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.
| ISBN: | 9783540220299 |
| Publication date: | 29th April 2004 |
| Author: | Stephan Heuel |
| Publisher: | Springer an imprint of Springer Berlin Heidelberg |
| Format: | Paperback |
| Pagination: | 205 pages |
| Series: | Lecture Notes in Computer Science |
| Genres: |
Geometry Maths for computer scientists Pattern recognition Computer vision Artificial intelligence Probability and statistics Graphics programming |
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.
Uncertain Projective Geometry features in the following genres: Geometry, Maths for computer scientists, Pattern recognition, Computer vision, Artificial intelligence, Probability and statistics, Graphics programming
Uncertain Projective Geometry is available in Paperback
Uncertain Projective Geometry was written by Stephan Heuel and published by Springer an imprint of Springer Berlin Heidelberg
Uncertain Projective Geometry has 205 pages
Yes it is part of Lecture Notes in Computer Science series