Uncertain Projective Geometry
Statistical Reasoning for Polyhedral Object Reconstruction
Heuel, Stephan.
creator
author.
SpringerLink (Online service)
text
gw
2004
monographic
eng
access
XVIII, 210 p. online resource.
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.
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.
by Stephan Heuel.
Mathematics
Mathematical statistics
Artificial intelligence
Computer graphics
Image processing
Pattern recognition
Geometry
Mathematics
Geometry
Pattern Recognition
Image Processing and Computer Vision
Probability and Statistics in Computer Science
Computer Graphics
Artificial Intelligence (incl. Robotics)
QA440-699
516
Springer eBooks
Lecture Notes in Computer Science, 3008
9783540246565
http://dx.doi.org/10.1007/b97201
http://dx.doi.org/10.1007/b97201
121227
20170515111559.0
978-3-540-24656-5