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Computer Vision – A Modern Approach

Computer

Vision

机器视觉

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Computer Vision – A Modern Approach 机器视觉-一种现代方法英文版电子版仅供预览及学习交流使用，下载后请24小时内删除，支持正版，喜欢的请购买正版书籍。

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CONTENTS

IIMAGEFORMATION 1

1 RADIOMETRY — MEASURING LIGHT 3

1.1 Light in Space 3

1.1.1 Foreshortening 3

1.1.2 Solid Angle 4

1.1.3 Radiance 6

1.2 Light at Surfaces 8

1.2.1 Simplifying Assumptions 9

1.2.2 The Bidirectional Reﬂectance Distribution Function 9

1.3 Important Special Cases 11

1.3.1 Radiosity 11

1.3.2 Directional Hemispheric Reﬂectance 12

1.3.3 Lambertian Surfaces and Albedo 12

1.3.4 Specular Surfaces 13

1.3.5 The Lambertian + Specular Model 14

1.4 Quick Reference: Radiometric Terminology for Light 16

1.5 Quick Reference: Radiometric Properties of Surfaces 17

1.6 Quick Reference: Important Types of Surface 18

1.7 Notes 19

1.8 Assignments 19

2 SOURCES, SHADOWS AND SHADING 21

2.1 Radiometric Properties of Light Sources 21

2.2 Qualitative Radiometry 22

2.3 Sources and their Eﬀects 23

2.3.1 Point Sources 24

2.3.2 Line Sources 26

2.3.3 Area Sources 27

2.4 Local Shading Models 28

2.4.1 Local Shading Models for Point Sources 28

2.4.2 Area Sources and their Shadows 31

2.4.3 Ambient Illumination 31

2.5 Application: Photometric Stereo 33

2.5.1 Normal and Albedo from Many Views 36

2.5.2 Shape from Normals 37

2.6 Interreﬂections: Global Shading Models 40

2.6.1 An Interreﬂection Model 42

2.6.2 Solving for Radiosity 43

2.6.3 The qualitative eﬀects of interreﬂections 45

2.7 Notes 47

2.8 Assignments 50

2.8.1 Exercises 50

2.8.2 Programming Assignments 51

3 COLOUR 53

3.1 The Physics of Colour 53

3.1.1 Radiometry for Coloured Lights: Spectral Quantities 53

3.1.2 The Colour of Surfaces 54

3.1.3 The Colour of Sources 55

3.2 Human Colour Perception 58

3.2.1 Colour Matching 58

3.2.2 Colour Receptors 61

3.3 Representing Colour 63

3.3.1 Linear Colour Spaces 63

3.3.2 Non-linear Colour Spaces 68

3.3.3 Spatial and Temporal Eﬀects 73

3.4 Application: Finding Specularities 73

3.5 Surface Colour from Image Colour 77

3.5.1 Surface Colour Perception in People 77

3.5.2 Inferring Lightness 80

3.5.3 A Model for Image Colour 83

3.5.4 Surface Colour from Finite Dimensional Linear Models 86

3.6 Notes 89

vii

3.6.1 Trichromacy and Colour Spaces 89

3.6.2 Lightness and Colour Constancy 90

3.6.3 Colour in Recognition 91

3.7 Assignments 91

II IMAGE MODELS 94

4 GEOMETRIC IMAGE FEATURES 96

4.1 Elements of Diﬀerential Geometry 100

4.1.1 Curves 100

4.1.2 Surfaces 105

Application: The shape of specularities 109

4.2 Contour Geometry 112

4.2.1 The Occluding Contour and the Image Contour 113

4.2.2 The Cusps and Inﬂections of the Image Contour 114

4.2.3 Koenderink’s Theorem 115

4.3 Notes 117

4.4 Assignments 118

5 ANALYTICAL IMAGE FEATURES 120

5.1 Elements of Analytical Euclidean Geometry 120

5.1.1 Coordinate Systems and Homogeneous Coordinates 121

5.1.2 Coordinate System Changes and Rigid Transformations 124

5.2 Geometric Camera Parameters 129

5.2.1 Intrinsic Parameters 129

5.2.2 Extrinsic Parameters 132

5.2.3 A Characterization of Perspective Projection Matrices 132

5.3 Calibration Methods 133

5.3.1 A Linear Approach to Camera Calibration 134

Technique: Linear Least Squares Methods 135

5.3.2 Taking Radial Distortion into Account 139

5.3.3 Using Straight Lines for Calibration 140

5.3.4 Analytical Photogrammetry 143

Technique: Non-Linear Least Squares Methods 145

5.4 Notes 147

5.5 Assignments 147

viii

6 AN INTRODUCTION TO PROBABILITY 150

6.1 Probability in Discrete Spaces 151

6.1.1 Probability: the P-function 151

6.1.2 Conditional Probability 153

6.1.3 Choosing P 153

6.2 Probability in Continuous Spaces 159

6.2.1 Event Structures for Continuous Spaces 159

6.2.2 Representing a P-function for the Real Line 160

6.2.3 Probability Densities 161

6.3 Random Variables 161

6.3.1 Conditional Probability and Independence 162

6.3.2 Expectations 163

6.3.3 Joint Distributions and Marginalization 165

6.4 Standard Distributions and Densities 165

6.4.1 The Normal Distribution 167

6.5 Probabilistic Inference 167

6.5.1 The Maximum Likelihood Principle 168

6.5.2 Priors, Posteriors and Bayes’ rule 170

6.5.3 Bayesian Inference 170

6.5.4 Open Issues 177

6.6 Discussion 178

III EARLY VISION: ONE IMAGE 180

7 LINEAR FILTERS 182

7.1 Linear Filters and Convolution 182

7.1.1 Convolution 182

7.1.2 Example: Smoothing by Averaging 183

7.1.3 Example: Smoothing with a Gaussian 185

7.2 Shift invariant linear systems 186

7.2.1 Discrete Convolution 188

7.2.2 Continuous Convolution 190

7.2.3 Edge Eﬀects in Discrete Convolutions 192

7.3 Spatial Frequency and Fourier Transforms 193

7.3.1 Fourier Transforms 193

7.4 Sampling and Aliasing 197

7.4.1 Sampling 198

7.4.2 Aliasing 201

7.4.3 Smoothing and Resampling 202

7.5 Technique: Scale and Image Pyramids 204

7.5.1 The Gaussian Pyramid 205

7.5.2 Applications of Scaled Representations 206

7.5.3 Scale Space 208

7.6 Discussion 211

7.6.1 Real Imaging Systems vs Shift-Invariant Linear Systems 211

7.6.2 Scale 212

8 EDGE DETECTION 214

8.1 Estimating Derivatives with Finite Diﬀerences 214

8.1.1 Diﬀerentiation and Noise 216

8.1.2 Laplacians and edges 217

8.2 Noise 217

8.2.1 Additive Stationary Gaussian Noise 219

8.3 Edges and Gradient-based Edge Detectors 224

8.3.1 Estimating Gradients 224

8.3.2 Choosing a Smoothing Filter 225

8.3.3 Why Smooth with a Gaussian? 227

8.3.4 Derivative of Gaussian Filters 229

8.3.5 Identifying Edge Points from Filter Outputs 230

8.4 Commentary 234

9 FILTERS AND FEATURES 237

9.1 Filters as Templates 237

9.1.1 Convolution as a Dot Product 237

9.1.2 Changing Basis 238

9.2 Human Vision: Filters and Primate Early Vision 239

9.2.1 The Visual Pathway 239

9.2.2 How the Visual Pathway is Studied 241

9.2.3 The Response of Retinal Cells 241

9.2.4 The Lateral Geniculate Nucleus 242

9.2.5 The Visual Cortex 243

9.2.6 A Model of Early Spatial Vision 246

9.3 Technique: Normalised Correlation and Finding Patterns 248

9.3.1 Controlling the Television by Finding Hands by Normalised

Correlation 248

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