Uncertain projective geometry : statistical reasoning for polyhedral object reconstruction

Algebraic projective geometry, with its multilinear relations and its embedding into Grassmann-Cayley algebra, has become the basic representation of multiple view geometry, resulting in deep insights into the algebraic structure of geometric relations, as well as in efficient and versatile algorith...

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Bibliografske podrobnosti
Glavni avtor: Heuel, Stephan
Format: Livre numérique
Jezik:Anglais
Izdano: Berlin [etc.] : Springer [20..].
Cham : Springer Nature
Serija:Lecture notes in computer science 3008
Teme:
Online dostop:Accès sur la plateforme de l'éditeur
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Sporočilo: Archives Springer e-books (Licence nationale)
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Edition sous un autre format:• Uncertain projective geometry, statistical reasoning for polyhedral object reconstruction, Stephan Heuel, Berlin, Springer, 2004, 1 vol. (XVI-205 p.), Lecture notes in computer science, 3-540-22029-1
• Uncertain Projective Geometry, Texte imprimé, 9783662207802
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245 1 0 |a Uncertain projective geometry :  |b statistical reasoning for polyhedral object reconstruction   |c Stephan Heuel. 
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505 0 |a 1 Introduction -- 2 Representation of Geometric Entities and Transformations -- 3 Geometric Reasoning Using Projective Geometry -- 4 Statistical Geometric Reasoning -- 5 Polyhedral Object Reconstruction -- 6 Conclusions -- A Notation -- B Linear Algebra -- C Statistics. 
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520 |a Algebraic projective geometry, with its multilinear relations and its embedding into Grassmann-Cayley algebra, has become the basic representation of multiple view geometry, resulting in deep insights into the algebraic structure of geometric relations, as well as in efficient and versatile algorithms for computer vision and image analysis. This book provides a coherent integration of algebraic projective geometry and spatial reasoning under uncertainty with applications in computer vision. Beyond systematically introducing the theoretical foundations from geometry and statistics and clear rules for performing geometric reasoning under uncertainty, the author provides a collection of detailed algorithms. The book addresses researchers and advanced students interested in algebraic projective geometry for image analysis, in statistical representation of objects and transformations, or in generic tools for testing and estimating within the context of geometric multiple-view analysis. 
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