Information Theory in Scientific Visualization

被引:72
|
作者
Wang, Chaoli [1 ]
Shen, Han-Wei [2 ]
机构
[1] Michigan Technol Univ, Dept Comp Sci, Houghton, MI 49931 USA
[2] Ohio State Univ, Dept Comp Sci & Engn, Columbus, OH 43210 USA
基金
美国国家科学基金会;
关键词
information theory; scientific visualization; visual communication channel; MUTUAL-INFORMATION; VIEW SELECTION; IMAGE; FRAMEWORK; REGISTRATION; MAXIMIZATION; ENTROPY;
D O I
10.3390/e13010254
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
In recent years, there is an emerging direction that leverages information theory to solve many challenging problems in scientific data analysis and visualization. In this article, we review the key concepts in information theory, discuss how the principles of information theory can be useful for visualization, and provide specific examples to draw connections between data communication and data visualization in terms of how information can be measured quantitatively. As the amount of digital data available to us increases at an astounding speed, the goal of this article is to introduce the interested readers to this new direction of data analysis research, and to inspire them to identify new applications and seek solutions using information theory.
引用
收藏
页码:254 / 273
页数:20
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