Visual Perception Through Video Imagery

For several decades researchers have tried to construct perception
systems based on the registration data from video cameras. This
work has produced various tools that have made recent advances
possible in this area.

Part 1 of this book deals with the problem of the calibration
and auto-calibration of video captures. Part 2 is essentially
concerned with the estimation of the relative object/capture
position when a priori information is introduced (the CAD model of
the object). Finally, Part 3 discusses the inference of density
information and the shape recognition in images.

Auteur
Michel Dhome, is Research Director at the CNRS and is a Professor at the University of Clermont-Ferrand, France.

Résumé
For several decades researchers have tried to construct perception systems based on the registration data from video cameras. This work has produced various tools that have made recent advances possible in this area. Part 1 of this book deals with the problem of the calibration and auto-calibration of video captures. Part 2 is essentially concerned with the estimation of the relative object/capture position when a priori information is introduced (the CAD model of the object). Finally, Part 3 discusses the inference of density information and the shape recognition in images.

Contenu

Introduction 13

Part 1 17

Chapter 1. Calibration of Vision Sensors 19
Jean-Marc LAVEST and Gérard RIVES

1.1. Introduction 19

1.2. General formulation of the problem of calibration 20

1.2.1. Formulation of the problem 20

1.2.1.1. Modeling the camera and lens: pin-hole model 22

1.2.1.2. Formation of images: perspective projection 22

1.2.1.3. Changing lens/camera reference point 23

1.2.1.4. Changing of the camera/image point 24

1.2.1.5. Changing of coordinates in the image plane 24

1.2.2. General expression 25

1.2.2.1. General formulation of the problem of calibration 27

1.3. Linear approach 27

1.3.1. Principle 27

1.3.2. Notes and comments 29

1.4. Non-linear photogrammetric approach 30

1.4.1. Mathematic model 31

1.4.2. Solving the problem 34

1.4.3. Multi-image calibration 35

1.4.4. Self-calibration by bundle adjustment 36

1.4.4.1. Redefinition of the problem 36

1.4.4.2. Estimation of redundancy 37

1.4.4.3. Solution for a near scale factor 37

1.4.4.4. Initial conditions 38

1.4.5. Precision calculation 38

1.5. Results of experimentation 39

1.5.1. Bundle adjustment for a traditional lens 39

1.5.1.1. Initial and experimental conditions 39

1.5.1.2. Sequence of classic images 40

1.5.2. Specific case of fish-eye lenses 42

1.5.2.1. Traditional criterion 43

1.5.2.2. Zero distortion at r0 43

1.5.2.3. Normalization of distortion coefficients 44

1.5.2.4. Experiments 45

1.5.3. Calibration of underwater cameras 48

1.5.3.1. Theoretical notes 48

1.5.3.2. Experiments .49

1.5.3.3. The material 49

1.5.3.4. Results in air 49

1.5.3.5. Calibration in water 50

1.5.3.6. Relation between the calibration in air and in water 53

1.5.4. Calibration of zooms 55

1.5.4.1. Recalling optical properties 55

1.5.4.2. Estimate of the principal point 56

1.5.4.3. Experiments 57

1.6. Bibliography 58

Chapter 2. Self-Calibration of Video Sensors 61
Rachid DERICHE

2.1. Introduction 61

2.2. Reminder and notation 64

2.3. Huang-Faugeras constraints and Trivedi's equations 66

2.3.1. Huang-Faugeras constraints 66

2.3.2. Trivedi's constraints 67

2.3.3. Discussion 68

2.4. Kruppa equations 68

2.4.1. Geometric derivation of Kruppa equations 68

2.4.2. An algebraic derivation of Kruppa equations 70

2.4.3. Simplified Kruppa equations 72

2.5. Implementation 74

2.5.1. The choice of initial conditions 74

2.5.2. Optimization 75

2.6. Experimental results 76

2.6.1. Estimation of angles and length ratios from images 77

2.6.2. Experiments with synthetic data 78

2.6.3. Experiments with real data 79

2.7. Conclusion 85

2.8. Acknowledgement 87

2.9. Bibliography 87

Chapter 3. Specific Displacements for Self-calibration 91
Diane LINGRAND, François GASPARD and Thierry VIÉVILLE

3.1. Introduction: interest to resort to specific movements 91

3.2. Modeling: parametrization of specific models 93

3.2.1. Specific projection models 93

3.2.2. Specifications of internal parameters of the camera 96

3.2.3. Taking into account specific displacements 97

3.2.4. Relation with specific properties in the scene 100

3.3. Self-calibration of a camera 100

3.3.1. Usage of pure rotations or points at the horizon 103

3.3.2. Pure rotation and fixed parameters 104

3.3.3. Rotation around a fixed axis 106

3.4. Perception of depth 108

3.4.1. Usage of pure translations 108 3.4.2. Retinal movements 111<...