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Multiple View Geometry in Computer Vision

Multiple View Geometry in Computer Vision - 03 edition

ISBN13: 978-0521540513

Cover of Multiple View Geometry in Computer Vision 03 (ISBN 978-0521540513)
ISBN13: 978-0521540513
ISBN10: 0521540518
Edition: 03
Copyright: 2003
Publisher: Cambridge University Press
Published: 2003
International: No
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Multiple View Geometry in Computer Vision - 03 edition

ISBN13: 978-0521540513

Richard Hartley

ISBN13: 978-0521540513
ISBN10: 0521540518
Edition: 03
Copyright: 2003
Publisher: Cambridge University Press
Published: 2003
International: No
Summary

A basic problem in computer vision is to understand the structure of a real world scene. This book covers relevant geometric principles and how to represent objects algebraically so they can be computed and applied. Recent major developments in the theory and practice of scene reconstruction are described in detail in a unified framework. Richard Hartley and Andrew Zisserman provide comprehensive background material and explain how to apply the methods and implement the algorithms.

Table of Contents

Table of Contents
Foreword
Preface
1 Introduction - a Tour of Multiple View Geometry 1
The Background: Projective Geometry, Transformations and Estimation 23
2 Projective Geometry and Transformations of 2D 25
3 Projective Geometry and Transformations of 3D 65
4 Estimation - 2D Projective Transformations 87
5 Algorithm Evaluation and Error Analysis 132
Camera Geometry and Single View Geometry 151
6 Camera Models 153
7 Computation of the Camera Matrix [Rho] 178
8 More Single View Geometry 195
Two-View Geometry 237
9 Epipolar Geometry and the Fundamental Matrix 239
10 3D Reconstruction of Cameras and Structure 262
11 Computation of the Fundamental Matrix F 279
12 Structure Computation 310
13 Scene planes and homographies 325
14 Affine Epipolar Geometry 344
Three-View Geometry 363
15 The Trifocal Tensor 365
16 Computation of the Trifocal Tensor [Tau] 391
N-View Geometry 409
17 N-Linearities and Multiple View Tensors 411
18 N-View Computational Methods 434
19 Auto-Calibration 458
20 Duality 502
21 Cheirality 515
22 Degenerate Configurations 533
Appendices 561
App. 1 Tensor Notation 562
App. 2 Gaussian (Normal) and [chi][superscript 2] Distributions 565
App. 3 Parameter Estimation 568
App. 4 Matrix Properties and Decompositions 578
App. 5 Least-squares Minimization 588
App. 6 Iterative Estimation Methods 597
App. 7 Some Special Plane Projective Transformations 628
Bibliography 634
Index 646

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