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Issue No.12 - Dec. (2011 vol.17)
pp: 1932-1941
Marc Ruiz , University of Girona
Anton Bardera , University of Girona
Imma Boada , University of Girona
Ivan Viola , University of Bergen
Miquel Feixas , University of Girona
Mateu Sbert , University of Girona
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.
INDEX TERMS
Transfer function, Information theory, Informational divergence, Kullback-Leibler distance.
CITATION
Marc Ruiz, Anton Bardera, Imma Boada, Ivan Viola, Miquel Feixas, Mateu Sbert, "Automatic Transfer Functions Based on Informational Divergence", IEEE Transactions on Visualization & Computer Graphics, vol.17, no. 12, pp. 1932-1941, Dec. 2011, doi:10.1109/TVCG.2011.173
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