| dc.contributor.author | Samsel, Francesca | en_US |
| dc.contributor.author | Overmyer, Trinity | en_US |
| dc.contributor.author | Navrátil, Paul A. | en_US |
| dc.contributor.editor | Johansson, Jimmy and Sadlo, Filip and Marai, G. Elisabeta | en_US |
| dc.date.accessioned | 2019-06-02T18:14:31Z | |
| dc.date.available | 2019-06-02T18:14:31Z | |
| dc.date.issued | 2019 | |
| dc.identifier.isbn | 978-3-03868-090-1 | |
| dc.identifier.uri | https://doi.org/10.2312/evs.20191170 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/evs20191170 | |
| dc.description.abstract | Color 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.publisher | The Eurographics Association | en_US |
| dc.title | Highlight Insert Colormaps: Luminance for Focused Data Analysis | en_US |
| dc.description.seriesinformation | EuroVis 2019 - Short Papers | |
| dc.description.sectionheaders | Volume, Simulation, and Data Reduction | |
| dc.identifier.doi | 10.2312/evs.20191170 | |
| dc.identifier.pages | 55-59 | |
