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Machine Vision

Theory, Algorithms, Practicalities

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… Read more

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Description

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.

Key features

· 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.

Readership

Academic and industry researchers in computer science and computer engineering particularly in machine vision, computer vision, and robotics.

Table of contents

About the Author

Dedication

Foreword

Preface

Acknowledgments

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

Thresholding Techniques

Chapter 4: Thresholding Techniques

Edge Detection

Chapter 5: Edge Detection

Binary Shape Analysis

Chapter 6: Binary Shape Analysis

Boundary Pattern Analysis

Chapter 7: Boundary Pattern Analysis

Mathematical Morphology

Chapter 8: Mathematical Morphology

Part 2: Intermediate-Level Vision

Line Detection

Chapter 9: Line Detection

Circle Detection

Chapter 10: Circle Detection

The Hough Transform and Its Nature

Chapter 11: The Hough Transform and Its Nature

Ellipse Detection

Chapter 12: Ellipse Detection

Hole 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

Motion

Chapter 18: Motion

Invariants and Their Applications

Chapter 19: Invariants and Their Applications

Egomotion and Related Tasks

Chapter 20: Egomotion and Related Tasks

Image Transformations

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

Texture

Chapter 26: Texture

Image Acquisition

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?

Robust Statistics

Appendix A: Robust Statistics

List of Acronyms and Abbreviations

References

Author Index

Subject Index

Review quotes

“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

Product details

About the author

ED

E. R. Davies

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.
Affiliations and expertise
Emeritus Professor of Machine Vision, Royal Holloway, University of London, UK (deceased)

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