dc.contributor.author | Lampe, Ove Daae | eng |
dc.contributor.author | Hauser, Helwig | eng |
dc.date.accessioned | 2011-12-15T10:09:40Z | |
dc.date.available | 2011-12-15T10:09:40Z | |
dc.date.issued | 2011-06-28 | eng |
dc.Published | Computer Graphics Forum 30(3): 633-642 | en |
dc.identifier.issn | 0167-7055 | en_US |
dc.identifier.uri | https://hdl.handle.net/1956/5297 | |
dc.description | Presented in Proceedings of Eurographics/IEEE-VGTC Symp. on Visualization (EuroVis 2011) 1-3 June 2011, Bergen, Norway | en |
dc.description.abstract | In this work, we present a technique based on kernel density estimation for rendering smooth curves. With this approach, we produce uncluttered and expressive pictures, revealing frequency information about one, or, multiple curves, independent of the level of detail in the data, the zoom level, and the screen resolution. With this technique the visual representation scales seamlessly from an exact line drawing, (for low-frequency/low-complexity curves) to a probability density estimate for more intricate situations. This scale-independence facilitates displays based on non-linear time, enabling high-resolution accuracy of recent values, accompanied by long historical series for context. We demonstrate the functionality of this approach in the context of prediction scenarios and in the context of streaming data. | en_US |
dc.language.iso | eng | eng |
dc.publisher | Blackwell Publishing Ltd. | en_US |
dc.relation.ispartof | <a href="http://hdl.handle.net/1956/5302" target="blank">Interactive Visual Analysis of Process Data</a> | en_US |
dc.title | Curve Density Estimates | en_US |
dc.type | Peer reviewed | |
dc.type | Journal article | |
dc.description.version | acceptedVersion | en_US |
dc.rights.holder | Copyright the authors. All rights reserved | en_US |
dc.identifier.doi | https://doi.org/10.1111/j.1467-8659.2011.01912.x | |
dc.subject.nsi | VDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation, visualization, signal processing, image processing: 429 | en_US |