计算机视觉
2012-5
电子工业出版社
福赛斯(David A. Forsyth),泊斯(Jean Ponce)
761
1268000
David A.Forsyth
无
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：（美国）福赛斯（David A. Forsyth） （美国）泊斯（Jean Ponce）
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
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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
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）.
《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。
无
计算机视觉方面的经典著作，第二版比第一版有较多改进，且反映了近年的新进展，是每个研究图像处理，分析，识别等技术的必备书籍，最新文献到2011年的计算机视觉3大会议。
计算机视觉方面的参考书
很不错的一本书，值得细细学习研究，
大致翻看了一下，经典啊
书很详细，物流很好
我的第一本历史探险漫画书寻宝记全套1－20
正在看，写的挺好的！！！！！11
有网络资料支持，非常不错。大师级的视野，思想的盛宴。
MIT的经典教材，内容丰富，和《图像处理、分析与机器视觉》配合着看，收获良多
计算机视觉的经典著作，内容全面。
很经典的一本计算机视觉教材
虽然有电子版的了，但还是想要一本纸质的，感谢电子工业出版社第一时间影印了这本书。书的内容没说的，值得购买。只是感觉纸张和油墨都不够好，谈不上完美。另外书的定价也偏贵一点，折后能在60左右是比较合适的价格。不过也算瑕不掩瑜了。
非常好的一本书，很适合研究使用。
内容老了些，学学基础倒也可以
很好的图书，好好学习；
本书数学描述方式不通用，看着怪怪的，譬如：卷积表达不直接用连续或者离散求和的方式，而是写成shift（x）这种方式；另外，一些地方cover的range够大，但是只是带一下，书不同于论文，有些地方还是要说清楚，因为不仅仅是买一本论文的参考索引
第一版买不到了，买了这版，感觉质量很一般很一般，纸很薄，不像正品。。
纸质不错。不多说了，学习的话，没什么问题。换句话，再好的书，不看结果就无需在乎书本身了。对了，价格当时买很便宜。英文版啊！
感觉这本书不是很实用，还可以吧
书中详细介绍了各种图像处理的基础知识
很大的一本，挺厚的，还挺重，质量不错