Synopses & Reviews
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.
· Includes solid, accessible coverage of 2-D and 3-D scene analysis.
· Offers thorough treatment of the Hough Transform—a key technique for inspection and surveillance.
· Brings vital topics and techniques together in an integrated system design approach.
· Takes full account of the requirement for real-time processing in real applications.
m 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
Thoroughly updated solid text reference for an increasingly important field
eers of machine vision."
William Wee, University of Cincinnati
“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.”
— 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.”
— 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.”
— Mechanical Engineering, August 2006
Machine Vision: Theory, Algorithms, Practicalities provides a solid background on all the major topics of machine vision-the science and engineering of extracting data from images-in addition to the core areas of image processing, image analysis and machine/computer vision, including automated visual inspection. While this new edition maintains its emphasis on practicalities in conjunction with theory and algorithms, 25% of the book will be completely new material and another 25% will be fully revised. The increasingly important topics of 3D vision and motion will form an entire section of the book and the author will create many more exercises to support classroom work. Ten years ago (early in the life of the 1st edition of this title) an undergraduate computer vision course was not a common feature of many engineering or computer science curricula. A digital image processing course was more common, but generally as an application of signal processing techniques within an EE/CompE program. Today, however, a larger number of institutions offer computer vision courses at the undergraduate level in both CS and CompE curricula. In some cases the CV courses are offered as a complement or continuation of an IP course, in other cases as a standalone elective. Many of these are designed for Sr. undergraduates and 1st year graduates. We are revising what has been recognized as a classic in the field for this growing market.
About the Author
Roy Davies is a Professor of Machine Vision at Royal Holloway, University of London, and has extensive experience of machine vision, image analysis, automated visual inspection, and noise suppression techniques. His book Electronics, Noise, and Signal Recovery
was published in 1993 by Academic Press, and is a useful companion to the present volume.
Royal Holloway, University of London, U.K.
Table of Contents
1. Vision, the Challenge
Part 1 Low-Level Vision
2. Images and Imaging Operations
3. Basic Image Filtering Operations
4. Thresholding Techniques
5. Edge Detection
6. Binary Shape Analysis
7. Boundary Pattern Analysis
8. Mathematical Morphology
Part 2 Intermediate-Level Vision
9. Line Detection
10. Circle Detection
11. The Hough Transform and Its Nature
12. Ellipse Detection
13. Hole Detection
14. Polygon and Corner Detection
15. Abstract Pattern Matching Techniques
Part 3 3–D Vision and Motion
16. The Three-Dimensional World
17. Tackling the Perspective n-Point Problem
19. Invariants and their Applications
20. Egomotion and Related Tasks
21. Image Transformations and Camera Calibration
Part 4 Towards Real-Time Pattern Recognition Systems
22. Automated Visual Inspection
23. Inspection of Cereal Grains
24. Statistical Pattern Recognition
25. Biologically Inspired Recognition Schemes
27. Image Acquisition
28. Real-Time Hardware and Systems Design Considerations
Part 5 Perspectives on Vision
29. Machine Vision, Art or Science?
Appendix A Robust Statistics