Global Feature Tracking and Similarity Estimation in Time-Dependent Scalar Fields

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dc.contributor.authorSaikia, Himangshuen_US
dc.contributor.authorWeinkauf, Tinoen_US
dc.contributor.editorHeer, Jeffrey and Ropinski, Timo and van Wijk, Jarkeen_US
dc.date.accessioned2017-06-12T05:22:17Z
dc.date.available2017-06-12T05:22:17Z
dc.date.issued2017
dc.description.abstractWe present an algorithm for tracking regions in time-dependent scalar fields that uses global knowledge from all time steps for determining the tracks. The regions are defined using merge trees, thereby representing a hierarchical segmentation of the data in each time step. The similarity of regions of two consecutive time steps is measured using their volumetric overlap and a histogram difference. The main ingredient of our method is a directed acyclic graph that records all relevant similarity information as follows: the regions of all time steps are the nodes of the graph, the edges represent possible short feature tracks between consecutive time steps, and the edge weights are given by the similarity of the connected regions. We compute a feature track as the global solution of a shortest path problem in the graph. We use these results to steer the - to the best of our knowledge - first algorithm for spatio-temporal feature similarity estimation. Our algorithm works for 2D and 3D time-dependent scalar fields. We compare our results to previous work, showcase its robustness to noise, and exemplify its utility using several real-world data sets.en_US
dc.description.number3
dc.description.sectionheadersScalar Field Analysis
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume36
dc.identifier.doi10.1111/cgf.13163
dc.identifier.issn1467-8659
dc.identifier.pages001-011
dc.identifier.urihttps://doi.org/10.1111/cgf.13163
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13163
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.titleGlobal Feature Tracking and Similarity Estimation in Time-Dependent Scalar Fieldsen_US
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