Feature Tracking by Two-Step Optimization
| Autor: | Bernd Hentschel, Dominik Denker, Torsten Kuhlen, Dirk N. Helmrich, Andrea Schnorr |
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| Rok vydání: | 2020 |
| Předmět: |
Optimization problem
Matching (graph theory) Computer science Data domain Feature extraction Two-graph Graph theory Computer Graphics and Computer-Aided Design Graph Feature (computer vision) Independent set Signal Processing Graph (abstract data type) Computer Vision and Pattern Recognition Algorithm Software |
| Zdroj: | IEEE Transactions on Visualization and Computer Graphics. 26:2219-2233 |
| ISSN: | 2160-9306 1077-2626 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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