TY  - BOOK
AU  - Heuel,Stephan
ED  - SpringerLink (Online service)
TI  - Uncertain Projective Geometry: Statistical Reasoning for Polyhedral Object Reconstruction 
T2  - Lecture Notes in Computer Science,
SN  - 9783540246565
AV  - Q337.5 
U1  - 006.4 23
PY  - 2004///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Computer science
KW  - Artificial intelligence
KW  - Computer graphics
KW  - Computer vision
KW  - Optical pattern recognition
KW  - Geometry
KW  - Computer Science
KW  - Pattern Recognition
KW  - Image Processing and Computer Vision
KW  - Probability and Statistics in Computer Science
KW  - Computer Graphics
KW  - Artificial Intelligence (incl. Robotics)
N1  - 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
N2  - 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
UR  - http://dx.doi.org/10.1007/b97201
ER  -