计算机视觉

出版时间：2012-5  出版社：电子工业出版社  作者：福赛斯(David A. Forsyth),泊斯(Jean Ponce)  页数：761  字数：1268000  译者：David A.Forsyth  
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内容概要

　　计算机视觉是研究如何使人工系统从图像或多维数据中“感知”的科学。本书是计算机视觉领域的经典教材，内容涉及几何摄像模型、光照和着色、色彩、线性滤波、局部图像特征、纹理、立体相对、运动结构、聚类分割、组合与模型拟合、追踪、配准、平滑表面与骨架、距离数据、图像分类、对象检测与识别、基于图像的建模与渲染、人形研究、图像搜索与检索、优化技术等内容。与前一版相比，本书简化了部分主题，增加了应用示例，重写了关于现代特性的内容，详述了现代图像编辑技术与对象识别技术。

作者简介

作者：（美国）福赛斯（David A. Forsyth） （美国）泊斯（Jean Ponce）

书籍目录

I IMAGE FORMATION
1 Geometric Camera Models
　1.1 Image Formation
　　1.1.1 Pinhole Perspective
　　1.1.2 Weak Perspective
　　1.1.3 Cameras with Lenses
　　1.1.4 The Human Eye
　1.2 Intrinsic and Extrinsic Parameters
　　1.2.1 Rigid Transformations and Homogeneous Coordinates
　　1.2.2 Intrinsic Parameters
　　1.2.3 Extrinsic Parameters
　　1.2.4 Perspective Projection Matrices
　　1.2.5 Weak-Perspective Projection Matrices
　1.3 Geometric Camera Calibration
　　1.3.1 ALinear Approach to Camera Calibration
　　1.3.2 ANonlinear Approach to Camera Calibration
　1.4 Notes
2 Light and Shading
　2.1 Modelling Pixel Brightness
　　2.1.1 Reflection at Surfaces
　　2.1.2 Sources and Their Effects
　　2.1.3 The Lambertian+Specular Model
　　2.1.4 Area Sources
　2.2 Inference from Shading
　　2.2.1 Radiometric Calibration and High Dynamic Range Images
　　2.2.2 The Shape of Specularities
　　2.2.3 Inferring Lightness and Illumination
　　2.2.4 Photometric Stereo: Shape from Multiple Shaded Images
　2.3 Modelling Interreflection
　　2.3.1 The Illumination at a Patch Due to an Area Source
　　2.3.2 Radiosity and Exitance
　　2.3.3 An Interreflection Model
　　2.3.4 Qualitative Properties of Interreflections
　2.4 Shape from One Shaded Image
　2.5 Notes
3 Color
　3.1 Human Color Perception
　　3.1.1 Color Matching
　　3.1.2 Color Receptors
　3.2 The Physics of Color
　　3.2.1 The Color of Light Sources
　　3.2.2 The Color of Surfaces
　3.3 Representing Color
　　3.3.1 Linear Color Spaces
　　3.3.2 Non-linear Color Spaces
　3.4 AModel of Image Color
　　3.4.1 The Diffuse Term
　　3.4.2 The Specular Term
　3.5 Inference from Color
　　3.5.1 Finding Specularities Using Color
　　3.5.2 Shadow Removal Using Color
　　3.5.3 Color Constancy: Surface Color from Image Color
　3.6 Notes
II EARLY VISION: JUST ONE IMAGE
4 Linear Filters
　4.1 Linear Filters and Convolution
　　4.1.1 Convolution
　4.2 Shift Invariant Linear Systems
　　4.2.1 Discrete Convolution
　　4.2.2 Continuous Convolution
　　4.2.3 Edge Effects in Discrete Convolutions
　4.3 Spatial Frequency and Fourier Transforms
　　4.3.1 Fourier Transforms
　4.4 Sampling and Aliasing
　　4.4.1 Sampling
　　4.4.2 Aliasing
　　4.4.3 Smoothing and Resampling
　4.5 Filters as Templates
　　4.5.1 Convolution as a Dot Product
　　4.5.2 Changing Basis
　4.6 Technique: Normalized Correlation and Finding Patterns
　　4.6.1 Controlling the Television by Finding Hands by
Normalized
　　Correlation
　4.7 Technique: Scale and Image Pyramids
　　4.7.1 The Gaussian Pyramid
　　4.7.2 Applications of Scaled Representations
　4.8 Notes
5 Local Image Features
　5.1 Computing the Image Gradient
　　5.1.1 Derivative of Gaussian Filters
　5.2 Representing the Image Gradient
　　5.2.1 Gradient-Based Edge Detectors
　　5.2.2 Orientations
　5.3 Finding Corners and Building Neighborhoods
　　5.3.1 Finding Corners
　　5.3.2 Using Scale and Orientation to Build a Neighborhood
　5.4 Describing Neighborhoods with SIFT and HOG Features
　　5.4.1 SIFT Features
　　5.4.2 HOG Features
　5.5 Computing Local Features in Practice
　5.6 Notes
6 Texture
　6.1 Local Texture Representations Using Filters
　　6.1.1 Spots and Bars
　　6.1.2 From Filter Outputs to Texture Representation
　　6.1.3 Local Texture Representations in Practice
　6.2 Pooled Texture Representations by Discovering Textons
　　6.2.1 Vector Quantization and Textons
　　6.2.2 K-means Clustering for Vector Quantization
　6.3 Synthesizing Textures and Filling Holes in Images
　　6.3.1 Synthesis by Sampling Local Models
　　6.3.2 Filling in Holes in Images
　6.4 Image Denoising
　　6.4.1 Non-local Means
　　6.4.2 Block Matching 3D (BM3D)
　　6.4.3 Learned Sparse Coding
　　6.4.4 Results
　6.5 Shape from Texture
　　6.5.1 Shape from Texture for Planes
　　6.5.2 Shape from Texture for Curved Surfaces
　6.6 Notes
III EARLY VISION: MULTIPLE IMAGES
7 Stereopsis
　7.1 Binocular Camera Geometry and the Epipolar Constraint
　　7.1.1 Epipolar Geometry
　　7.1.2 The Essential Matrix
　　7.1.3 The Fundamental Matrix
　7.2 Binocular Reconstruction
　　7.2.1 Image Rectification
　7.3 Human Stereopsis
　7.4 Local Methods for Binocular Fusion
　　7.4.1 Correlation
　　7.4.2 Multi-Scale Edge Matching
　7.5 Global Methods for Binocular Fusion
　　7.5.1 Ordering Constraints and Dynamic Programming
　　7.5.2 Smoothness and Graphs
　7.6 Using More Cameras
　　7.7 Application: Robot Navigation
　7.8 Notes
8 Structure from Motion
　8.1 Internally Calibrated Perspective Cameras
　　8.1.1 Natural Ambiguity of the Problem
　　8.1.2 Euclidean Structure and Motion from Two Images
　　8.1.3 Euclidean Structure and Motion from Multiple Images
　8.2 Uncalibrated Weak-Perspective Cameras
　　8.2.1 Natural Ambiguity of the Problem
　　8.2.2 Affine Structure and Motion from Two Images
　　8.2.3 Affine Structure and Motion from Multiple Images
　　8.2.4 From Affine to Euclidean Shape
　8.3 Uncalibrated Perspective Cameras
　　8.3.1 Natural Ambiguity of the Problem
　　8.3.2 Projective Structure and Motion from Two Images
　　8.3.3 Projective Structure and Motion from Multiple Images
　　8.3.4 From Projective to Euclidean Shape
　8.4 Notes
IV MID-LEVEL VISION
9 Segmentation by Clustering
　9.1 Human Vision: Grouping and Gestalt
　9.2 Important Applications
　　9.2.1 Background Subtraction
　　9.2.2 Shot Boundary Detection
　　9.2.3 Interactive Segmentation
　　9.2.4 Forming Image Regions
　9.3 Image Segmentation by Clustering Pixels
　　9.3.1 Basic Clustering Methods
　　9.3.2 The Watershed Algorithm
　　9.3.3 Segmentation Using K-means
　　9.3.4 Mean Shift: Finding Local Modes in Data
　　9.3.5 Clustering and Segmentation with Mean Shift
　9.4 Segmentation, Clustering, and Graphs
　　9.4.1 Terminology and Facts for Graphs
　　9.4.2 Agglomerative Clustering with a Graph
　　9.4.3 Divisive Clustering with a Graph
　　9.4.4 Normalized Cuts
　9.5 Image Segmentation in Practice
　　9.5.1 Evaluating Segmenters
　9.6 Notes
10 Grouping and Model Fitting
　10.1 The Hough Transform
　　10.1.1 Fitting Lines with the Hough Transform
　　10.1.2 Using the Hough Transform
　10.2 Fitting Lines and Planes
　　10.2.1 Fitting a Single Line
　　10.2.2 Fitting Planes
　　10.2.3 Fitting Multiple Lines
　10.3 Fitting Curved Structures
　10.4 Robustness
　　10.4.1 M-Estimators
　　10.4.2 RANSAC: Searching for Good Points
　　10.5 Fitting Using Probabilistic Models
　　10.5.1 Missing Data Problems
　　10.5.2 Mixture Models and Hidden Variables
　　10.5.3 The EM Algorithm for Mixture Models
　　10.5.4 Difficulties with the EM Algorithm
　10.6 Motion Segmentation by Parameter Estimation
　　10.6.1 Optical Flow and Motion
　　10.6.2 Flow Models
　　10.6.3 Motion Segmentation with Layers
　10.7 Model Selection: Which Model Is the Best Fit?
　　10.7.1 Model Selection Using Cross-Validation
　10.8 Notes
11 Tracking
　11.1 Simple Tracking Strategies
　　11.1.1 Tracking by Detection
　　11.1.2 Tracking Translations by Matching
　　11.1.3 Using Affine Transformations to Confirm a Match
　11.2 Tracking Using Matching
　　11.2.1 Matching Summary Representations
　　11.2.2 Tracking Using Flow
　11.3 Tracking Linear Dynamical Models with Kalman Filters
　　11.3.1 Linear Measurements and Linear Dynamics
　　11.3.2 The Kalman Filter
　　11.3.3 Forward-backward Smoothing
　11.4 Data Association
　　11.4.1 Linking Kalman Filters with Detection Methods
　　11.4.2 Key Methods of Data Association
　11.5 Particle Filtering
　　11.5.1 Sampled Representations of Probability Distributions
　　11.5.2 The Simplest Particle Filter
　　11.5.3 The Tracking Algorithm
　　11.5.4 A Workable Particle Filter
　　11.5.5 Practical Issues in Particle Filters
　11.6 Notes
V HIGH-LEVEL VISION
12 Registration
　12.1 Registering Rigid Objects
　　12.1.1 Iterated Closest Points
　　12.1.2 Searching for Transformations via Correspondences
　　12.1.3 Application: Building Image Mosaics
　12.2 Model-based Vision: Registering Rigid Objects with
Projection
　　12.2.1 Verification: Comparing Transformed and Rendered
Source
　　to Target
　12.3 Registering Deformable Objects
　　12.3.1 Deforming Texture with Active Appearance Models
　　12.3.2 Active Appearance Models in Practice
　　12.3.3 Application: Registration in Medical Imaging Systems
　12.4 Notes
13 Smooth Surfaces and Their Outlines
　13.1 Elements of Differential Geometry
　　13.1.1 Curves
　　13.1.2 Surfaces
　13.2 Contour Geometry
　　13.2.1 The Occluding Contour and the Image Contour
　　13.2.2 The Cusps and Inflections of the Image Contour
　　13.2.3 Koenderink’s Theorem
　13.3 Visual Events: More Differential Geometry
　　13.3.1 The Geometry of the Gauss Map
　　13.3.2 Asymptotic Curves
　　13.3.3 The Asymptotic Spherical Map
　　13.3.4 Local Visual Events
　　13.3.5 The Bitangent Ray Manifold
　　13.3.6 Multilocal Visual Events
　　13.3.7 The Aspect Graph
　13.4 Notes
14 Range Data
　14.1 Active Range Sensors
　14.2 Range Data Segmentation
　　14.2.1 Elements of Analytical Differential Geometry
　　14.2.2 Finding Step and Roof Edges in Range Images
　　14.2.3 Segmenting Range Images into Planar Regions
　14.3 Range Image Registration and Model Acquisition
　　14.3.1 Quaternions
　　14.3.2 Registering Range Images
　　14.3.3 Fusing Multiple Range Images
　14.4 Object Recognition
　　14.4.1 Matching Using Interpretation Trees
　　14.4.2 Matching Free-Form Surfaces Using Spin Images
　14.5 Kinect
　　14.5.1 Features
　　14.5.2 Technique: Decision Trees and Random Forests
　　14.5.3 Labeling Pixels
　　14.5.4 Computing Joint Positions
　14.6 Notes
15 Learning to Classify
　15.1 Classification, Error, and Loss
　　15.1.1 Using Loss to Determine Decisions
　　15.1.2 Training Error, Test Error, and Overfitting
　　15.1.3 Regularization
　　15.1.4 Error Rate and Cross-Validation
　　15.1.5 Receiver Operating Curves
　15.2 Major Classification Strategies
　　15.2.1 Example: Mahalanobis Distance
　　15.2.2 Example: Class-Conditional Histograms and Naive
Bayes
　　15.2.3 Example: Classification Using Nearest Neighbors
　　15.2.4 Example: The Linear Support Vector Machine
　　15.2.5 Example: Kernel Machines
　　15.2.6 Example: Boosting and Adaboost
　15.3 Practical Methods for Building Classifiers
　　15.3.1 Manipulating Training Data to Improve Performance
　　15.3.2 Building Multi-Class Classifiers Out of Binary
Classifiers
　　15.3.3 Solving for SVMS and Kernel Machines
　15.4 Notes
16 Classifying Images
　16.1 Building Good Image Features
　　16.1.1 Example Applications
　　16.1.2 Encoding Layout with GIST Features
　　16.1.3 Summarizing Images with Visual Words
　　16.1.4 The Spatial Pyramid Kernel
　　16.1.5 Dimension Reduction with Principal Components
　　16.1.6 Dimension Reduction with Canonical Variates
　　16.1.7 Example Application: Identifying Explicit Images
　　16.1.8 Example Application: Classifying Materials
　　16.1.9 Example Application: Classifying Scenes
　16.2 Classifying Images of Single Objects
　　16.2.1 Image Classification Strategies
　　16.2.2 Evaluating Image Classification Systems
　　16.2.3 Fixed Sets of Classes
　　16.2.4 Large Numbers of Classes
　　16.2.5 Flowers, Leaves, and Birds: Some Specialized
Problems
　16.3 Image Classification in Practice
　　16.3.1 Codes for Image Features
　　16.3.2 Image Classification Datasets
　　16.3.3 Dataset Bias
　　16.3.4 Crowdsourcing Dataset Collection
　16.4 Notes
17 Detecting Objects in Images
　17.1 The Sliding Window Method
　　17.1.1 Face Detection
　　17.1.2 Detecting Humans
　　17.1.3 Detecting Boundaries
　17.2 Detecting Deformable Objects
　17.3 The State of the Art of Object Detection
　　17.3.1 Datasets and Resources
　17.4 Notes
18 Topics in Object Recognition
　18.1 What Should Object Recognition Do?
　　18.1.1 What Should an Object Recognition System Do?
　　18.1.2 Current Strategies for Object Recognition
　　18.1.3 What Is Categorization?
　　18.1.4 Selection: What Should Be Described?
　18.2 Feature Questions
　　18.2.1 Improving Current Image Features
　　18.2.2 Other Kinds of Image Feature
　18.3 Geometric Questions
　18.4 Semantic Questions
　　18.4.1 Attributes and the Unfamiliar
　　18.4.2 Parts, Poselets and Consistency
　　18.4.3 Chunks of Meaning
VI APPLICATIONS AND TOPICS
19 Image-Based Modeling and Rendering
　19.1 Visual Hulls
　　19.1.1 Main Elements of the Visual Hull Model
　　19.1.2 Tracing Intersection Curves
　　19.1.3 Clipping Intersection Curves
　　19.1.4 Triangulating Cone Strips
　　19.1.5 Results
　　19.1.6 Going Further: Carved Visual Hulls
　19.2 Patch-Based Multi-View Stereopsis
　　19.2.1 Main Elements of the PMVS Model
　　19.2.2 Initial Feature Matching
　　19.2.3 Expansion
　　19.2.4 Filtering
　　19.2.5 Results
　19.3 The Light Field
　19.4 Notes
20 Looking at People
　20.1 HMM’s, Dynamic Programming, and Tree-Structured Models
　　20.1.1 Hidden Markov Models
　　20.1.2 Inference for an HMM
　　20.1.3 Fitting an HMM with EM
　　20.1.4 Tree-Structured Energy Models
　20.2 Parsing People in Images
　　20.2.1 Parsing with Pictorial Structure Models
　　20.2.2 Estimating the Appearance of Clothing
　20.3 Tracking People
　　20.3.1 Why Human Tracking Is Hard
　　20.3.2 Kinematic Tracking by Appearance
　　20.3.3 Kinematic Human Tracking Using Templates
　20.4 3D from 2D: Lifting
　　20.4.1 Reconstruction in an Orthographic View
　　20.4.2 Exploiting Appearance for Unambiguous
Reconstructions
　　20.4.3 Exploiting Motion for Unambiguous Reconstructions
　20.5 Activity Recognition
　　20.5.1 Background: Human Motion Data
　　20.5.2 Body Configuration and Activity Recognition
　　20.5.3 Recognizing Human Activities with Appearance
Features
　　20.5.4 Recognizing Human Activities with Compositional
Models
　20.6 Resources
　20.7 Notes
21 Image Search and Retrieval
　21.1 The Application Context
　　21.1.1 Applications
　　21.1.2 User Needs
　　21.1.3 Types of Image Query
　　21.1.4 What Users Do with Image Collections
　21.2 Basic Technologies from Information Retrieval
　　21.2.1 Word Counts
　　21.2.2 Smoothing Word Counts
　　21.2.3 Approximate Nearest Neighbors and Hashing
　　21.2.4 Ranking Documents
　21.3 Images as Documents
　　21.3.1 Matching Without Quantization
　　21.3.2 Ranking Image Search Results
　　21.3.3 Browsing and Layout
　　21.3.4 Laying Out Images for Browsing
　21.4 Predicting Annotations for Pictures
　　21.4.1 Annotations from Nearby Words
　　21.4.2 Annotations from the Whole Image
　　21.4.3 Predicting Correlated Words with Classifiers
　　21.4.4 Names and Faces
　　21.4.5 Generating Tags with Segments
　21.5 The State of the Art of Word Prediction
　　21.5.1 Resources
　　21.5.2 Comparing Methods
　　21.5.3 Open Problems
　21.6 Notes
VII BACKGROUND MATERIAL
22 Optimization Techniques
　22.1 Linear Least-Squares Methods
　　22.1.1 Normal Equations and the Pseudoinverse
　　22.1.2 Homogeneous Systems and Eigenvalue Problems
　　22.1.3 Generalized Eigenvalues Problems
　　22.1.4 An Example: Fitting a Line to Points in a Plane
　　22.1.5 Singular Value Decomposition
　22.2 Nonlinear Least-Squares Methods
　　22.2.1 Newton’s Method: Square Systems of Nonlinear
Equations.
　　22.2.2 Newton’s Method for Overconstrained Systems
　　22.2.3 The Gauss—Newton and Levenberg—Marquardt Algorithms
　22.3 Sparse Coding and Dictionary Learning
　　22.3.1 Sparse Coding
　　22.3.2 Dictionary Learning
　　22.3.3 Supervised Dictionary Learning
　22.4 Min-Cut/Max-Flow Problems and Combinatorial
Optimization
　　22.4.1 Min-Cut Problems
　　22.4.2 Quadratic Pseudo-Boolean Functions
　　22.4.3 Generalization to Integer Variables
　22.5 Notes
　　Bibliography
　　Index
　　List of Algorithms
　　Courses
　　Computer Vision (Computer Science)
　　Previous Edition(s)
　　Net price is Pearson＇s wholesale price to college bookstores and
other resellers.
　　Table of Contents
I IMAGE FORMATION
1 Geometric Camera Models
　　1.1 Image Formation
　　1.1.1 Pinhole Perspective
　　1.1.2 Weak Perspective
　　1.1.3 Cameras with Lenses
　　1.1.4 The Human Eye
　1.2 Intrinsic and Extrinsic Parameters
　　1.2.1 Rigid Transformations and Homogeneous Coordinates
　　1.2.2 Intrinsic Parameters
　　1.2.3 Extrinsic Parameters
　　1.2.4 Perspective Projection Matrices
　　1.2.5 Weak-Perspective Projection Matrices
　1.3 Geometric Camera Calibration
　　1.3.1 ALinear Approach to Camera Calibration
　　1.3.2 ANonlinear Approach to Camera Calibration
　1.4 Notes
2 Light and Shading
　2.1 Modelling Pixel Brightness
　　2.1.1 Reflection at Surfaces
　　2.1.2 Sources and Their Effects
　　2.1.3 The Lambertian+Specular Model
　　2.1.4 Area Sources
　2.2 Inference from Shading
　　2.2.1 Radiometric Calibration and High Dynamic Range Images
　　2.2.2 The Shape of Specularities
　　2.2.3 Inferring Lightness and Illumination
　　2.2.4 Photometric Stereo: Shape from Multiple Shaded Images
　2.3 Modelling Interreflection
　　2.3.1 The Illumination at a Patch Due to an Area Source
　　2.3.2 Radiosity and Exitance
　　2.3.3 An Interreflection Model
　　2.3.4 Qualitative Properties of Interreflections
　2.4 Shape from One Shaded Image
　2.5 Notes
3 Color
　3.1 Human Color Perception
　　3.1.1 Color Matching
　　3.1.2 Color Receptors
　3.2 The Physics of Color
　　3.2.1 The Color of Light Sources
　　3.2.2 The Color of Surfaces
　3.3 Representing Color
　　3.3.1 Linear Color Spaces
　　3.3.2 Non-linear Color Spaces
　3.4 AModel of Image Color
　　3.4.1 The Diffuse Term
　　3.4.2 The Specular Term
　3.5 Inference from Color
　　3.5.1 Finding Specularities Using Color
　　3.5.2 Shadow Removal Using Color
　　3.5.3 Color Constancy: Surface Color from Image Color
　3.6 Notes
II EARLY VISION: JUST ONE IMAGE
4 Linear Filters
　4.1 Linear Filters and Convolution
　　4.1.1 Convolution
　4.2 Shift Invariant Linear Systems
　　4.2.1 Discrete Convolution
　　4.2.2 Continuous Convolution
　　4.2.3 Edge Effects in Discrete Convolutions
　4.3 Spatial Frequency and Fourier Transforms
　　4.3.1 Fourier Transforms
　4.4 Sampling and Aliasing
　　4.4.1 Sampling
　　4.4.2 Aliasing
　　4.4.3 Smoothing and Resampling
　4.5 Filters as Templates
　　4.5.1 Convolution as a Dot Product
　　4.5.2 Changing Basis
　4.6 Technique: Normalized Correlation and Finding Patterns
　　4.6.1 Controlling the Television by Finding Hands by
Normalized
　　Correlation
　4.7 Technique: Scale and Image Pyramids
　　4.7.1 The Gaussian Pyramid
　　4.7.2 Applications of Scaled Representations
　4.8 Notes
5 Local Image Features
　5.1 Computing the Image Gradient
　5.1.1 Derivative of Gaussian Filters
　5.2 Representing the Image Gradient
　　5.2.1 Gradient-Based Edge Detectors
　　5.2.2 Orientations
　5.3 Finding Corners and Building Neighborhoods
　　5.3.1 Finding Corners
　　5.3.2 Using Scale and Orientation to Build a Neighborhood
　5.4 Describing Neighborhoods with SIFT and HOG Features
　　5.4.1 SIFT Features
　　5.4.2 HOG Features
　5.5 Computing Local Features in Practice
　5.6 Notes
6 Texture
　6.1 Local Texture Representations Using Filters
　　6.1.1 Spots and Bars
　　6.1.2 From Filter Outputs to Texture Representation
　　6.1.3 Local Texture Representations in Practice
　6.2 Pooled Texture Representations by Discovering Textons
　　6.2.1 Vector Quantization and Textons
　　6.2.2 K-means Clustering for Vector Quantization
　6.3 Synthesizing Textures and Filling Holes in Images
　　6.3.1 Synthesis by Sampling Local Models
　　6.3.2 Filling in Holes in Images
　6.4 Image Denoising
　　6.4.1 Non-local Means
　　6.4.2 Block Matching 3D (BM3D)
　　6.4.3 Learned Sparse Coding
　　6.4.4 Results
　6.5 Shape from Texture
　　6.5.1 Shape from Texture for Planes
　　6.5.2 Shape from Texture for Curved Surfaces
　6.6 Notes
III EARLY VISION: MULTIPLE IMAGES
7 Stereopsis
　7.1 Binocular Camera Geometry and the Epipolar Constraint
　　7.1.1 Epipolar Geometry
　　7.1.2 The Essential Matrix
　　7.1.3 The Fundamental Matrix
　7.2 Binocular Reconstruction
　　7.2.1 Image Rectification
　7.3 Human Stereopsis
　7.4 Local Methods for Binocular Fusion
　　7.4.1 Correlation
　　7.4.2 Multi-Scale Edge Matching
　7.5 Global Methods for Binocular Fusion
　　7.5.1 Ordering Constraints and Dynamic Programming
　　7.5.2 Smoothness and Graphs
　7.6 Using More Cameras
　7.7 Application: Robot Navigation
　7.8 Notes
8 Structure from Motion
　8.1 Internally Calibrated Perspective Cameras
　　8.1.1 Natural Ambiguity of the Problem
　　8.1.2 Euclidean Structure and Motion from Two Images
　　8.1.3 Euclidean Structure and Motion from Multiple Images
　8.2 Uncalibrated Weak-Perspective Cameras
　　8.2.1 Natural Ambiguity of the Problem
　　8.2.2 Affine Structure and Motion from Two Images
　　8.2.3 Affine Structure and Motion from Multiple Images
　　8.2.4 From Affine to Euclidean Shape
　8.3 Uncalibrated Perspective Cameras
　　8.3.1 Natural Ambiguity of the Problem
　　8.3.2 Projective Structure and Motion from Two Images
　　8.3.3 Projective Structure and Motion from Multiple Images
　　8.3.4 From Projective to Euclidean Shape
　8.4 Notes
IV MID-LEVEL VISION
9 Segmentation by Clustering
　9.1 Human Vision: Grouping and Gestalt
　9.2 Important Applications
　　9.2.1 Background Subtraction
　　9.2.2 Shot Boundary Detection
　　9.2.3 Interactive Segmentation
　　9.2.4 Forming Image Regions
　9.3 Image Segmentation by Clustering Pixels
　　9.3.1 Basic Clustering Methods
　　9.3.2 The Watershed Algorithm
　　9.3.3 Segmentation Using K-means
　　9.3.4 Mean Shift: Finding Local Modes in Data
　　9.3.5 Clustering and Segmentation with Mean Shift
　9.4 Segmentation, Clustering, and Graphs
　　9.4.1 Terminology and Facts for Graphs
　　9.4.2 Agglomerative Clustering with a Graph
　　9.4.3 Divisive Clustering with a Graph
　　9.4.4 Normalized Cuts
　9.5 Image Segmentation in Practice
　　9.5.1 Evaluating Segmenters
　9.6 Notes
10 Grouping and Model Fitting
　10.1 The Hough Transform
　　10.1.1 Fitting Lines with the Hough Transform
　　10.1.2 Using the Hough Transform
　10.2 Fitting Lines and Planes
　　10.2.1 Fitting a Single Line
　　10.2.2 Fitting Planes
　　10.2.3 Fitting Multiple Lines
　10.3 Fitting Curved Structures
　10.4 Robustness
　　10.4.1 M-Estimators
　　10.4.2 RANSAC: Searching for Good Points
　10.5 Fitting Using Probabilistic Models
　　10.5.1 Missing Data Problems
　　10.5.2 Mixture Models and Hidden Variables
　　10.5.3 The EM Algorithm for Mixture Models
　　10.5.4 Difficulties with the EM Algorithm
　10.6 Motion Segmentation by Parameter Estimation
　　10.6.1 Optical Flow and Motion
　　10.6.2 Flow Models
　　10.6.3 Motion Segmentation with Layers
　10.7 Model Selection: Which Model Is the Best Fit?
　　10.7.1 Model Selection Using Cross-Validation
　10.8 Notes
11 Tracking
　11.1 Simple Tracking Strategies
　　11.1.1 Tracking by Detection
　　11.1.2 Tracking Translations by Matching
　　11.1.3 Using Affine Transformations to Confirm a Match
　11.2 Tracking Using Matching
　　11.2.1 Matching Summary Representations
　　11.2.2 Tracking Using Flow
　11.3 Tracking Linear Dynamical Models with Kalman Filters
　　11.3.1 Linear Measurements and Linear Dynamics
　　11.3.2 The Kalman Filter
　　11.3.3 Forward-backward Smoothing
　11.4 Data Association
　　11.4.1 Linking Kalman Filters with Detection Methods
　　11.4.2 Key Methods of Data Association
　11.5 Particle Filtering
　　11.5.1 Sampled Representations of Probability Distributions
　　11.5.2 The Simplest Particle Filter
　　11.5.3 The Tracking Algorithm
　　11.5.4 A Workable Particle Filter
　　11.5.5 Practical Issues in Particle Filters
　11.6 Notes
V HIGH-LEVEL VISION
12 Registration
　12.1 Registering Rigid Objects
　　12.1.1 Iterated Closest Points
　　12.1.2 Searching for Transformations via Correspondences
　　12.1.3 Application: Building Image Mosaics
　12.2 Model-based Vision: Registering Rigid Objects with
Projection
　　12.2.1 Verification: Comparing Transformed and Rendered
Source
　　to Target
　12.3 Registering Deformable Objects
　　12.3.1 Deforming Texture with Active Appearance Models
　　12.3.2 Active Appearance Models in Practice
　　12.3.3 Application: Registration in Medical Imaging Systems
　12.4 Notes
13 Smooth Surfaces and Their Outlines
　13.1 Elements of Differential Geometry
　　13.1.1 Curves
　　13.1.2 Surfaces
　13.2 Contour Geometry
　　13.2.1 The Occluding Contour and the Image Contour
　　13.2.2 The Cusps and Inflections of the Image Contour
　　13.2.3 Koenderink’s Theorem
　13.3 Visual Events: More Differential Geometry
　　13.3.1 The Geometry of the Gauss Map
　　13.3.2 Asymptotic Curves
　　13.3.3 The Asymptotic Spherical Map
　　13.3.4 Local Visual Events
　　13.3.5 The Bitangent Ray Manifold
　　13.3.6 Multilocal Visual Events
　　13.3.7 The Aspect Graph
　13.4 Notes
14 Range Data
　14.1 Active Range Sensors
　14.2 Range Data Segmentation
　　14.2.1 Elements of Analytical Differential Geometry
　　14.2.2 Finding Step and Roof Edges in Range Images
　　14.2.3 Segmenting Range Images into Planar Regions
　14.3 Range Image Registration and Model Acquisition
　　14.3.1 Quaternions
　　14.3.2 Registering Range Images
　　14.3.3 Fusing Multiple Range Images
　14.4 Object Recognition
　　14.4.1 Matching Using Interpretation Trees
　　14.4.2 Matching Free-Form Surfaces Using Spin Images
　14.5 Kinect
　　14.5.1 Features
　　14.5.2 Technique: Decision Trees and Random Forests
　　14.5.3 Labeling Pixels
　　14.5.4 Computing Joint Positions
　14.6 Notes
15 Learning to Classify
　15.1 Classification, Error, and Loss
　　15.1.1 Using Loss to Determine Decisions
　　15.1.2 Training Error, Test Error, and Overfitting
　　15.1.3 Regularization
　　15.1.4 Error Rate and Cross-Validation
　　15.1.5 Receiver Operating Curves
　15.2 Major Classification Strategies
　　15.2.1 Example: Mahalanobis Distance
　　15.2.2 Example: Class-Conditional Histograms and Naive
Bayes
　　15.2.3 Example: Classification Using Nearest Neighbors
　　15.2.4 Example: The Linear Support Vector Machine
　　15.2.5 Example: Kernel Machines
　　15.2.6 Example: Boosting and Adaboost
　15.3 Practical Methods for Building Classifiers
　　15.3.1 Manipulating Training Data to Improve Performance
　　15.3.2 Building Multi-Class Classifiers Out of Binary
Classifiers
　　15.3.3 Solving for SVMS and Kernel Machines
　15.4 Notes
16 Classifying Images
　16.1 Building Good Image Features
　　16.1.1 Example Applications
　　16.1.2 Encoding Layout with GIST Features
　　16.1.3 Summarizing Images with Visual Words
　　16.1.4 The Spatial Pyramid Kernel
　　16.1.5 Dimension Reduction with Principal Components
　　16.1.6 Dimension Reduction with Canonical Variates
　　16.1.7 Example Application: Identifying Explicit Images
　　16.1.8 Example Application: Classifying Materials
　　16.1.9 Example Application: Classifying Scenes
　16.2 Classifying Images of Single Objects
　　16.2.1 Image Classification Strategies
　　16.2.2 Evaluating Image Classification Systems
　　16.2.3 Fixed Sets of Classes
　　16.2.4 Large Numbers of Classes
　　16.2.5 Flowers, Leaves, and Birds: Some Specialized
Problems
　16.3 Image Classification in Practice
　　16.3.1 Codes for Image Features
　　16.3.2 Image Classification Datasets
　　16.3.3 Dataset Bias
　　16.3.4 Crowdsourcing Dataset Collection
　16.4 Notes
17 Detecting Objects in Images
　17.1 The Sliding Window Method
　　17.1.1 Face Detection
　　17.1.2 Detecting Humans
　　17.1.3 Detecting Boundaries
　17.2 Detecting Deformable Objects
　17.3 The State of the Art of Object Detection
　　17.3.1 Datasets and Resources
　17.4 Notes
18 Topics in Object Recognition
　18.1 What Should Object Recognition Do?
　　18.1.1 What Should an Object Recognition System Do?
　　18.1.2 Current Strategies for Object Recognition
　　18.1.3 What Is Categorization?
　　18.1.4 Selection: What Should Be Described?
　18.2 Feature Questions
　　18.2.1 Improving Current Image Features
　　18.2.2 Other Kinds of Image Feature
　18.3 Geometric Questions
　18.4 Semantic Questions
　　18.4.1 Attributes and the Unfamiliar
　　18.4.2 Parts, Poselets and Consistency
　　18.4.3 Chunks of Meaning
VI APPLICATIONS AND TOPICS
19 Image-Based Modeling and Rendering
　19.1 Visual Hulls
　　19.1.1 Main Elements of the Visual Hull Model
　　19.1.2 Tracing Intersection Curves
　　19.1.3 Clipping Intersection Curves
　　19.1.4 Triangulating Cone Strips
　　19.1.5 Results
　　19.1.6 Going Further: Carved Visual Hulls
　19.2 Patch-Based Multi-View Stereopsis
　　19.2.1 Main Elements of the PMVS Model
　　19.2.2 Initial Feature Matching
　　19.2.3 Expansion
　　19.2.4 Filtering
　　19.2.5 Results
　19.3 The Light Field
　19.4 Notes
20 Looking at People
　20.1 HMM’s, Dynamic Programming, and Tree-Structured Models
　　20.1.1 Hidden Markov Models
　　20.1.2 Inference for an HMM
　　20.1.3 Fitting an HMM with EM
　　20.1.4 Tree-Structured Energy Models
　20.2 Parsing People in Images
　　20.2.1 Parsing with Pictorial Structure Models
　　20.2.2 Estimating the Appearance of Clothing
　20.3 Tracking People
　　20.3.1 Why Human Tracking Is Hard
　　20.3.2 Kinematic Tracking by Appearance
　　20.3.3 Kinematic Human Tracking Using Templates
　20.4 3D from 2D: Lifting
　　20.4.1 Reconstruction in an Orthographic View
　　20.4.2 Exploiting Appearance for Unambiguous
Reconstructions
　　20.4.3 Exploiting Motion for Unambiguous Reconstructions
　20.5 Activity Recognition
　　20.5.1 Background: Human Motion Data
　　20.5.2 Body Configuration and Activity Recognition
　　20.5.3 Recognizing Human Activities with Appearance
Features
　　20.5.4 Recognizing Human Activities with Compositional
Models
　20.6 Resources
　20.7 Notes
21 Image Search and Retrieval
　21.1 The Application Context
　　21.1.1 Applications
　　21.1.2 User Needs
　　21.1.3 Types of Image Query
　　21.1.4 What Users Do with Image Collections
　21.2 Basic Technologies from Information Retrieval
　　21.2.1 Word Counts
　　21.2.2 Smoothing Word Counts
　　21.2.3 Approximate Nearest Neighbors and Hashing
　　21.2.4 Ranking Documents
　21.3 Images as Documents
　　21.3.1 Matching Without Quantization
　　21.3.2 Ranking Image Search Results
　　21.3.3 Browsing and Layout
　　21.3.4 Laying Out Images for Browsing
　21.4 Predicting Annotations for Pictures
　　21.4.1 Annotations from Nearby Words
　　21.4.2 Annotations from the Whole Image
　　21.4.3 Predicting Correlated Words with Classifiers
　　21.4.4 Names and Faces
　　21.4.5 Generating Tags with Segments
　21.5 The State of the Art of Word Prediction
　　21.5.1 Resources
　　21.5.2 Comparing Methods
　　21.5.3 Open Problems
　　21.6 Notes
VII BACKGROUND MATERIAL
22 Optimization Techniques
　22.1 Linear Least-Squares Methods
　　22.1.1 Normal Equations and the Pseudoinverse
　　22.1.2 Homogeneous Systems and Eigenvalue Problems
　　22.1.3 Generalized Eigenvalues Problems
　　22.1.4 An Example: Fitting a Line to Points in a Plane
　　22.1.5 Singular Value Decomposition
　22.2 Nonlinear Least-Squares Methods
　　22.2.1 Newton’s Method: Square Systems of Nonlinear
Equations.
　　22.2.2 Newton’s Method for Overconstrained Systems
　　22.2.3 The Gauss—Newton and Levenberg—Marquardt Algorithms
　22.3 Sparse Coding and Dictionary Learning
　　22.3.1 Sparse Coding
　　22.3.2 Dictionary Learning
　　22.3.3 Supervised Dictionary Learning
　22.4 Min-Cut/Max-Flow Problems and Combinatorial
Optimization
　　22.4.1 Min-Cut Problems
　　22.4.2 Quadratic Pseudo-Boolean Functions
　　22.4.3 Generalization to Integer Variables
　22.5 Notes
　　Bibliography
Index
List of Algorithms

章节摘录

版权页：   插图：    Inference from Shading Registered images are not essential for radiometric calibration. For example, it is sufficient to have two images where we believe the histogram of Eij values is the same （Grossberg and Nayar 2002）. This occurs, for example, when the images are of the same scene, but are not precisely registered. Patterns of intensity around edges also can reveal calibration （Lin et al. 2004）. There has not been much recent study of lightness constancy algorithms. The basic idea is due to Land and McCann （1971）.Their work was formalized for the computer vision community by Horn （1974）. A variation on Horn's algorithm was constructed by Blake （1985）. This is the lightness algorithm we describe. It appeared originally in a slightly different form, where it was called the Retinex algorithm （Land and McCann 1971）. Retinex was originally intended as a color constancy algorithm. It is surprisingly difficult to analyze （Brainard and Wandell 1986）. Retinex estimates the log-illumination term by subtracting the log-albedo from the log-intensity. This has the disadvantage that we do not impose any struc- tural eonstraints on illumination. This point has largely been ignored, beeause the main focus has been on albedo estimates. However, albedo estimates are likely to be improved by balancing violations of albedo eonstraints with those of illumination constraints. Lightness techniques are not as widely used as they should be, particularly given that there is some evidence that they produce useful information on real images （Brelstaff and Blake 1987）. Classifying illumination versus albedo simply by looking at the magnitude of the gradient is crude, and ignores important cues. Sharp shading changes occur at shadow boundaries or normal discontinuities, but using chromaticity （Funt et al. 1992） or multiple images under different lighting conditions （Weiss 20011 yields improved estimates. One can learn to distinguish illumination from albedo （Freeman et al. 2000）. Discriminative methods to classify edges into albedo or shading help （Tappen et al. 2006b） and chromaticity cues can contribute （Farenzena and Fusiello 2007）.

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《计算机视觉:一种现代方法(第2版)(英文版)》可作为高等院校计算几何、计算机图形学、图像处理、机器人学等专业学生的教材，也可供相关的专业人士阅读。

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