Extended Branch Decomposition Graphs: Structural Comparison of Scalar Data

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dc.contributor.authorSaikia, Himangshuen_US
dc.contributor.authorSeidel, Hans-Peteren_US
dc.contributor.authorWeinkauf, Tinoen_US
dc.contributor.editorH. Carr, P. Rheingans, and H. Schumannen_US
dc.date.accessioned2015-03-03T12:33:43Z
dc.date.available2015-03-03T12:33:43Z
dc.date.issued2014en_US
dc.description.abstractWe present a method to find repeating topological structures in scalar data sets. More precisely, we compare all subtrees of two merge trees against each other - in an efficient manner exploiting redundancy. This provides pair-wise distances between the topological structures defined by sub/superlevel sets, which can be exploited in several applications such as finding similar structures in the same data set, assessing periodic behavior in time-dependent data, and comparing the topology of two different data sets. To do so, we introduce a novel data structure called the extended branch decomposition graph, which is composed of the branch decompositions of all subtrees of the merge tree. Based on dynamic programming, we provide two highly efficient algorithms for computing and comparing extended branch decomposition graphs. Several applications attest to the utility of our method and its robustness against noise.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12360en_US
dc.publisherThe Eurographics Association and John Wiley and Sons Ltd.en_US
dc.titleExtended Branch Decomposition Graphs: Structural Comparison of Scalar Dataen_US
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EuroVis14: Eurographics Conference on Visualization