Overview

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/

Full Product Details

Author:   E. R. Davies (Emeritus Professor of Machine Vision, Royal Holloway, University of London, UK) ,  E. R. Davies (Royal Holloway, University of London, UK) ,  E. R. Davies (Royal Holloway, University of London, UK) ,  E. R. Davies (Royal Holloway, University of London, UK)
Publisher:   Elsevier Science Publishing Co Inc
Imprint:   Academic Press Inc
Edition:   5th edition
Weight:   1.990kg
ISBN:  

9780128092842

ISBN 10:   012809284
Pages:   900
Publication Date:   14 November 2017
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

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

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Author Information

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.

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