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Photogrammetric computer vision = st...

Forstner, Wolfgang.

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Photogrammetric computer vision = statistics, geometry, orientation and reconstruction /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Photogrammetric computer vision/ by Wolfgang Forstner, Bernhard P. Wrobel.
其他題名:	statistics, geometry, orientation and reconstruction /
作者:	Forstner, Wolfgang.
其他作者:	Wrobel, Bernhard P.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xvii, 816 p. :ill. (some col.), digital ;24 cm.
內容註:	Introduction -- Tasks for Photogrammetric Computer Vision -- Modelling in Automated Photogrammetric Computer Vision -- Probability Theory and Random Variables -- Testing -- Estimation -- Homogeneous Representations of Points, Lines and Planes -- Transformations -- Geometric Operations -- Rotations -- Oriented Projective Geometry -- Reasoning with Uncertain Geometric Entities -- Orientation and Reconstruction -- Bundle Adjustment -- Surface Reconstruction from Point Clouds -- References -- Index.
Contained By:	Springer eBooks
標題:	Photogrammetry - Digital techniques. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-11550-4
ISBN:	9783319115504

Photogrammetric computer vision = statistics, geometry, orientation and reconstruction /
Forstner, Wolfgang.

Photogrammetric computer visionstatistics, geometry, orientation and reconstruction /[electronic resource] :by Wolfgang Forstner, Bernhard P. Wrobel. - Cham :Springer International Publishing :2016. - xvii, 816 p. :ill. (some col.), digital ;24 cm. - Geometry and computing,v.111866-6795 ;. - Geometry and computing ;v.11..

Introduction -- Tasks for Photogrammetric Computer Vision -- Modelling in Automated Photogrammetric Computer Vision -- Probability Theory and Random Variables -- Testing -- Estimation -- Homogeneous Representations of Points, Lines and Planes -- Transformations -- Geometric Operations -- Rotations -- Oriented Projective Geometry -- Reasoning with Uncertain Geometric Entities -- Orientation and Reconstruction -- Bundle Adjustment -- Surface Reconstruction from Point Clouds -- References -- Index.

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.

ISBN: 9783319115504

Standard No.: 10.1007/978-3-319-11550-4doiSubjects--Topical Terms:

625183
Photogrammetry
--Digital techniques.

LC Class. No.: TA593

Dewey Class. No.: 526.982

Photogrammetric computer vision = statistics, geometry, orientation and reconstruction /
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520 $a 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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