Multiple View Geometry in Computer Vision [NOOK Book]

Overview

A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes ...
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Multiple View Geometry in Computer Vision

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Overview

A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.
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Editorial Reviews

Booknews
Hartly, in the US private sector, and Zisserman (U. of Oxford) describe techniques for reconstructing a real-world scene from several images of it, a basic problem in computer vision. The methods have been developed from projective geometry and photogrametry, and have the special feature of being uncalibrated, that is, it is not necessary to know or to compute the camera's internal parameters before getting an answer to the problem. They discuss the geometric principles, their algebraic representation in terms of camera projection matrices, the fundamental matrix, and the trifocal tensor. Annotation c. Book News, Inc., Portland, OR (booknews.com)
From the Publisher
"The authors have succeeded very well in describing the main techniques in mainstream multiple view geometry, both classical and modern, in a clear and consistent way....I heartily recommend this book." Computing Reviews
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Product Details

  • ISBN-13: 9781139636124
  • Publisher: Cambridge University Press
  • Publication date: 12/5/2012
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 2
  • Sales rank: 1,083,855
  • File size: 26 MB
  • Note: This product may take a few minutes to download.

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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