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電腦圖形學中的資訊理論方法(全彩英文版香農信息科學經典)

  • 作者:(西)馬修·斯伯特//米格爾·費薩斯//海梅·里高//米格爾·喬弗//(奧)伊萬·維奧拉|責編:陳亮//夏丹
  • 出版社:世界圖書出版公司
  • ISBN:9787519275976
  • 出版日期:2020/08/01
  • 裝幀:平裝
  • 頁數:153
人民幣:RMB 139 元      售價:
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內容大鋼
    資訊理論方法廣泛應用於工程、物理、遺傳學、神經科學等科學領域,在電腦圖形學中也逐漸成為有用的工具。本書中介紹了資訊理論的基本概念,以及其如何在輻射度、自適應光線追蹤、形狀描述符、視點選擇與顯著性、科學可視化和幾何簡化等電腦圖形領域應用。本書提出的一些方法,例如視點技術,是可視化的最新技術。本書強調了資訊理論方法的共性方面,並以統一的方式介紹它們,以便向讀者表明資訊理論方法可以幫助解決電腦圖形學中的哪些問題,提供哪些特定工具,以及如何應用它們。
    本書可供電腦圖形學以及相關領域的學生和技術人員學習閱讀,IT領域學生和從業人員都會對了解這些應用感興趣。

作者介紹
(西)馬修·斯伯特//米格爾·費薩斯//海梅·里高//米格爾·喬弗//(奧)伊萬·維奧拉|責編:陳亮//夏丹

目錄
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

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