This volume introduces the fundamental concepts and tools involved in the design and implementation of object recognition systems. Divided into three parts, it first introduces the topic and covers the acquisition of images, then details 3-D object reconstruction, modelling and matching, and finally describes typical recognition systems using case studies. Key features include: Extensive literature surveys of state-of-the-art systems Recognition will be essential reading for research scientists, advanced undergraduate and postgraduate students in computer vision, image processing and pattern…mehr
This volume introduces the fundamental concepts and tools involved in the design and implementation of object recognition systems. Divided into three parts, it first introduces the topic and covers the acquisition of images, then details 3-D object reconstruction, modelling and matching, and finally describes typical recognition systems using case studies. Key features include: Extensive literature surveys of state-of-the-art systems Recognition will be essential reading for research scientists, advanced undergraduate and postgraduate students in computer vision, image processing and pattern classification. It will also be of interest to practitioners working in the field of computer vision. Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Mohammed Bennamoun, Queensland University of Technology, Brisbane, QLD, Australia / George Mamic, Queensland University of Technology, Brisbane, QLD, Australia
Inhaltsangabe
A Introduction and Acquisition Systems. 1. Introduction. 2. Stereo Matching and Reconstruction of a Depth Map. A Summary. B Database Creation and Modelling for 3 D Object Recognition. 3. 3 D Object Creation for Recognition. 4. Object Representation and Feature Matching. B Summary. C Vision Systems Case Studies. 5. Optical Character Recognition. 6. Recognition by Parts and Part Segmentation Techniques. 7. 3 D Object Recognition Systems. C Summary. Appendices. A. Vector and Matrix Analysis. A.1 Preliminaries. A.1.1 Determinant. A.1.2 Inversion. A.2 Derivatives and Integrals of Matrices. A.3 Vectors and Vector Analysis. A.4 Eigenvalues and Eigenvectors. A.5 Quadratic Forms. B. Principal Component Analysis. C. Optimisation Fundamentals. C.1 Fundamental Concepts. C.2 Linear Least Squares. C.3 Non linear Optimisation. C.4 Direct Search Techniques. C.4.1 Simplex Method. C.5 Gradient Methods. C.5.1 Newton Raphson Technique. C.5.2 Davidon Fletcher Powell. C.6 Simulated Annealing. D. Differential Geometry Basic Principles. E. Spline Theory. E.1 Spline Definitions. F. Detailed Derivation of Registration Equations. References.
A - Introduction and Acquisition Systems.- 1. Introduction.- 2. Stereo Matching and Reconstruction of a Depth Map.- A - Summary.- B - Database Creation and Modelling for 3-D Object Recognition.- 3. 3-D Object Creation for Recognition.- 4. Object Representation and Feature Matching.- B - Summary.- C - Vision Systems - Case Studies.- 5. Optical Character Recognition.- 6. Recognition by Parts and Part Segmentation Techniques.- 7. 3-D Object Recognition Systems.- C - Summary.- Appendices.- A. Vector and Matrix Analysis.- A.1 Preliminaries.- A.1.1 Determinant.- A.1.2 Inversion.- A.2 Derivatives and Integrals of Matrices.- A.3 Vectors and Vector Analysis.- A.4 Eigenvalues and Eigenvectors.- A.5 Quadratic Forms.- B. Principal Component Analysis.- C. Optimisation Fundamentals.- C.1 Fundamental Concepts.- C.2 Linear Least Squares.- C.3 Non-linear Optimisation.- C.4 Direct Search Techniques.- C.4.1 Simplex Method.- C.5 Gradient Methods.- C.5.1 Newton-Raphson Technique.- C.5.2 Davidon-Fletcher-Powell.- C.6 Simulated Annealing.- D. Differential Geometry - Basic Principles.- E. Spline Theory.- E.1 Spline Definitions.- F. Detailed Derivation of Registration Equations.- References.
A Introduction and Acquisition Systems. 1. Introduction. 2. Stereo Matching and Reconstruction of a Depth Map. A Summary. B Database Creation and Modelling for 3 D Object Recognition. 3. 3 D Object Creation for Recognition. 4. Object Representation and Feature Matching. B Summary. C Vision Systems Case Studies. 5. Optical Character Recognition. 6. Recognition by Parts and Part Segmentation Techniques. 7. 3 D Object Recognition Systems. C Summary. Appendices. A. Vector and Matrix Analysis. A.1 Preliminaries. A.1.1 Determinant. A.1.2 Inversion. A.2 Derivatives and Integrals of Matrices. A.3 Vectors and Vector Analysis. A.4 Eigenvalues and Eigenvectors. A.5 Quadratic Forms. B. Principal Component Analysis. C. Optimisation Fundamentals. C.1 Fundamental Concepts. C.2 Linear Least Squares. C.3 Non linear Optimisation. C.4 Direct Search Techniques. C.4.1 Simplex Method. C.5 Gradient Methods. C.5.1 Newton Raphson Technique. C.5.2 Davidon Fletcher Powell. C.6 Simulated Annealing. D. Differential Geometry Basic Principles. E. Spline Theory. E.1 Spline Definitions. F. Detailed Derivation of Registration Equations. References.
A - Introduction and Acquisition Systems.- 1. Introduction.- 2. Stereo Matching and Reconstruction of a Depth Map.- A - Summary.- B - Database Creation and Modelling for 3-D Object Recognition.- 3. 3-D Object Creation for Recognition.- 4. Object Representation and Feature Matching.- B - Summary.- C - Vision Systems - Case Studies.- 5. Optical Character Recognition.- 6. Recognition by Parts and Part Segmentation Techniques.- 7. 3-D Object Recognition Systems.- C - Summary.- Appendices.- A. Vector and Matrix Analysis.- A.1 Preliminaries.- A.1.1 Determinant.- A.1.2 Inversion.- A.2 Derivatives and Integrals of Matrices.- A.3 Vectors and Vector Analysis.- A.4 Eigenvalues and Eigenvectors.- A.5 Quadratic Forms.- B. Principal Component Analysis.- C. Optimisation Fundamentals.- C.1 Fundamental Concepts.- C.2 Linear Least Squares.- C.3 Non-linear Optimisation.- C.4 Direct Search Techniques.- C.4.1 Simplex Method.- C.5 Gradient Methods.- C.5.1 Newton-Raphson Technique.- C.5.2 Davidon-Fletcher-Powell.- C.6 Simulated Annealing.- D. Differential Geometry - Basic Principles.- E. Spline Theory.- E.1 Spline Definitions.- F. Detailed Derivation of Registration Equations.- References.
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