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Multiple View Geometry in Computer Vision

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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 Zisserm 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. First Edition HB (2000): 0-521-62304-9


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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 Zisserm 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. First Edition HB (2000): 0-521-62304-9

30 review for Multiple View Geometry in Computer Vision

  1. 4 out of 5

    Wooi Hen Yap

    The book title says it all. It's not exaggerating when I considered this one a must have for students, practitioners and researchers alike intended to work on 3D computer vision. It covers broad yet deep state-of-the-art methods in 3D reconstruction from multiple cameras, from single camera. However it is not for the faint-hearted .. Not recommended for beginners who had no good knowledge linear algebra, bayesian estimation or tensor algebra. The book title says it all. It's not exaggerating when I considered this one a must have for students, practitioners and researchers alike intended to work on 3D computer vision. It covers broad yet deep state-of-the-art methods in 3D reconstruction from multiple cameras, from single camera. However it is not for the faint-hearted .. Not recommended for beginners who had no good knowledge linear algebra, bayesian estimation or tensor algebra.

  2. 4 out of 5

    Delhi Irc

    Location: GG6 IRC Accession no: DL026989

  3. 4 out of 5

    Jingran

  4. 5 out of 5

    Nataliya

  5. 5 out of 5

    Saurav Shekhar

  6. 5 out of 5

    Voidpointer

  7. 4 out of 5

    Ken Fast

  8. 4 out of 5

    Chris Choy

  9. 5 out of 5

    Warren Green

  10. 5 out of 5

    Vsevolod Radchenko

  11. 4 out of 5

    Bowei Zhang

  12. 5 out of 5

    Mike

  13. 5 out of 5

    Frank

  14. 5 out of 5

    Dan Farmer

  15. 4 out of 5

    Fire Ball

  16. 4 out of 5

    Adam Pere

  17. 4 out of 5

    Costas Plakas

  18. 4 out of 5

    Landis Huffman

  19. 4 out of 5

    Jan Margeta

  20. 5 out of 5

    Merwan BIREM

  21. 5 out of 5

    Li Xiaoliable

  22. 5 out of 5

    Mauro Brenna

  23. 4 out of 5

    Greg

  24. 4 out of 5

    Stephen Johnson

  25. 5 out of 5

    Michael

  26. 4 out of 5

    Chris

  27. 5 out of 5

    Hanlin Goh

  28. 4 out of 5

    Matthew

  29. 4 out of 5

    Dylan Jackson

  30. 5 out of 5

    Tyler

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