Theory, Algorithms, PracticalitiesBy
- E. R. Davies, Royal Holloway, University of London, U.K.
In the last 40 years, machine vision has evolved into a mature field embracing a wide range of applications including surveillance, automated inspection, robot assembly, vehicle guidance, traffic monitoring and control, signature verification, biometric measurement, and analysis of remotely sensed images. While researchers and industry specialists continue to document their work in this area, it has become increasingly difficult for professionals and graduate students to understand the essential theory and practicalities well enough to design their own algorithms and systems. This book directly addresses this need.As in earlier editions, E.R. Davies clearly and systematically presents the basic concepts of the field in highly accessible prose and images, covering essential elements of the theory while emphasizing algorithmic and practical design constraints. In this thoroughly updated edition, he divides the material into horizontal levels of a complete machine vision system. Application case studies demonstrate specific techniques and illustrate key constraints for designing real-world machine vision systems.
Academic and industry researchers in computer science and computer engineering particularly in machine vision, computer vision, and robotics.
Published: December 2004
Imprint: Morgan Kaufmann
This book brings together the analytic aspects of image processing with the practicalities of applying the techniques in an industrial setting. It is excellent grounding for a machine vision researcher. John Billingsley, University of Southern Queensland The book in its previous incarnations has established its place as a unique repository of detailed analysis of important image processing and computer vision algorithms. This edition builds on these strengths and adds material to guide the readers understanding of the latest developments in the field. The result is a comprehensive up-to-date reference text. Farzin Deravi, University of Kent This book is an essential reference for anyone developing techniques for machine vision analysis, including systems for industrial inspection, biomedical analysis, and much more. It comes from a long-term practitioner and is packed with the fundamental techniques required to build and prototype methods to test their applicability to the problem at hand. Majid Mirmehdi, University of Bristol The book contains a large number of experimental design and evaluation procedures that are of keen interest to industrial application engineers of machine vision. William Wee, University of Cincinnati Author E.R. Davies covers essential elements of the theory while addressing algorithmic and practical design constraints. In this updated edition, he divides the material into horizontal levels of a complete machine vision system. He includes coverage of 2-D and 3-D scene analysis, along with the Hough Transform, a key technique for inspection and surveillance. Mechanical Engineering, August 2006
- 1. Vision, the ChallengePart 1 Low-Level Vision2. Images and Imaging Operations3. Basic Image Filtering Operations4. Thresholding Techniques5. Edge Detection6. Binary Shape Analysis7. Boundary Pattern Analysis8. Mathematical MorphologyPart 2 Intermediate-Level Vision9. Line Detection10. Circle Detection11. The Hough Transform and Its Nature12. Ellipse Detection13. Hole Detection14. Polygon and Corner Detection15. Abstract Pattern Matching TechniquesPart 3 3D Vision and Motion16. The Three-Dimensional World17. Tackling the Perspective n-Point Problem18. Motion19. Invariants and their Applications20. Egomotion and Related Tasks21. Image Transformations and Camera CalibrationPart 4 Towards Real-Time Pattern Recognition Systems22. Automated Visual Inspection23. Inspection of Cereal Grains24. Statistical Pattern Recognition25. Biologically Inspired Recognition Schemes26. Texture27. Image Acquisition28. Real-Time Hardware and Systems Design ConsiderationsPart 5 Perspectives on Vision29. Machine Vision, Art or Science?Appendix A Robust Statistics