Multiple View Geometry in Computer Vision

by Richard Hartley, Andrew Zisserman

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.

Product Details

ISBN-13:	9781139636124
Publisher:	Cambridge University Press
Publication date:	03/25/2004
Sold by:	Barnes & Noble
Format:	eBook
File size:	26 MB
Note:	This product may take a few minutes to download.

Table of Contents

1. Introduction - a tour of multiple view geometry; Part 0. The Background: Projective Geometry, Transformations and Estimation: 2. Projective geometry and transformations of 2D; 3. Projective geometry and transformations of 3D; 4. Estimation - 2D projective transforms; 5. Algorithm evaluation and error analysis; Part I. Camera Geometry and Single View Geometry: 6. Camera models; 7. Computation of the camera matrix; 8. More single view geometry; Part II. Two-View Geometry: 9. Epipolar geometry and the fundamental matrix; 10. 3D reconstruction of cameras and structure; 11. Computation of the fundamental matrix F; 12. Structure computation; 13. Scene planes and homographies; 14. Affine epipolar geometry; Part III. Three-View Geometry: 15. The trifocal tensor; 16. Computation of the trifocal tensor T; Part IV. N -View Geometry: 17. N-linearities and multiple view tensors; 18. N-view computational methods; 19. Auto-calibration; 20. Duality; 21. Chirality; 22. Degenerate configurations; Part V. Appendices: Appendix 1. Tensor notation; Appendix 2. Gaussian (normal) and chi-squared distributions; Appendix 3. Parameter estimation. Appendix 4. Matrix properties and decompositions; Appendix 5. Least-squares minimization; Appendix 6. Iterative Estimation Methods; Appendix 7. Some special plane projective transformations; Bibliography; Index.

Editorial Reviews

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)

Booknews

"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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