Automatic Calibration and Reconstruction for Active Vision Systems

• Κύριοι συγγραφείς: Zhang, Beiwei (Συγγραφέας), Li, Y. F. (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Dordrecht : Springer Netherlands : Imprint: Springer, 2012.
• Σειρά: Intelligent Systems, Control and Automation: Science and Engineering, 57
• Θέματα: Engineering.; Image processing.; Computer mathematics.; Robotics.; Automation.; Robotics and Automation.; Image Processing and Computer Vision.; Computational Science and Engineering.

In this book, the design of two new planar patterns for camera calibration of intrinsic parameters is addressed and a line-based method for distortion correction is suggested. The dynamic calibration of structured light systems, which consist of a camera and a projector is also treated. Also, the 3D...

Πίνακας περιεχομένων:

• 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.