Curve Density Estimates

Ove Daae Lampe, Helwig Hauser

ARTICLE, Computer Graphics Forum, 2011

• www

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.

Published

Computer Graphics Forum

• Volume: 30
• Number: 3
• Pages: 633–642
• Event: EuroVis 2011
• Location: Bergen, Norway
• Project: elad
• URL: http://dx.doi.org/10.1111/j.1467-8659.2011.01912.x

Media

BibTeX

@article{lampe11curveDensity,
  author = {Ove Daae Lampe and Helwig Hauser},
  title = {Curve Density Estimates},
  year = {2011},
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.},
  journal = {Computer Graphics Forum},
  volume = {30},
  number = {3},
  pages = {633--642},
  url = {http://dx.doi.org/10.1111/j.1467-8659.2011.01912.x},
  event = {EuroVis 2011},
  location = {Bergen, Norway},  

}