Automatic Transfer Functions Based on Informational Divergence

Marc Ruiz*, Anton Bardera, Imma Boada, Ivan Viola, Miquel Feixas, Mateu Sbert

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

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 languageEnglish
Pages (from-to)1932-1941
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume17
Issue number12
DOIs
StatePublished - Dec 2011
Externally publishedYes
EventIEEE Visualization Conference (Vis)/IEEE Information Visualization Conference (InfoVis) - Providence
Duration: Oct 23 2011Oct 28 2011

Keywords

  • Transfer function
  • Information theory
  • Informational divergence
  • Kullback-Leibler distance
  • THEORETIC FRAMEWORK
  • VOLUME VISUALIZATION
  • MATHEMATICAL-THEORY
  • FLOW VISUALIZATION
  • HISTOGRAMS
  • COMMUNICATION
  • SELECTION
  • DESIGN

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