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Photogrammetric computer vision / Wolfgang Förstner (2016)

Public Titre : Photogrammetric computer vision : statistics, geometry, orientation and reconstruction Type de document : Guide/Manuel Auteurs : Wolfgang Förstner, Auteur ; Bernhard P. Wrobel, Auteur Editeur : Springer Nature Année de publication : 2016 Collection : Geometry and computing, ISSN 1866-6795 num. 11 Importance : 816 p. Format : 21 x 28 cm ISBN/ISSN/EAN : 978-3-319-11549-8 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] aérotriangulation numérique [Termes IGN] compensation par faisceaux [Termes IGN] couple stéréoscopique [Termes IGN] données maillées [Termes IGN] données vectorielles [Termes IGN] estimation statistique [Termes IGN] géométrie [Termes IGN] géométrie projective [Termes IGN] image 2D [Termes IGN] image 3D [Termes IGN] incertitude géométrique [Termes IGN] ligne (géométrie) [Termes IGN] modèle de Gauss-Markov [Termes IGN] modèle géométrique de prise de vue [Termes IGN] plan (géométrie) [Termes IGN] point [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'objet [Termes IGN] rotation d'objet [Termes IGN] semis de points [Termes IGN] transformation géométrique [Termes IGN] variable aléatoire [Termes IGN] vision par ordinateur [Termes IGN] visualisation 3D Index. décimale : 33.30 Photogrammétrie numérique Résumé : (Editeur) This textbook offers a statistical view on the geometry of multiple view analysis, required for camera calibration and orientation and for geometric scene reconstruction based on geometric image features. The authors have backgrounds in geodesy and also long experience with development and research in computer vision, and this is the first book to present a joint approach from the converging fields of photogrammetry and computer vision. Part I of the book provides an introduction to estimation theory, covering aspects such as Bayesian estimation, variance components, and sequential estimation, with a focus on the statistically sound diagnostics of estimation results essential in vision metrology. Part II provides tools for 2D and 3D geometric reasoning using projective geometry. This includes oriented projective geometry and tools for statistically optimal estimation and test of geometric entities and transformations and their rela­tions, tools that are useful also in the context of uncertain reasoning in point clouds. Part III is de­voted to modelling the geometry of single and multiple cameras, addressing calibration and orienta­tion, including statistical evaluation and reconstruction of corresponding scene features and surfaces based on geometric image features. The authors provide algorithms for various geometric computa­tion problems in vision metrology, together with mathematical justifications and statistical analysis, thus enabling thorough evaluations. The chapters are self-contained with numerous figures and exer­cises, and they are supported by an appendix that explains the basic mathematical notation and a de­tailed index. The book can serve as the basis for undergraduate and graduate courses in photogrammetry, com­puter vision, and computer graphics. It is also appropriate for researchers, engineers, and software developers in the photogrammetry and GIS industries, particularly those engaged with statistically based geometric computer vision methods. Note de contenu : 1. Introduction 1.1. Tasks for Photogrammetric Computer Vision 1.2. Modelling in Photogrammetric Computer Vision 1.3. The Book 1.4. On Notation Part One - Statistics and Estimation 2. Probability Theory and Random Variables 2.1. Notions of Probability 2.2. Axiomatic Definition of Probability 2.3. Random Variables 2.4. Distributions 2.5. Moments 2.6. Quantiles of a Distribution 2.7. Functions of Random Variables 2.8. Stochastic Processes 2.9. Generating Random Numbers 2.10. Exercises 3. Testing 3.1. Principles of Hypothesis Testing 3.2. Testability of an Alternative Hypothesis 3.3. Common Tests 3.4. Exercises 4. Estimation 4.1. Estimation Theory 4.2. The Linear Gauss–Markov Model 4.3. Gauss–Markov Model with Constraints 4.4. The Nonlinear Gauss–Markov Model 4.5. Datum or Gauge Definitions and Transformations 4.6. Evaluation 4.7. Robust Estimation and Outlier Detection 4.8. Estimation with Implicit Functional Models 4.9. Methods for Closed Form Estimations 4.10. Estimation in Autoregressive Models 4.11. Exercises Part two - Geometry 5. Homogeneous Representations of Points, Lines and Planes 5.1. Homogeneous Vectors and Matrices 5.2. Homogeneous Representations of Points and Lines in 2D 5.3. Homogeneous Representations in IPn 5.4. Homogeneous Representations of 3D Lines 5.5. On Plücker Coordinates for Points, Lines and Planes 5.6. The Principle of Duality 5.7. Conics and Quadrics 5.8. Normalizations of Homogeneous Vectors 5.9. Canonical Elements of Coordinate Systems 5.10. Exercises 6. Transformations 6.1. Structure of Projective Collineations 6.2. Basic Transformations 6.3. Concatenation and Inversion of Transformations 6.4. Invariants of Projective Mappings 6.5. Perspective Collineations 6.6. Projective Correlations 6.7. Hierarchy of Projective Transformations and Their Characteristics 6.8. Normalizations of Transformations 6.9. Conditioning 6.10. Exercises 7. Geometric Operations 7.1. Geometric Operations in 2D Space 7.2. Geometric Operations in 3D Space 7.3. Vector and Matrix Representations for Geometric Entities 7.4. Minimal Solutions for Conics and Transformations 7.5. Exercises 8. Rotations 8.1. Rotations in 3D 8.2. Concatenation of Rotations 8.3. Relations Between the Representations for Rotations 8.4. Rotations from Corresponding Vector Pairs 8.5. Exercises 9. Oriented Projective Geometry 9.1. Oriented Entities and Constructions 9.2. Transformation of Oriented Entities 9.3. Exercises 10. Reasoning with Uncertain Geometric Entities 10.1. Motivation 10.2. Representing Uncertain Geometric Elements 10.3. Propagation of the Uncertainty of Homogeneous Entities 10.4. Evaluating Statistically Uncertain Relations 10.5. Closed Form Solutions for Estimating Geometric Entities 10.6. Iterative Solutions for Maximum Likelihood Estimation 10.7. Exercises Part Three - Orientation and Reconstruction 11. Overview 11.1. Scene, Camera, and Image Models 11.2. The Setup of Orientation, Calibration, and Reconstruction 11.3. Exercises 12. Geometry and Orientation of the Single Image 12.1. Geometry of the Single Image 12.2. Orientation of the Single Image 12.3. Inverse Perspective and 3D Information from a Single Image 12.4. Exercises 13. Geometry and Orientation of the Image Pair 13.1. Motivation 13.2 The Geometry of the Image Pair 13.3 Relative Orientation of the Image Pair 13.4. Triangulation 13.5. Absolute Orientation and Spatial Similarity Transformation 13.6. Orientation of the Image Pair and Its Quality 13.7. Exercises 14. Geometry and Orientation of the Image Triplet 14.1. Geometry of the Image Triplet 14.2. Relative Orientation of the Image Triplet 14.3. Exercises 15. Bundle Adjustment 15.1. Motivation for Bundle Adjustment and Its Tasks 15.2. Block Adjustment 15.3. Sparsity of Matrices, Free Adjustment and Theoretical Precision 15.4. Self-calibrating Bundle Adjustment 15.5. Camera Calibration 15.6. Outlier Detection and Approximate Values 15.7. View Planning 15.8. Exercises 16. Surface Reconstruction 16.1. Introduction 16.2. Parametric 21/2D Surfaces 16.3. Models for Reconstructing One-Dimensional Surface Profiles 16.4. Reconstruction of 21/2D Surfaces from 3D Point Clouds 16.5. Examples for Surface Reconstruction 16.6. Exercises Appendix: Basics and Useful Relations from Linear Algebra Numéro de notice : 22610 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Manuel Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82915

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22610-02	DEP-ECP	Livre	Marne-la-Vallée	Dépôt en unité	Exclu du prêt
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