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| 书名 | 计算机视觉--一种现代方法(第2版英文版)/国外计算机科学教材系列 |
| 分类 | 教育考试-考试-计算机类 |
| 作者 | (美)福赛斯//泊斯 |
| 出版社 | 电子工业出版社 |
| 下载 |  |
| 简介 | 编辑推荐 《计算机视觉--一种现代方法(第2版英文版)/国外计算机科学教材系列》编著者福赛斯。 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。 内容推荐 《计算机视觉--一种现代方法(第2版英文版)/国外计算机科学教材系列》编著者福赛斯。 《计算机视觉--一种现代方法(第2版英文版)/国外计算机科学教材系列》内容提要：计算机视觉是借助于几何、物理和学习理论来建立模型，从而使用统计方法来处理数据的一种方-法。本书是近年较为成功的一本计算机视觉教材，内容涉及几何摄像机模型、光照与着色、彩色、线性滤波器、局部图像特性、纹理、立体视觉、从运动求取结构、聚类分割、组合与模型拟台、跟踪、配准、平滑曲面及其轮廓、距离数据、分类、图像分类、图像目标检测、目标识别专题、基于图像的建模与渲染、图像中人的研究、图像搜索与检索、优化技术等。全书条理清楚，系统性强，且各章相对独立；此外，全书理论联系实际，并纳入了近年来该领域的最新研究成果。 本书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。 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 |
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