Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3008)
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Table of contents (9 chapters)
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Uncertain Projective Geometry
Book Subtitle: Statistical Reasoning for Polyhedral Object Reconstruction
Authors: Stephan Heuel
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/b97201
Publisher: Springer Berlin, Heidelberg
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 2004
Softcover ISBN: 978-3-540-22029-9Published: 29 April 2004
eBook ISBN: 978-3-540-24656-5Published: 22 April 2004
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XVIII, 210
Topics: Geometry, Pattern Recognition, Image Processing and Computer Vision, Probability and Statistics in Computer Science, Computer Graphics, Artificial Intelligence