Computer and Machine Vision
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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 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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Product details

  • Hardback | 912 pages
  • 195.58 x 236.22 x 45.72mm | 1,700.96g
  • Academic Press Inc
  • San Diego, United States
  • English
  • 4th edition
  • 0123869080
  • 9780123869081
  • 765,268

Table of contents

1 Vision, the Challenge 2 Images and Imaging Operations 3 Basic Image Filtering Operations 4 Thresholding Techniques 5 Edge Detection 6 Corner and Interest Point Detection 7 Mathematical Morphology 8 Texture 9 Binary Shape Analysis 10 Boundary Pattern Analysis 11 Line Detection 12 Circle and Ellipse Detection 13 The Hough Transform and Its Nature 14 Abstract Pattern Matching Techniques 15 The Three-Dimensional World 16 Tackling the perspective n-point problem 17 Invariants and perspective 18 Image transformations and camera calibration 19 Motion 20 Automated Visual Inspection 21 Inspection of Cereal Grains 22 Surveillance 23 In-Vehicle Vision Systems24 Statistical Pattern Recognition 25 Image Acquisition 26 Real-Time Hardware and Systems Design Considerations 27 Epilogue-Perspectives in Vision Appendix Robust statistics References Index
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About E. R. Davies

Roy Davies is a Professor of Machine Vision at Royal Holloway, University of London, and has extensive experience of machine vision, image analysis, automated visual inspection, and noise suppression techniques. His book Electronics, Noise, and Signal Recovery was published in 1993 by Academic Press, and is a useful companion to the present volume.
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