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Automatic Transfer Functions based on Informational Divergence

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

ARTICLE, IEEE Transactions on Visualization and Computer Graphics, 2011

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.

Published

IEEE Transactions on Visualization and Computer Graphics

  • Volume: 17
  • Number: 12
  • Pages: 1932–1941
  • Event: IEEE Visualization Conference 2011
  • Location: Providence, RI, USA
  • Project: IllustraSound

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BibTeX

@article{ruiz11automaticTFs,
  author = {Marc Ruiz and Anton Bardera and Imma Boada and Ivan Viola and 
  Miquel Feixas and Mateu Sbert},
  title = {Automatic Transfer Functions based on Informational Divergence},
  year = {2011},
  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.},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  volume = {17},
  number = {12},
  pages = {1932--1941},
  event = {IEEE Visualization Conference 2011},
  location = {Providence, RI, USA},

}






 Last Modified: Jean-Paul Balabanian, 2014-06-18