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Automatic Transfer Functions Based on Informational Divergence
Dec. 2011 (vol. 17 no. 12)
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
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.

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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 and Computer Graphics, vol. 17, no. 12, pp. 1932-1941, Dec. 2011, doi:10.1109/TVCG.2011.173
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