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Towards Benchmark Data Generation for Feature Tracking in Scalar Fields
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
TU Kaiserslautern, Germany.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-5352-1086
TU Kaiserslautern, Germany.
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2022 (English)In: 2022 IEEE WORKSHOP ON TOPOLOGICAL DATA ANALYSIS AND VISUALIZATION (TOPOINVIS 2022), IEEE , 2022, p. 103-112Conference paper, Published paper (Refereed)
Abstract [en]

We describe a benchmark data generator for tracking methods for two- and three-dimensional time-dependent scalar fields. More and more topology-based tracking methods are presented in the visualization community, but the validation and evaluation of the tracking results are currently limited to qualitative visual approaches. We present a pipeline for creating different ground truth features that support evaluating tracking methods based on quantitative measures. In short, our approach randomly simulates a temporal point cloud with birth, death, split, merge, and continuation events, where the points are then used to derive a scalar field whose topological features correspond to the points. These scalar fields can be used as the input for different tracking methods, where the computed tracks can be compared against the ground truth feature evolution. This approach facilitates directly comparing the results of different tracking methods, independent of the initial feature characterization.

Place, publisher, year, edition, pages
IEEE , 2022. p. 103-112
Keywords [en]
Human-centered computing; Visualization; Visualization design and evaluation methods; Human-centered computing; Visualization; Visualization application domains; Scientific visualization
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-191882DOI: 10.1109/TopoInVis57755.2022.00017ISI: 000913326500011ISBN: 9781665493543 (electronic)ISBN: 9781665493550 (print)OAI: oai:DiVA.org:liu-191882DiVA, id: diva2:1738720
Conference
IEEE VIS Workshop on Topological Data Analysis and Visualization (TopoInVis), Oklahoma City, OK, oct 17, 2022
Note

Funding Agencies|SeRC (Swedish e-Science Research Center); ELLIIT environment for strategic research in Sweden; Swedish Research Council (VR) [2019-05487]

Available from: 2023-02-22 Created: 2023-02-22 Last updated: 2023-06-09

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Nilsson, EmmaMasood, Talha BinHotz, Ingrid

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  • apa
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