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dc.contributor.authorSamsel, Francescaen_US
dc.contributor.authorOvermyer, Trinityen_US
dc.contributor.authorNavrátil, Paul A.en_US
dc.contributor.editorJohansson, Jimmy and Sadlo, Filip and Marai, G. Elisabetaen_US
dc.date.accessioned2019-06-02T18:14:31Z
dc.date.available2019-06-02T18:14:31Z
dc.date.issued2019
dc.identifier.isbn978-3-03868-090-1
dc.identifier.urihttps://doi.org/10.2312/evs.20191170
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evs20191170
dc.description.abstractColor provides the primary conduit through which we extract insight from data visualizations. As the dynamic range of data grows, extracting salient features from surrounding context becomes increasingly challenging. Default colormaps provided by visualization software are poorly suited to perform such reductions of visual data. Here we present sets of highlight insert colormaps (HICs) that provide scientists with the means to quickly and easily render a detailed overview of their data, create detailed scans of their data, and examine the outer ranges of data in detail. This method builds on the long understood discriminatory power of luminance and in the highlight region provides 3x to 10x the discriminative power of common colormaps.en_US
dc.publisherThe Eurographics Associationen_US
dc.titleHighlight Insert Colormaps: Luminance for Focused Data Analysisen_US
dc.description.seriesinformationEuroVis 2019 - Short Papers
dc.description.sectionheadersVolume, Simulation, and Data Reduction
dc.identifier.doi10.2312/evs.20191170
dc.identifier.pages55-59


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