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Computer Vision: Algorithms and Applications

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Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorith/>Computer Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of "recipes," this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory Suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book Supplies supplementary course material for students at the associated website, http: //szeliski.org/Book/ Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.


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Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorith/>Computer Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of "recipes," this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory Suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book Supplies supplementary course material for students at the associated website, http: //szeliski.org/Book/ Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

30 review for Computer Vision: Algorithms and Applications

  1. 5 out of 5

    Bowei Zhang

    Even though I am fond of cutting-edge textbooks, I don't quite recommend this to read for FUN... Instead, this book should belong to the category of typical REFERENCE BOOKS since it's comprehensive and inevitably dull...

  2. 4 out of 5

    Delhi Irc

    Location: GG6 IRC Accession No: DL027195

  3. 5 out of 5

    Andy D.

  4. 4 out of 5

    Stéphane

  5. 4 out of 5

    Tekin

  6. 4 out of 5

    Mohamed Moawed

  7. 5 out of 5

    Calum Murray

  8. 5 out of 5

    Amanda

  9. 5 out of 5

    Hanna

  10. 4 out of 5

    Geary Scherer

  11. 4 out of 5

    Middlethought

  12. 5 out of 5

    Greg Turk

  13. 5 out of 5

    Arash Mehrjou

  14. 4 out of 5

    Warren Green

  15. 5 out of 5

    Mukundh Bhushan

  16. 5 out of 5

    Arash Kamangir

  17. 4 out of 5

    Eric Paniagua

  18. 5 out of 5

    Parham Nooralishahi

  19. 5 out of 5

    Mahmut Ali

  20. 5 out of 5

    Jingran

  21. 4 out of 5

    Brent Foust

  22. 4 out of 5

    Ng Hon Ming

  23. 5 out of 5

    Tim Daly

  24. 4 out of 5

    Marcel

  25. 4 out of 5

    Pan He

  26. 4 out of 5

    Don P.

  27. 4 out of 5

    Aydın Arpa

  28. 5 out of 5

    Bruno Korbar

  29. 5 out of 5

    Leonardo

  30. 5 out of 5

    John Williams

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