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Multiple View Geometry in Computer Vision

Multiple View Geometry in Computer Vision

2nd Edition

£73.00

  • Date Published: March 2004
  • availability: In stock
  • format: Paperback
  • isbn: 9780521540513

£ 73.00
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About the Authors
  • 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.

    • Thoroughly updated, with over 50 algorithms and lots more extra material
    • Explains the required mathematical background
    • Can be used for graduate courses or as an overview of field
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    Reviews & endorsements

    'I am very positive about this book. 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.' Computing Reviews

    '… a book which is timely, extremely thorough and commendably clear … Overall, the approach is masterly … The authors have managed to present the very essence of the subject in a way which the most subtle ideas seem natural and straightforward. I have never seen such a clear exploration of the geometry of vision. I would wholeheartedly recommend this book. It deserves to be in the library of every serious researcher in the field of computer vision.' Journal of Robotica

    'The new edition features an extended introduction covering the key ideas in the book (which itself have 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.' Zentralblatt MATH

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    Product details

    • Edition: 2nd Edition
    • Date Published: March 2004
    • format: Paperback
    • isbn: 9780521540513
    • length: 670 pages
    • dimensions: 248 x 175 x 36 mm
    • weight: 1.465kg
    • contains: 36 colour illus. 35 tables 124 exercises
    • availability: In stock
  • 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.

  • Authors

    Richard Hartley, Australian National University, Canberra

    Andrew Zisserman, University of Oxford

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