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About the Author
Chapter 1: Vision, the Challenge
Part 1: Low-Level Vision
Images and Imaging Operations
Chapter 2: Images and Imaging Operations
Basic Image Filtering Operations
Chapter 3: Basic Image Filtering Operations
Chapter 4: Thresholding Techniques
Chapter 5: Edge Detection
Binary Shape Analysis
Chapter 6: Binary Shape Analysis
Boundary Pattern Analysis
Chapter 7: Boundary Pattern Analysis
Chapter 8: Mathematical Morphology
Part 2: Intermediate-Level Vision
Chapter 9: Line Detection
Chapter 10: Circle Detection
The Hough Transform and Its Nature
Chapter 11: The Hough Transform and Its Nature
Chapter 12: Ellipse Detection
Chapter 13: Hole Detection
Polygon and Corner Detection
Chapter 14: Polygon and Corner Detection
Abstract Pattern Matching Techniques
Chapter 15: Abstract Pattern Matching Techniques
Part 3: 3-D Vision and Motion
The Three-Dimensional World
Chapter 16: The Three-Dimensional World
Tackling the Perspective n-point Problem
Chapter 17: Tackling the Perspective n-point Problem
Chapter 18: Motion
Invariants and Their Applications
Chapter 19: Invariants and Their Applications
Egomotion and Related Tasks
Chapter 20: Egomotion and Related Tasks
Chapter 21: Image Transformations and Camera Calibration
Part 4: Toward Real-Time Pattern Recognition Systems
Automated Visual Inspection
Chapter 22: Automated Visual Inspection
Inspection of Cereal Grains
Chapter 23: Inspection of Cereal Grains
Statistical Pattern Recognition
Chapter 24: Statistical Pattern Recognition
Biologically Inspired Recognition Schemes
Chapter 25: Biologically Inspired Recognition Schemes
Chapter 26: Texture
Chapter 27: Image Acquisition
Real-Time Hardware and Systems Design Considerations
Chapter 28: Real-Time Hardware and Systems Design Considerations
Part 5: Perspectives on Vision
Chapter 29: Machine Vision: Art or Science?
Appendix A: Robust Statistics
List of Acronyms and Abbreviations
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.
Academic and industry researchers in computer science and computer engineering particularly in machine vision, computer vision, and robotics.
- No. of pages:
- © Morgan Kaufmann 2004
- 22nd December 2004
- Morgan Kaufmann
- Hardcover ISBN:
- eBook ISBN:
“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 reader’s 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
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.
Emeritus Professor of Machine Vision, Royal Holloway, University of London, UK
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