. Najm (open in a new tab)
Ассоциированный редакторs: Dongbin Xiu (open in a new tab) Tao Zhou (open in a new tab)
Редактор-основатель: Nicholas Zabaras (open in a new tab)

Выходит 6 номеров в год

ISSN Печать: 2152-5080

ISSN Онлайн: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

A CONTOUR TREE BASED VISUALIZATION FOR EXPLORING DATA WITH UNCERTAINTY

Том 3, Выпуск 3, 2013, pp. 203-223
DOI: 10.1615/Int.J.UncertaintyQuantification.2012003956
Get accessDownload

Краткое описание

Uncertainty is a common and crucial issue in scientific data. The exploration and analysis of three-dimensional (3D) and large two-dimensional (2D) data with uncertainty information demand an effective visualization augmented with both user interaction and relevant context. The contour tree has been exploited as an efficient data structure to guide exploratory visualization. This paper proposes an interactive visualization tool for exploring data with quantitative uncertainty representations. First, we introduce a balanced planar hierarchical contour tree layout integrated with tree view interaction, allowing users to quickly navigate between levels of detail for contours of large data. Further, uncertainty information is attached to a planar contour tree layout to avoid the visual cluttering and occlusion in viewing uncertainty in 3D data or large 2D data. For the first time, the uncertainty information is explored as a combination of the data-level uncertainty which represents the uncertainty concerning the numerical values of the data, the contour variability which quantifies the positional variation of contours, and the topology variability which reveals the topological variation of contour trees. This information provides a new insight into how the uncertainty exists with and relates to the features of the data. The experimental results show that this new visualization facilitates a quick and accurate selection of prominent contours with high or low uncertainty and variability.

ЦИТИРОВАНО В
  1. Wu Keqin, Zhang Song, Feature-Based Uncertainty Visualization, in Innovative Approaches of Data Visualization and Visual Analytics, 2014. Crossref

  2. Günther David, Salmon Joseph, Tierny Julien, Mandatory Critical Points of 2D Uncertain Scalar Fields, Computer Graphics Forum, 33, 3, 2014. Crossref

  3. Mihai Mihaela, Westermann Rüdiger, Visualizing the stability of critical points in uncertain scalar fields, Computers & Graphics, 41, 2014. Crossref

  4. Demir Ismail, Kehrer Johannes, Westermann Rudiger, Screen-space silhouettes for visualizing ensembles of 3D isosurfaces, 2016 IEEE Pacific Visualization Symposium (PacificVis), 2016. Crossref

  5. Wolf Gert W., Scale independent surface characterisation: Geography meets precision surface metrology, Precision Engineering, 49, 2017. Crossref

  6. Liebmann T., Scheuermann G., Critical Points of Gaussian-Distributed Scalar Fields on Simplicial Grids, Computer Graphics Forum, 35, 3, 2016. Crossref

  7. Wu Keqin, Zhang Song, Feature-Based Uncertainty Visualization, in Big Data, 2016. Crossref

  8. Heine C., Leitte H., Hlawitschka M., Iuricich F., De Floriani L., Scheuermann G., Hagen H., Garth C., A Survey of Topology-based Methods in Visualization, Computer Graphics Forum, 35, 3, 2016. Crossref

  9. Yan Lin, Wang Yusu, Munch Elizabeth, Gasparovic Ellen, Wang Bei, A Structural Average of Labeled Merge Trees for Uncertainty Visualization, IEEE Transactions on Visualization and Computer Graphics, 26, 1, 2020. Crossref

  10. Vidal Jules, Budin Joseph, Tierny Julien, Progressive Wasserstein Barycenters of Persistence Diagrams, IEEE Transactions on Visualization and Computer Graphics, 2019. Crossref

  11. Heine Christian, Garth Christoph, Topological Subdivision Graphs for Comparative and Multifield Visualization, in Topological Methods in Data Analysis and Visualization V, 2020. Crossref

  12. Lohfink Anna‐Pia, Wetzels Florian, Lukasczyk Jonas, Weber Gunther H., Garth Christoph, Fuzzy Contour Trees: Alignment and Joint Layout of Multiple Contour Trees, Computer Graphics Forum, 39, 3, 2020. Crossref

  13. Bujack Roxana, Yan Lin, Hotz Ingrid, Garth Christoph, Wang Bei, State of the Art in Time‐Dependent Flow Topology: Interpreting Physical Meaningfulness Through Mathematical Properties, Computer Graphics Forum, 39, 3, 2020. Crossref

  14. Wolf Gert W, Surfaces—topography and topology, Surface Topography: Metrology and Properties, 8, 1, 2020. Crossref

  15. Athawale Tushar M., Maljovec Dan, Yan Lin, Johnson Chris R., Pascucci Valerio, Wang Bei, Uncertainty Visualization of 2D Morse Complex Ensembles Using Statistical Summary Maps, IEEE Transactions on Visualization and Computer Graphics, 28, 4, 2022. Crossref

  16. Pont Mathieu, Vidal Jules, Delon Julie, Tierny Julien, Wasserstein Distances, Geodesics and Barycenters of Merge Trees, IEEE Transactions on Visualization and Computer Graphics, 28, 1, 2022. Crossref

  17. Evers Marina, Herick Maria, Molchanov Vladimir, Linsen Lars, Coherent Topological Landscapes for Simulation Ensembles, in Computer Vision, Imaging and Computer Graphics Theory and Applications, 1474, 2022. Crossref

  18. Athawale Tushar, Johnson Chris R., Probabilistic Asymptotic Decider for Topological Ambiguity Resolution in Level-Set Extraction for Uncertain 2D Data, IEEE Transactions on Visualization and Computer Graphics, 25, 1, 2019. Crossref

  19. Wetzels Florian, Leitte Heike, Garth Christoph, Branch Decomposition‐Independent Edit Distances for Merge Trees, Computer Graphics Forum, 41, 3, 2022. Crossref

  20. Yan Lin, Masood Talha Bin, Sridharamurthy Raghavendra, Rasheed Farhan, Natarajan Vijay, Hotz Ingrid, Wang Bei, Scalar Field Comparison with Topological Descriptors: Properties and Applications for Scientific Visualization, Computer Graphics Forum, 40, 3, 2021. Crossref

  21. Athawale Tushar M., Johnson Chris R., Sane Sudhanshu, Pugmire David, Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models, IEEE Transactions on Visualization and Computer Graphics, 29, 1, 2023. Crossref

Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции Цены и условия подписки Begell House Контакты Language English 中文 Русский Português German French Spain