Automatic Transfer Functions based on Informational Divergence
Abstract
In this paper we present a framework to define transfer functions from a target distribution provided by the user. A targetdistribution can reflect the data importance, or highly relevant data value interval, or spatial segmentation. Our approach is basedon 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.
M. Ruiz, A. Bardera, I. Boada, I. Viola, M. Feixas, and M. Sbert, "Automatic Transfer Functions based on Informational Divergence," IEEE Transactions on Visualization and Computer Graphics, vol. 17, iss. 12, p. 1932–1941, 2011.
[BibTeX]
In this paper we present a framework to define transfer functions from a target distribution provided by the user. A targetdistribution can reflect the data importance, or highly relevant data value interval, or spatial segmentation. Our approach is basedon 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.
@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",
journal = "IEEE Transactions on Visualization and Computer Graphics",
year = "2011",
volume = "17",
number = "12",
pages = "1932--1941",
abstract = "In this paper we present a framework to define transfer functions from a target distribution provided by the user. A targetdistribution can reflect the data importance, or highly relevant data value interval, or spatial segmentation. Our approach is basedon 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.",
images = "images/ruiz11automaticTFs1.jpg, images/ruiz11automaticTFs2.jpg, images/ruiz11automaticTFs3.jpg, images/ruiz11automaticTFs4.jpg",
thumbnails = "images/ruiz11automaticTFs1_thumb.jpg, images/ruiz11automaticTFs2_thumb.jpg, images/ruiz11automaticTFs3_thumb.jpg, images/ruiz11automaticTFs4_thumb.jpg",
event = "IEEE Visualization Conference 2011",
location = "Providence, RI, USA",
project = "illustrasound"
}