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Computer and Machine Vision: Theory, Algorithms, Practicalities

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Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject. Key features include: Practical examples and case studies give the 'ins and outs' of developing real-world vision systems, giving engineers the realities of implementing the principles in practice New chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer vision Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging The 'recent developments' section now included in each chapter will be useful in bringing students and practitioners up to date with the subject


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Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject. Key features include: Practical examples and case studies give the 'ins and outs' of developing real-world vision systems, giving engineers the realities of implementing the principles in practice New chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer vision Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging The 'recent developments' section now included in each chapter will be useful in bringing students and practitioners up to date with the subject

33 review for Computer and Machine Vision: Theory, Algorithms, Practicalities

  1. 4 out of 5

    Yehoshua Meyer

  2. 5 out of 5

    Yehoshua Meyer

  3. 5 out of 5

    Adam

  4. 4 out of 5

    Rey

  5. 5 out of 5

    Rajasekaran Ra

  6. 4 out of 5

    Subhajit Das

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  10. 5 out of 5

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  11. 5 out of 5

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  13. 4 out of 5

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  14. 5 out of 5

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  15. 4 out of 5

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  20. 4 out of 5

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  22. 4 out of 5

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  26. 4 out of 5

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  27. 5 out of 5

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  29. 5 out of 5

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  30. 5 out of 5

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  31. 4 out of 5

    Linywu

  32. 4 out of 5

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  33. 4 out of 5

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