Abstract
In this paper we present a framework to define transfer functions from a target distribution provided by the user. A target distribution can reflect the data importance, or highly relevant data value interval, or spatial segmentation. Our approach is based on a communication channel between a set of viewpoints and a set of bins of a volume data set, and it supports 1D as well as 2D transfer functions including the gradient information. The transfer functions are obtained by minimizing the informational divergence or Kullback-Leibler distance between the visibility distribution captured by the viewpoints and a target distribution selected by the user. The use of the derivative of the informational divergence allows for a fast optimization process. Different target distributions for 1D and 2D transfer functions are analyzed together with importance-driven and view-based techniques.
Original language | English |
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Pages (from-to) | 1932-1941 |
Number of pages | 10 |
Journal | IEEE Transactions on Visualization and Computer Graphics |
Volume | 17 |
Issue number | 12 |
DOIs | |
State | Published - Dec 2011 |
Externally published | Yes |
Event | IEEE Visualization Conference (Vis)/IEEE Information Visualization Conference (InfoVis) - Providence Duration: Oct 23 2011 → Oct 28 2011 |
Keywords
- Transfer function
- Information theory
- Informational divergence
- Kullback-Leibler distance
- THEORETIC FRAMEWORK
- VOLUME VISUALIZATION
- MATHEMATICAL-THEORY
- FLOW VISUALIZATION
- HISTOGRAMS
- COMMUNICATION
- SELECTION
- DESIGN