Automatic Transfer Functions Based on Informational Divergence
Automatic Transfer Functions Based on Informational Divergence
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DOI：
10.1109/tvcg.2011.173
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发表时间：
2011-12
影响因子：
5.2
通讯作者：
Marc Ruiz;A. Bardera;I. Boada;I. Viola;M. Feixas;M. Sbert
中科院分区：
文献类型：
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作者：
Marc Ruiz;A. Bardera;I. Boada;I. Viola;M. Feixas;M. Sbert
在本文中，我们提出了一种根据用户提供的目标分布来定义传递函数的框架。目标分布可以反映数据的重要性，或高度相关的数据值区间，或空间分割。我们的方法是基于体数据集的一组视点和一组面元之间的通信通道，它支持包含梯度信息的一维和二维传递函数。通过最小化视点捕获的能见度分布与用户选择的目标分布之间的信息发散或Kullback-Leibler距离来获得传递函数。信息发散度的导数的使用允许快速优化过程。分析了一维和二维传递函数的不同目标分布，以及重要性驱动和基于视图的技术。
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.