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Photogrammetric Computer Vision

Statistics, Geometry, Orientation and Reconstruction

Autor: Wolfgang Foerstner, Bernhard P. Wrobel

Jazyk:

Vazba: Brožovaná

Vydavatel: Springer International Publishing AG

Dostupnost: Skladem u dodavatele

Odesíláme za 5-8 dnů

1 455 Kč

This textbook offers a statistical view on the geometry of multiple view analysis, required for came...

Informace o knize

Autor

Wolfgang Foerstner, Bernhard P. Wrobel

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2018

Stránek

816

EAN

9783319791708

Enbook ID

20118356

Vydavatel

Springer International Publishing AG

Hmotnost

1972

Rozměry

379 x 332 x 45

Kompletní popis

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.

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Photogrammetric Computer Vision

Statistics, Geometry, Orientation and Reconstruction

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