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Curve Density Estimates

Lampe, Ove Daae; Hauser, Helwig
Peer reviewed, Journal article
Accepted version
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URI
https://hdl.handle.net/1956/5297
Date
2011-06-28
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  • Department of Informatics [541]
Original version
https://doi.org/10.1111/j.1467-8659.2011.01912.x
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.
Description
Presented in Proceedings of Eurographics/IEEE-VGTC Symp. on Visualization (EuroVis 2011) 1-3 June 2011, Bergen, Norway
Publisher
Blackwell Publishing Ltd.
Copyright
Copyright the authors. All rights reserved

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