Uncertain Projective Geometry

Statistical Reasoning for Polyhedral Object Reconstruction

Stephan Heuel - Collection Lecture Notes in Computer Science

206 pages, parution le 05/08/2004

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Résumé

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.

Written for:
Researchers, professionals, and advanced students

Sommaire

Introduction
Representation of Geometric Entities and Transformations
Geometric Reasoning Using Projective Geometry
Statistical Geometric Reasoning
Polyhedral Object Reconstruction
Conclusions

A Notation
B Linear Algebra
C Statistics

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	PAPIER
Éditeur(s)	Springer
Auteur(s)	Stephan Heuel
Collection	Lecture Notes in Computer Science
Parution	05/08/2004
Nb. de pages	206
Format	15,5 x 23,5
Couverture	Broché
Poids	350g
Intérieur	Noir et Blanc
EAN13	9783540220299