信息论已被证明可以有效地解决许多计算机视觉和模式识别（CVPR）问题，如图像匹配、聚类和分割、显著性检测、特征选择、最优分类器设计等。如今，研究人员正在将信息理论的元素广泛引入CVPR领域，其中包括测度（熵、交互信息）、原理（最大熵、极大极小熵）和理论（速率失真理论、类型法）等。本书通过增量复杂性方法探索和介绍了信息论的元素，同时也提出了CVPR问题的形成和和最具代表性的算法。当应用于不同的问题时，作者会突出信息理论原理之间的有趣关联，寻求一个全面的研究路线图。本书的研究结果为CVPR和机器学习。

1 Introduction
1.1 Measures, Principles, Theories, and More
1.2 Detailed Organization of the Book
1.3 The ITinCVPR Roadmap
2 Interest Points, Edges, and Contour Grouping
2.1 Introduction
2.2 Entropy and Interest Points
2.2.1 Kadir and Brady Scale Saliency Detector
2.2.2 Point Filtering by Entropy Analysis Through Scale Space
2.2.3 Chernoff Information and Optimal Filtering
2.2.4 Bayesian Filtering of the Scale Saliency Feature Extractor: The Algorithm
2.3 Information Theory as Evaluation Tool: The Statistical Edge Detection Case
2.3.1 Statistical Edge Detection
2.3.2 Edge Localization
2.4 Finding Contours Among Clutter
2.4.1 Problem Statement
2.4.2 A* Road Tracking
2.4.3 A* Convergence Proof
2.5 Junction Detection and Grouping
2.5.1 Junction Detection
2.5.2 Connecting and Filtering Junctions Problems
2.6 Key References
3 Contour and Region-Based Image Segmentation
3.1 Introduction
3.2 Discriminative Segmentation with Jensen-Shannon Divergence
3.2.1 The Active Polygons Functional
3.2.2 Jensen-Shannon Divergence and the Speed Function
3.3 MDL in Contour-Based Segmentation
3.3.1 B-Spline Parameterization of Contours
3.3.2 MDL for B-Spline Parameterization
3.3.3 MDL Contour-based Segmentation
3.4 Model Order Selection in Region-Based Segmentation
3.4.1 Jump-Diffusion for Optimal Segmentation
3.4.2 Speeding-up the Jump-Diffusion Process
3.4.3 K-adventurers Algorithm
3.5 Model-Based Segmentation Exploiting The Maximum Entropy Principle
3.5.1 Maximum Entropy and Markov Random Fields
3.5.2 Efficient Learning with Belief Propagation
3.6 Integrating Segmentation, Detection and Recognition
3.6.1 Image Parsing
3.6.2 The Data-Driven Generative Model
3.6.3 The Power of Discriminative Processes
3.6.4 The Usefulness of Combining Generative and Discriminative
Problems
3.7 Key References
4 Registration, Matching, and Recognition
4.1 Introduction
4.2 Image Alignment and Mutual Information
4.2.1 Alignment and Image Statistics
4.2.2 Entropy Estimation with Parzen's Windows
4.2.3 The EMMA Algorithm
4.2.4 Solving the Histogram-Binning Problem
4.3 Alternative Metrics for Image Alignment
4.3.1 Normalizing Mutual Information
4.3.2 Conditional Entropies
4.3.3 Extension to the Multimodal Case
4.3.4 Aifme Alignment of Multiple Images
4.3.5 The Renyi Entropy
4.3.6 RSnyi's Entropy and Entropic Spanning Graphs
4.3.7 The Jensen-Renyi Divergence and Its Applications
4.3.8 Other Measures Related to R~nyi Entropy
4.3.9 Experimental Results
4.4 Deformable Matching with Jensen Divergence and Fisher Information
4.4.1 The Distributional Shape Model
4.4.2 Multiple Registration and Jensen-Shannon Divergence
4.4.3 Information Geometry and Fisher-Rao Information
4.4.4 Dynamics of the Fisher Information Metric
4.5 Structural Learning with MDL
4.5.1 The Usefulness of Shock Trees
4.5.2 A Generative Tree Model Based on Mixtures
4.5.3 Learning the Mixture
4.5.4 Tree Edit-Distance and MDL
Problems
4.6 Key References
5 Image and Pattern Clustering
5.1 Introduction
5.2 Gaussian Mixtures and Model Selection
5.2.1 Gaussian Mixtures Methods
5.2.2 Defining Ganssian Mixtures
5.2.3 EM Algorithm and Its Drawbacks
5.2.4 Model Order Selection
5.3 EBEM Algorithm: Exploiting Entropic Graphs
5.3.1 The Gaussianity Criterion and Entropy Estimation
5.3.2 Shannon Entropy from R~nyi Entropy Estimation
5.3.3 Minimum Description Length for EBEM
5.3.4 Kernel-Splitting Equations
5.3.5 Experiments
5.4 Information Bottleneck and Rate Distortion Theory
5.4.1 Rate Distortion Theory Based Clustering
5.4.2 The Information Bottleneck Principle
5.5