Computer Vision: Principles, Algorithms, Applications, Learning

ISBN-13: 9780128092842 / Angielski / Twarda / 2017 / 900 str.

Royal Holloway E. R. (Emeritus Professor of Machine Vision

Opis

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 graduate students, researchers and R&D engineers working in this vibrant subject.

• Three new chapters on Machine Learning emphasise the way the subject has been developing during the past few years
• A new chapter on Object Segmentation and Shape Models reflects the methodology of machine learning and gives practical demonstrations of its application
• Careful attention to the modern area of Deep Learning underlines the unprecedented progress achieved recently-as reflected by the new chapter on face detection and recognition
• In-depth discussions have been included on geometric transformations, the EM algorithm, boosting, semantic segmentation, face frontalisation, RNNs and other key topics
• Examples and case studies 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

Spis treści

1. Vision, the Challenge2. Images and Imaging Operations3. Image Filtering and Morphology4. The Role of Thresholding5. Edge Detection6. Corner, Interest Point and Invariant Feature Detection7. Texture Analysis8. Binary Shape Analysis9. Boundary Pattern Analysis10. Line, Circle and Ellipse Detection11. The Generalised Hough Transform12. Object Segmentation and Shape Models13. Basic Classification Concepts14. Machine Learning: Probabilistic Methods15. Deep Learning Networks16. The Three-Dimensional World17. Tackling the Perspective n-point Problem18. Invariants and perspective19. Image transformations and camera calibration20. Motion21. Face Detection and Recognition: the Impact of Deep Learning22. Surveillance23. In-Vehicle Vision Systems24. Epilogue-Perspectives in VisionAppendix A: Robust statisticsAppendix B: The Sampling TheoremAppendix C: The representation of colourAppendix D: Sampling from distributions

O autorze

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.