计算机图形学中的信息论方法(全彩英文版香农信息科学经典)

信息论方法广泛应用于工程、物理、遗传学、 神经科学等科学领域，在计算机图形学中也逐渐成 为有用的工具。本书中介绍了信息论的基本概念， 以及其如何在辐射度、自适应光线追踪、形状描述 符、视点选择与显著性、科学可视化和几何简化等 计算机图形领域应用。本书提出的一些方法，例如 视点技术，是可视化的新技术。本书强调了信息 论方法的共性方面，并以统一的方式介绍它们，以 便向读者表明信息论方法可以帮助解决计算机图形 学中的哪些问题，提供哪些特定工具，以及如何应 用它们。本书可供计算机图形学以及相关领域的学 生和技术人员学习阅读，IT领域学生和从业人员都 会对了解这些应用感兴趣。

Preface 1 Information Theory Basics 1.1 Entropy 1.2 Relative Entropy and Mutual Information 1.3 Inequalities 1.3.1 Jensen's Inequality 1.3.2 Log-sum Inequality 1.3.3 Jensen-Shannon Inequality 1.3.4 Data Processing Inequality 1.4 Entropy Rate 1.5 Entropy and Coding 1.6 Continuous Channel 1.7 Information Bottleneck Method 1.8 f-Divergences 1.9 Generalized Entropies 2 Scene Complexity and Refinement Criteria for Radiosity 2.1 Background 2.1.1 Radiosity Method 2.1.2 Form Factor Computation 2.1.3 Scene Random Walk 2.2 Scene Information Channel 2.2.1 Basic Definitions 2.2.2 From Visibility to Radiosity 2.3 Scene Complexity 2.3.1 Continuous Scene Visibility Mutual Information 2.3.2 Computation of Scene Visibility Complexity 2.3.3 Complexity and Discretisation 2.4 Refinement Criterion based on Mutual Information 2.4.1 Loss of Inform ation Transfer due to Discretisation 2.4.2 Mutual-Information-Based Oracle for Hierarchical Radiosity 2.5 Refinement Criteria Based on f-Divergences 3 Shape Descriptors 3.1 Background 3.2 Inner Shape Complexity 3.2.1 Complexity Measure 3.2.2 Inner 3D-shape Complexity Results 3.2.3 Inner 2D-shape Complexity Results 3.3 Outer Shape Complexity 4 Refinement Criteria for Ray-Tracing 4.1 Background 4.2 Pixel Quality 4.2.1 Pixel Color Entropy 4.2.2 Pixel Geometry Entropy 4.3 Pixel Contrast 4.3.1 Pixel Color Contrast 4.3.2 Pixel Geometry Contrast 4.3.3 Pixel Color-Geometry Contrast 4.4 Entropy-Based Supersampling 4.4.1 Algorithm 4.4.2 Results 4.5 Entropy-Based Adaptive Sampling 4.5.1 Adaptive Sampling 4.5.2 Algorithm 4.5.3 Implementation 4.5.4 Results 4.6 f-Divergences in Adaptive Sampling for Ray-Tracing 4.6.1 Algorithm 4.6.2 Results 5 Viewpoint Selection and Mesh Saliency 5.1 Background 5.2 Viewpoint Channel 5.2.1 Viewpoint Entropy and Mutual Information 5.2.2 Results 5.3 Viewpoint Similarity and Stability 5.4 Best View Selection and Object Exploration 5.4.1 Selection of N Best Views 5.4.2 Object Exploration 5.5 View-based Polygonal Information and Saliency 5.5.1 View-based Polygonal Information 5.5.2 View-based Mesh Saliency 5.6 Importance-driven Viewpoint Selection 6 View Selection in Scientific Visualization 6.1 Adaptation From Polygons to Volumes 6.1.1 Isosurfaces 6.1.2 Volumetric Data 6.2 Integration of Domain Semantics 6.2.1 Visualization of Molecular Structures 6.2.2 Guided Navigation in Data Semantics 7 Viewpoint-based Geometry Simplification 7.1 Background 7.2 Viewpoint-Based Error Metric 7.2.1 Analysis 7.3 Simplification Algorithm 7.4 Experiments 7.4.1 Viewpoint Entropy 7.4.2 Viewpoint Mutual Information 7.4.3 Viewpoint Kullback-Leibler Distance Summary Bibliography Author Biographies Index