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)
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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 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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