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Can I read Feature Tracking by Two-Step Optimization on EtoBox?
Feature Tracking by Two-Step Optimization by Andrea Schnorr; Dirk N. Helmrich; Dominik Denker; Torsten W. Kuhlen; Bernd Hentschel is a Computer Science article available to read on EtoBox.
What is Feature Tracking by Two-Step Optimization about?
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 additio
Who reads Feature Tracking by Two-Step Optimization?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Andrea Schnorr; Dirk N. Helmrich; Dominik Denker; Torsten W. Kuhlen; Bernd Hentschel
- Published
- 2020
- Language
- EN
- Field
- Computer Science (Physical Sciences)