Automatic transfer functions based on informational divergence.

IEEE Transactions on Visualization and Computer Graphics(2011)

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摘要
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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关键词
kullback-leibler distance,relevant data value interval,automatic transfer,volume data,target distribution,communication channel,informational divergence,different target distribution,data importance,visibility distribution,transfer function,probability distribution,information theory,transfer functions,visibility,kullback leibler distance,data visualization,mutual information,information analysis,optical transfer function
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