Poster: Complexity Estimation for Feature Tracking Data.

Dirk Norbert Helmrich, Andrea Schnorr, Torsten Wolfgang Kuhlen, Bernd Hentschel
The 8th IEEE Symposium on Large Data Analysis and Visualization (LDAV 2018)

Feature tracking is a method of time-varying data analysis. Due to the complexity of the underlying problem, different feature tracking algorithms have different levels of correctness in certain use cases. However, there is no efficient way to evaluate their performance on simulation data since there is no ground-truth easily obtainable. Synthetic data is a way to ensure a minimum level of correctness, though there are limits to their expressiveness when comparing the results to simulation data. To close this gap, we calculate a synthetic data set and use its results to extract a hypothesis about the algorithm performance that we can apply to simulation data.

» Show BibTeX

title={Complexity Estimation for Feature Tracking Data.},
author={Helmrich, Dirk N and Schnorr, Andrea and Kuhlen, Torsten W and Hentschel, Bernd},

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