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dc.contributor.authorWetzels, Florianen_US
dc.contributor.authorLeitte, Heikeen_US
dc.contributor.authorGarth, Christophen_US
dc.contributor.editorBorgo, Ritaen_US
dc.contributor.editorMarai, G. Elisabetaen_US
dc.contributor.editorSchreck, Tobiasen_US
dc.date.accessioned2022-06-03T06:06:17Z
dc.date.available2022-06-03T06:06:17Z
dc.date.issued2022
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14547
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14547
dc.description.abstractEdit distances between merge trees of scalar fields have many applications in scientific visualization, such as ensemble analysis, feature tracking or symmetry detection. In this paper, we propose branch mappings, a novel approach to the construction of edit mappings for merge trees. Classic edit mappings match nodes or edges of two trees onto each other, and therefore have to either rely on branch decompositions of both trees or have to use auxiliary node properties to determine a matching. In contrast, branch mappings employ branch properties instead of node similarity information, and are independent of predetermined branch decompositions. Especially for topological features, which are typically based on branch properties, this allows a more intuitive distance measure which is also less susceptible to instabilities from small-scale perturbations. For trees with O(n) nodes, we describe an O(n4) algorithm for computing optimal branch mappings, which is faster than the only other branch decomposition-independent method in the literature by more than a linear factor. Furthermore, we compare the results of our method on synthetic and real-world examples to demonstrate its practicality and utility.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleBranch Decomposition-Independent Edit Distances for Merge Treesen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersAlgorithms and Machine Learning
dc.description.volume41
dc.description.number3
dc.identifier.doi10.1111/cgf.14547
dc.identifier.pages367-378
dc.identifier.pages12 pages


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  • 41-Issue 3
    EuroVis 2022 - Conference Proceedings

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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License