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计算机视觉:一种现代方法(第二版 英文版)

计算机视觉:一种现代方法(第二版 英文版)

作者:(美)福赛斯 等著

出版社:电子工业出版社

出版时间:2012-05-01

ISBN:9787121168307

定价:¥95.00

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内容简介
  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
暂缺《计算机视觉:一种现代方法(第二版 英文版)》作者简介
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 byNormalized
  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 withProjection
  12.2.1 Verification: Comparing Transformed and RenderedSource
  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 NaiveBayes
  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 BinaryClassifiers
  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 SpecializedProblems
 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 UnambiguousReconstructions
  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 AppearanceFeatures
  20.5.4 Recognizing Human Activities with CompositionalModels
 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 NonlinearEquations.
  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 CombinatorialOptimization
  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 andother 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 byNormalized
  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 withProjection
  12.2.1 Verification: Comparing Transformed and RenderedSource
  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 NaiveBayes
  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 BinaryClassifiers
  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 SpecializedProblems
 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 UnambiguousReconstructions
  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 AppearanceFeatures
  20.5.4 Recognizing Human Activities with CompositionalModels
 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 NonlinearEquations.
  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 CombinatorialOptimization
  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
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