Computer Vision

Computer Vision

Principles, Algorithms, Applications, Learning

5th Edition - November 14, 2017

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  • Author: E. R. Davies
  • eBook ISBN: 9780128095751
  • Hardcover ISBN: 9780128092842

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Description

Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fifth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject. See an interview with the author explaining his approach to teaching and learning computer vision - http://scitechconnect.elsevier.com/computer-vision/

Key Features

  • Three new chapters on Machine Learning emphasise the way the subject has been developing; Two chapters cover Basic Classification Concepts and Probabilistic Models; and the The third covers the principles of Deep Learning Networks and shows their impact on computer vision, reflected in a new chapter Face Detection and Recognition.
  • A new chapter on Object Segmentation and Shape Models reflects the methodology of machine learning and gives practical demonstrations of its application.
  • In-depth discussions have been included on geometric transformations, the EM algorithm, boosting, semantic segmentation, face frontalisation, RNNs and other key topics.
  • Examples and applications—including the location of biscuits, foreign bodies, faces, eyes, road lanes, surveillance, vehicles and pedestrians—give the ‘ins and outs’ of developing real-world vision systems, showing the realities of practical implementation.
  • Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples.
  • The ‘recent developments’ sections included in each chapter aim to bring students and practitioners up to date with this fast-moving subject.
  • Tailored programming examples—code, methods, illustrations, tasks, hints and solutions (mainly involving MATLAB and C++)

Readership

Computer vision researchers; undergraduates and post graduates in computer vision, machine learning, pattern recognition and Image processing

Table of Contents

  • 1. Vision, the Challenge
    2. Images and Imaging Operations
    3. Image Filtering and Morphology
    4. The Role of Thresholding
    5. Edge Detection
    6. Corner, Interest Point and Invariant Feature Detection
    7. Texture Analysis
    8. Binary Shape Analysis
    9. Boundary Pattern Analysis
    10. Line, Circle and Ellipse Detection
    11. The Generalised Hough Transform
    12. Object Segmentation and Shape Models
    13. Basic Classification Concepts
    14. Machine Learning: Probabilistic Methods
    15. Deep Learning Networks
    16. The Three-Dimensional World
    17. Tackling the Perspective n-point Problem
    18. Invariants and perspective
    19. Image transformations and camera calibration
    20. Motion
    21. Face Detection and Recognition: the Impact of Deep Learning
    22. Surveillance
    23. In-Vehicle Vision Systems
    24. Epilogue—Perspectives in Vision
    Appendix A: Robust statistics
    Appendix B: The Sampling Theorem
    Appendix C: The representation of colour
    Appendix D: Sampling from distributions
    References
    Index

Product details

  • No. of pages: 900
  • Language: English
  • Copyright: © Academic Press 2017
  • Published: November 14, 2017
  • Imprint: Academic Press
  • eBook ISBN: 9780128095751
  • Hardcover ISBN: 9780128092842

About the Author

E. R. Davies

Roy Davies is Emeritus Professor of Machine Vision at Royal Holloway, University of London. He has worked on many aspects of vision, from feature detection to robust, real-time implementations of practical vision tasks. His interests include automated visual inspection, surveillance, vehicle guidance, crime detection and neural networks. He has published more than 200 papers, and three books. Machine Vision: Theory, Algorithms, Practicalities (1990) has been widely used internationally for more than 25 years, and is now out in this much enhanced fifth edition. Roy holds a DSc at the University of London, and has been awarded Distinguished Fellow of the British Machine Vision Association, and Fellow of the International Association of Pattern Recognition.

Affiliations and Expertise

Emeritus Professor of Machine Vision, Royal Holloway, University of London, UK

Ratings and Reviews

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  • PeterLazorik Fri Jan 11 2019

    Perfect book with examples.

    Perfect book with examples.

  • Vikash K. Wed Jan 24 2018

    Detailed coverage from basic to advanced with focus on Deep Learning

    Apart from being very well organised, detailed and complete the 5th edition provides new chapters on Face Recognition, Machine Learning, Deep Learning. Various aspects of DL and ML from the perspective of Computer Vision are addressed. With detailed focus on EM algorithm, boosting, semantic segmentation and RNN. Also more focus is given on Object Segmentation and Shape Models. Overall I would say a very good book, useful for beginner to advanced level.