Automatic calibration and reconstruction for active vision systems

Chapter 1 Introduction. 1.1 Vision Framework. 1.2 Background. 1.2.1 Calibrated Reconstruction. 1.2.1.1 Static Calibration based methods. 1.2.1.2 Dynamic Calibration based methods. 1.2.1.3 Relative Pose Problem. 1.2.2 Uncalibrated 3D reconstruction. 1.2.2.1 Factorization-based method. 1.2.2.2 Stratification-based method. 1.2.2.3 Using Structured Light System. 1.3 Scope. 1.3.1 System Calibration. 1.3.2 Plane-based Homography. 1.3.3 Structured Light System. 1.3.4 Omni-directional Vision System. 1.4 Objectives. 1.5 Book Structures. Chapter 2 System Description. 2.1 System Introduction. 2.1.1 Structured Light System. 2.1.2 Omni-directional Vision System. 2.2 Component Modeling. 2.2.1 Convex Mirror. 2.2.2 Camera Model. 2.2.3 Projector Model. 2.3 Pattern Coding Strategy. 2.3.1 Introduction. 2.3.2 Color-Encoded Light Pattern. 2.3.3 Decoding the Light Pattern. 2.4 Some Preliminaries. 2.4.1 Notations and Definitions. 2.4.2 Cross Ratio. 2.4.3 Plane-based Homography. 2.4.4 Fundamental Matrix. Chapter 3 Static Calibration. 3.1 Calibration Theory. 3.2 Polygon-based Calibration. 3.2.1 Design of the planar pattern. 3.2.2 Solving the vanishing line. 3.2.3 Solving the projection of a circle. 3.2.4 Solving the projection of circular point. 3.2.5 Algorithm. 3.2.6 Discussion. 3.3 Intersectant-Circle-based Calibration. 3.3.1 Planar Pattern Design. 3.3.2 Solution for the circular point. 3.4 Concentric-Circle-based Calibration. 3.4.1 Some Preliminaries. 3.4.2 The polynomial eigenvalue problem. 3.4.3 Orthogonality-based Algorithm. 3.4.4 Experiments. 3.4.4.1 Numerical Simulations. 3.4.4.2 Real Image Experiment. 3.5 Line-based Distortion Correction. 3.5.1 The distortion model. 3.5.2 The correction procedure. 3.5.3 Examples. 3.6 Summary. Chapter 4 Homography-based Dynamic Calibration. 4.1 Problem Statement. 4.2 System Constraints. 4.2.1 Two Propositions. 4.3 Calibration Algorithm. 4.3.1 Solution for the Scale Factor. 4.3.2 Solutions for the Translation Vector. 4.3.3 Solution for Rotation Matrix. 4.3.4 Implementation Procedure. 4.4 Error Analyses. 4.4.1 Errors in the Homographic matrix. 4.4.2 Errors in the translation vector. 4.4.3 Errors in the rotation matrix. 4.5 Experiments Study. 4.5.1 Computer Simulation. 4.5.2 Real Data Experiment. 4.6 Summary. Chapter 5 3D Reconstruction with Image-to-World Transformation. 5.1 Introduction. 5.2 Image-to-World Transformation matrix. 5.3 Two-Known-Plane based method. 5.3.1 Static Calibration. 5.3.2 Determining the on-line Homography. 5.3.3 Euclidean 3D Reconstruction. 5.3.4 Configuration of the two scene planes. 5.3.5 Computational Complexity Study. 5.3.6 Reconstruction Examples. 5.4 One-Known-Plane based method. 5.4.1 Calibration Tasks. 5.4.2 Generic Homography. 5.4.3 Dynamic Calibration. 5.4.4 Reconstruction Procedure. 5.4.5. Reconstruction Examples. 5.5 Summary. Chapter 6 Catadioptric Vision System. 6.1 Introduction. 6.1.1 Wide Field-of-View System. 6.1.2 Calibration of Omni-directional Vision System. 6.1.3 Test Example. 6.2 Panoramic Stereoscopic System. 6.2.1 System Configuration. 6.2.2 Co-axis Installation. 6.2.3 System Model. 6.2.4 Epipolar geometry and 3D reconstruction. 6.2.5 Calibration Procedure. 6.2.5.1 Initialization of the Parameters. 6.2.5.2 Non-linear optimization. 6.3 Parabolic Camera System. 6.3.1 System Configuration. 6.3.2 System Modeling. 6.3.3 Calibration with Lifted-Fundamental-matrix. 6.3.3.1 The lifted fundamental matrix. 6.3.3.2 Calibration Procedure. 6.3.3.3 Simplified Case. 6.3.3.4 Discussion. 6.3.4 Calibration Based on Homographic matrix. 6.3.4.1 Plane-to-mirror Homography. 6.3.4.2 Calibration Procedure. 6.3.4.3 Calibration Test. 6.3.5 Polynomial Eigenvalue Problem. 6.3.5.1 Mirror-to-mirror Homography. 6.3.5.2 Constraints and Solutions. 6.3.5.3 Test Example. 6.4 Hyperbolic Camera System. 6.4.1 System Structure. 6.4.2 Imaging Process and Back Projection. 6.4.3 Polynomial Eigenvalue Problem. 6.5 Summary. Chapter 7 Conclusions and Future Expectation. 7.1 Conclusions. 7.2 Future Expectations. References