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Automatic transfer functions based on informational divergence

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

This work was supported in part by Grant Numbers TIN2010-21089-C03-01 from the Spanish Government and 2009-SGR-643 from the Catalan Government, by the VERDIKT program (# 193170) of the Norwegian Research Council, and by the strategic funding for the MedViz research network (# 911597 P11) obtained from Helse Vest

© IEEE Transactions on Visualization and Computer Graphics, 2011, vol. 17, p. 1932-1941

Institute of Electrical and Electronics Engineers (IEEE)

Author: Ruiz Altisent, Marc
Bardera i Reig, Antoni
Boada, Imma
Viola, Ivan
Feixas Feixas, Miquel
Sbert, Mateu
Date: 2011 November
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
This work was supported in part by Grant Numbers TIN2010-21089-C03-01 from the Spanish Government and 2009-SGR-643 from the Catalan Government, by the VERDIKT program (# 193170) of the Norwegian Research Council, and by the strategic funding for the MedViz research network (# 911597 P11) obtained from Helse Vest
Format: application/pdf
ISSN: 1077-2626 (versió paper)
1941-0506 (versió electrònica)
Document access: http://hdl.handle.net/10256/12315
Language: eng
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Collection: MICINN/PN 2011-2013/TIN2010-21089-C03-01
AGAUR/2009-2014/2009 SGR-643
Versió postprint del document publicat a: http://dx.doi.org/10.1109/TVCG.2011.173
Articles publicats (D-IMA)
Is part of: © IEEE Transactions on Visualization and Computer Graphics, 2011, vol. 17, p. 1932-1941
Rights: Tots els drets reservats
Subject: Informació, Teoria de la
Information theory
Infografia
Computer graphics
Visualització (Informàtica)
Information display systems
Title: Automatic transfer functions based on informational divergence
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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