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Computer Vision

Principles, Algorithms, Applications, Learning

Computer Vision - Davies, E. R. - ISBN: 9780128092842
Prijs: € 102,70
Levertijd: 12 tot 15 werkdagen
Bindwijze: Boek, Gebonden (14-11-2017)
Genre: Communicatiekunde
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Previous Edition: Computer And Machine Vision / 4th Ed. 2012.


Titel: Computer Vision
auteur: Davies, E. R.
Mediatype: Boek
Bindwijze: Gebonden
Taal: Engels
Druk: 5
Aantal pagina's: 900
Uitgever: Elsevier Science
Plaats van publicatie: 03
Publicatiedatum: 2017-11-14
NUR: Communicatiekunde
Afmetingen: 235 x 191
ISBN/ISBN13: 9780128092842
Intern nummer: 38443103

Biografie (woord)

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.

Extra informatie

Gain an easy-to-understand introduction to the principles of computer vision together with insight into applying vision methods


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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