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Davies E.R. Computer Vision: Principles, Algorithms, Applications, Learning

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Davies E.R. Computer Vision: Principles, Algorithms, Applications, Learning
5th Edition. — Academic Press, 2018. — 879 p. — ISBN 978-0-12-809284-2.
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.
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
Vision, the Challenge
Images and Imaging Operations
Image Filtering and Morphology
The Role of Thresholding
Edge Detection
Corner, Interest Point and Invariant Feature Detection
Texture Analysis
Binary Shape Analysis
Boundary Pattern Analysis
Line, Circle and Ellipse Detection
The Generalised Hough Transform
Object Segmentation and Shape Models
Basic Classification Concepts
Machine Learning: Probabilistic Methods
Deep Learning Networks
The Three-Dimensional World
Tackling the Perspective n-point Problem
Invariants and perspective
Image transformations and camera calibration
Motion
Face Detection and Recognition: the Impact of Deep Learning
Surveillance
In-Vehicle Vision Systems
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
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