Feature Tracking by Two-Step Optimization

Andrea Schnorr, Dirk Norbert Helmrich, Dominik Denker, Torsten Wolfgang Kuhlen, Bernd Hentschel
IEEE Transactions on Visualization and Computer Graphics (TVCG 2018)

Tracking the temporal evolution of features in time-varying data is a key method in visualization. For typical feature definitions, such as vortices, objects are sparsely distributed over the data domain. In this paper, we present a novel approach for tracking both sparse and space-filling features. While the former comprise only a small fraction of the domain, the latter form a set of objects whose union covers the domain entirely while the individual objects are mutually disjunct. Our approach determines the assignment of features between two successive time-steps by solving two graph optimization problems. It first resolves one-to-one assignments of features by computing a maximum-weight, maximum-cardinality matching on a weighted bi-partite graph. Second, our algorithm detects events by creating a graph of potentially conflicting event explanations and finding a weighted, independent set in it. We demonstrate our method's effectiveness on synthetic and simulation data sets, the former of which enables quantitative evaluation because of the availability of ground-truth information. Here, our method performs on par or better than a well-established reference algorithm. In addition, manual visual inspection by our collaborators confirm the results' plausibility for simulation data.

» Show BibTeX

author = {Andrea Schnorr and Dirk N. Helmrich and Dominik Denker and Torsten W. Kuhlen and Bernd Hentschel},
title = {{F}eature {T}racking by {T}wo-{S}tep {O}ptimization},
journal = TVCG,
volume = {preprint available online},
doi = {https://doi.org/10.1109/TVCG.2018.2883630},
year = 2018,

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