An Introduction to 3D Computer Vision Techniques and Algorithms

Computer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene. However using algorithms, it is possible to take a collection of stereo-pair images of a scene and then automatically produce a photo-realistic, geometrically accurate digital 3D model. This book provides a comprehensive introduction to the methods, theories and algorithms of 3D computer vision. Almost every theoretical issue is underpinned with practical implementation or a working algorithm using pseudo-code and complete code written in C++ and MatLab . There is the additional clarification of an accompanying website with downloadable software, case studies and exercises. Organised in three parts, Cyganek and Siebert give a brief history of vision research, and subsequently: present basic low-level image processing operations for image matching, including a separate chapter on image matching algorithms; explain scale-space vision, as well as space reconstruction and multiview integration; demonstrate a variety of practical applications for 3D surface imaging and analysis; provide concise appendices on topics such as the basics of projective geometry and tensor calculus for image processing, distortion and noise in images plus image warping procedures. An Introduction to 3D Computer Vision Algorithms and Techniques is a valuable reference for practitioners and programmers working in 3D computer vision, image processing and analysis as well as computer visualisation. It would also be of interest to advanced students and researchers in the fields of engineering, computer science, clinical photography, robotics, graphics and mathematics.

Computer vision encompasses the construction of integrated visionsystems and the application of vision to problems of real-worldimportance. The process of creating 3D models is still ratherdifficult, requiring mechanical measurement of the camera positionsor manual alignment of partial 3D views of a scene. However usingalgorithms, it is possible to take a collection of stereo-pairimages of a scene and then automatically produce a photo-realisticgeometrically accurate digital 3D model.This book provides a comprehensive introduction to the methodstheories and algorithms of 3D computer vision. Almost everytheoretical issue is underpinned with practical implementation or aworking algorithm using pseudo-code and complete code written inC++ and MatLab®. There is the additional clarification of anaccompanying website with downloadable software, case studies andexercises. Organised in three parts, Cyganek and Siebert give abrief history of vision research, and subsequently:* present basic low-level image processing operations for imagematching, including a separate chapter on image matchingalgorithms;* explain scale-space vision, as well as space reconstruction andmultiview integration;* demonstrate a variety of practical applications for 3D surfaceimaging and analysis;* provide concise appendices on topics such as the basics ofprojective geometry and tensor calculus for image processingdistortion and noise in images plus image warping procedures.An Introduction to 3D Computer Vision Algorithms andTechniques is a valuable reference for practitioners andprogrammers working in 3D computer vision, image processing andanalysis as well as computer visualisation. It would also be ofinterest to advanced students and researchers in the fields ofengineering, computer science, clinical photography, roboticsgraphics and mathematics.

Preface.Acknowledgements.Notation and Abbreviations.Part I.1 Introduction.1.1 Stereo-pair Images and Depth Perception.1.2 3D Vision Systems.1.3 3D Vision Applications.1.4 Contents Overview: The 3D Vision Task in Stages.2 Brief History of Research on Vision.2.1 Abstract.2.2 Retrospective of Vision Research.2.3 Closure.Part II.3 2D and 3D Vision Formation.3.1 Abstract.3.2 Human Visual System.3.3 Geometry and Acquisition of a Single Image.3.4 Stereoscopic Acquisition Systems.3.5 Stereo Matching Constraints.3.6 Calibration of Cameras.3.7 Practical Examples.3.8 Appendix: Derivation of the Pin-hole CameraTransformation.3.9 Closure.4 Low-level Image Processing for Image Matching.4.1 Abstract.4.2 Basic Concepts.4.3 Discrete Averaging.4.4 Discrete Differentiation.4.5 Edge Detection.4.6 Structural Tensor.4.7 Corner Detection.4.8 Practical Examples.4.9 Closure.5 Scale-space Vision.5.1 Abstract.5.2 Basic Concepts.5.3 Constructing a Scale-space.5.4 Multi-resolution Pyramids.5.5 Practical Examples.5.6 Closure.6 Image Matching Algorithms.6.1 Abstract.6.2 Basic Concepts.6.3 Match Measures.6.4 Computational Aspects of Matching.6.5 Diversity of Stereo Matching Methods.6.6 Area-based Matching.6.7 Area-based Elastic Matching.6.8 Feature-based Image Matching.6.9 Gradient-based Matching.6.10 Method of Dynamic Programming.6.11 Graph Cut Approach.6.12 Optical Flow.6.13 Practical Examples.6.14 Closure.7 Space Reconstruction and Multiview Integration.7.1 Abstract.7.2 General 3D Reconstruction.7.3 Multiview Integration.7.4 Closure.8 Case Examples.8.1 Abstract.8.2 3D System for Vision-Impaired Persons.8.3 Face and Body Modelling.8.4 Clinical and Veterinary Applications.8.5 Movie Restoration.8.6 Closure.Part III.9 Basics of the Projective Geometry.9.1 Abstract.9.2 Homogeneous Coordinates.9.3 Point, Line and the Rule of Duality.9.4 Point and Line at Infinity.9.5 Basics on Conics.9.6 Group of Projective Transformations.9.7 Projective Invariants.9.8 Closure.10 Basics of Tensor Calculus for Image Processing.10.1 Abstract.10.2 Basic Concepts.10.3 Change of a Base.10.4 Laws of Tensor Transformations.10.5 The Metric Tensor.10.6 Simple Tensor Algebra.10.7 Closure.11 Distortions and Noise in Images.11.1 Abstract.11.2 Types and Models of Noise.11.3 Generating Noisy Test Images.11.4 Generating Random Numbers with Normal Distributions.11.5 Closure.12 Image Warping Procedures.12.1 Abstract.12.2 Architecture of the Warping System.12.3 Coordinate Transformation Module.12.4 Interpolation of Pixel Values.12.5 The Warp Engine.12.6 Software Model of the Warping Schemes.12.7 Warp Examples.12.8 Finding the Linear Transformation from PointCorrespondences.12.9 Closure.13 Programming Techniques for Image Processing and ComputerVision.13.1 Abstract.13.2 Useful Techniques and Methodology.13.3 Design Patterns.13.4 Object Lifetime and Memory Management.13.5 Image Processing Platforms.13.6 Closure.14 Image Processing Library.References.Index.