Frosyth, Ponce, Computer Vision A Modern Approach.pdf

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CONTENTS
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IMAGE FORMATION
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1 RADIOMETRY — MEASURING LIGHT
1.1 Light in Space
1.1.1 Foreshortening
1.1.2 Solid Angle
1.1.3 Radiance
1.2 Light at Surfaces
1.2.1 Simplifying Assumptions
1.2.2 The Bidirectional Reflectance Distribution Function
1.3 Important Special Cases
1.3.1 Radiosity
1.3.2 Directional Hemispheric Reflectance
1.3.3 Lambertian Surfaces and Albedo
1.3.4 Specular Surfaces
1.3.5 The Lambertian + Specular Model
1.4 Quick Reference: Radiometric Terminology for Light
1.5 Quick Reference: Radiometric Properties of Surfaces
1.6 Quick Reference: Important Types of Surface
1.7 Notes
1.8 Assignments
2 SOURCES, SHADOWS AND SHADING
2.1 Radiometric Properties of Light Sources
2.2 Qualitative Radiometry
2.3 Sources and their Effects
2.3.1 Point Sources
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2.3.2 Line Sources
2.3.3 Area Sources
Local Shading Models
2.4.1 Local Shading Models for Point Sources
2.4.2 Area Sources and their Shadows
2.4.3 Ambient Illumination
Application: Photometric Stereo
2.5.1 Normal and Albedo from Many Views
2.5.2 Shape from Normals
Interreflections: Global Shading Models
2.6.1 An Interreflection Model
2.6.2 Solving for Radiosity
2.6.3 The qualitative effects of interreflections
Notes
Assignments
2.8.1 Exercises
2.8.2 Programming Assignments
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2.4
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2.6
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3 COLOUR
3.1 The Physics of Colour
3.1.1 Radiometry for Coloured Lights: Spectral Quantities
3.1.2 The Colour of Surfaces
3.1.3 The Colour of Sources
3.2 Human Colour Perception
3.2.1 Colour Matching
3.2.2 Colour Receptors
3.3 Representing Colour
3.3.1 Linear Colour Spaces
3.3.2 Non-linear Colour Spaces
3.3.3 Spatial and Temporal Effects
3.4 Application: Finding Specularities
3.5 Surface Colour from Image Colour
3.5.1 Surface Colour Perception in People
3.5.2 Inferring Lightness
3.5.3 A Model for Image Colour
3.5.4 Surface Colour from Finite Dimensional Linear Models
3.6 Notes
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3.6.1 Trichromacy and Colour Spaces
3.6.2 Lightness and Colour Constancy
3.6.3 Colour in Recognition
Assignments
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3.7
II
IMAGE MODELS
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4 GEOMETRIC IMAGE FEATURES
4.1 Elements of Differential Geometry
4.1.1 Curves
4.1.2 Surfaces
Application: The shape of specularities
4.2 Contour Geometry
4.2.1 The Occluding Contour and the Image Contour
4.2.2 The Cusps and Inflections of the Image Contour
4.2.3 Koenderink’s Theorem
4.3 Notes
4.4 Assignments
5 ANALYTICAL IMAGE FEATURES
5.1 Elements of Analytical Euclidean Geometry
5.1.1 Coordinate Systems and Homogeneous Coordinates
5.1.2 Coordinate System Changes and Rigid Transformations
5.2 Geometric Camera Parameters
5.2.1 Intrinsic Parameters
5.2.2 Extrinsic Parameters
5.2.3 A Characterization of Perspective Projection Matrices
5.3 Calibration Methods
5.3.1 A Linear Approach to Camera Calibration
Technique: Linear Least Squares Methods
5.3.2 Taking Radial Distortion into Account
5.3.3 Using Straight Lines for Calibration
5.3.4 Analytical Photogrammetry
Technique: Non-Linear Least Squares Methods
5.4 Notes
5.5 Assignments
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6 AN INTRODUCTION TO PROBABILITY
6.1 Probability in Discrete Spaces
6.1.1 Probability: the P-function
6.1.2 Conditional Probability
6.1.3 Choosing P
6.2 Probability in Continuous Spaces
6.2.1 Event Structures for Continuous Spaces
6.2.2 Representing a P-function for the Real Line
6.2.3 Probability Densities
6.3 Random Variables
6.3.1 Conditional Probability and Independence
6.3.2 Expectations
6.3.3 Joint Distributions and Marginalization
6.4 Standard Distributions and Densities
6.4.1 The Normal Distribution
6.5 Probabilistic Inference
6.5.1 The Maximum Likelihood Principle
6.5.2 Priors, Posteriors and Bayes’ rule
6.5.3 Bayesian Inference
6.5.4 Open Issues
6.6 Discussion
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III
EARLY VISION: ONE IMAGE
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7 LINEAR FILTERS
7.1 Linear Filters and Convolution
7.1.1 Convolution
7.1.2 Example: Smoothing by Averaging
7.1.3 Example: Smoothing with a Gaussian
7.2 Shift invariant linear systems
7.2.1 Discrete Convolution
7.2.2 Continuous Convolution
7.2.3 Edge Effects in Discrete Convolutions
7.3 Spatial Frequency and Fourier Transforms
7.3.1 Fourier Transforms
7.4 Sampling and Aliasing
7.4.1 Sampling

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