E. R. Davies

Computer Vision

Principles, Algorithms, Applications, Learning

• Gebundenes Buch

Produktbeschreibung

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/

Produktdetails

• Verlag: Academic Press
• Artikelnr. des Verlages: C2015-0-05563-0
• 5. Aufl.
• Seitenzahl: 900
• Erscheinungstermin: 15. November 2017
• Englisch
• Abmessung: 244mm x 195mm x 50mm
• Gewicht: 1972g
• ISBN-13: 9780128092842
• ISBN-10: 012809284X
• Artikelnr.: 48768716

Autorenporträt

Roy Davies was Emeritus Professor of Machine Vision at Royal Holloway, University of London. He worked on many aspects of vision, from feature detection to robust, real-time implementations of practical vision tasks. His interests included 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 held a DSc at the University of London and was awarded Distinguished Fellow of the British Machine Vision Association, and Fellow of the International Association of Pattern Recognition.

Inhaltsangabe

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