Automatic Calibration and Reconstruction for Active Vision Systems

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...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Zhang, Beiwei (Autor), Li, Y. F. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Dordrecht : Springer Netherlands : Imprint: Springer, 2012.
Edición:1st ed. 2012.
Colección:Intelligent Systems, Control and Automation: Science and Engineering, 57
Temas:
Control engineering.
Robotics.
Automation.
Computer vision.
Mathematics-Data processing.
Control, Robotics, Automation.
Computer Vision.
Computational Science and Engineering.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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.

Ejemplares similares