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