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dc.contributor.authorBujack, Roxanaen_US
dc.contributor.authorKasten, Jensen_US
dc.contributor.authorNatarajan, Vijayen_US
dc.contributor.authorScheuermann, Geriken_US
dc.contributor.authorJoy, Kenneth I.en_US
dc.contributor.editorE. Bertini and J. Kennedy and E. Puppoen_US
dc.date.accessioned2015-05-24T19:43:09Z
dc.date.available2015-05-24T19:43:09Z
dc.date.issued2015en_US
dc.identifier.urihttp://dx.doi.org/10.2312/eurovisshort.20151121en_US
dc.description.abstractMoment invariants have proven to be a useful tool for the detection of patterns in scalar and vector fields. By their means, an interesting feature can be detected in a data set independent of its exact orientation, position, and scale. In this paper, we show that they can also be applied to explore an unknown dataset without prior determination of a query feature pattern it may possibly contain. The clustering of the high dimensional moment space reveals the major structures in the underlying flow field and gives an excellent overview for subsequent more profound exploration.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectImage Processing and Computer Vision [I.4.7]en_US
dc.subjectFeature Measurementen_US
dc.subjectMomentsen_US
dc.titleClustering Moment Invariants to Identify Similarity within 2D Flow Fieldsen_US
dc.description.seriesinformationEurographics Conference on Visualization (EuroVis) - Short Papersen_US
dc.description.sectionheadersVolume and Flow Visualizationen_US
dc.identifier.doi10.2312/eurovisshort.20151121en_US
dc.identifier.pages31-35en_US


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