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Towards Benchmark Data Generation for Feature Tracking in Scalar Fields
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- Nilsson, Emma (author)
- Linköpings universitet,Medie- och Informationsteknik,Tekniska fakulteten
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- Lukasczyk, Jonas (author)
- TU Kaiserslautern, Germany
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- Masood, Talha Bin (author)
- Linköpings universitet,Medie- och Informationsteknik,Tekniska fakulteten
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- (creator_code:org_t)
- IEEE, 2022
- 2022
- English.
- In: 2022 IEEE WORKSHOP ON TOPOLOGICAL DATA ANALYSIS AND VISUALIZATION (TOPOINVIS 2022). - : IEEE. - 9781665493543 - 9781665493550 ; , s. 103-112
- Conference paper (peer-reviewed)
Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Keyword
- Human-centered computing; Visualization; Visualization design and evaluation methods; Human-centered computing; Visualization; Visualization application domains; Scientific visualization
Publication and Content Type
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