Machine Vision - 1st Edition - ISBN: 9780122060908, 9781483275611

Machine Vision

1st Edition

Theory, Algorithms, Practicalities

Authors: E. R. Davies
Editors: P. G. Farrell J. R. Forrest
eBook ISBN: 9781483275611
Imprint: Academic Press
Published Date: 28th January 1990
Page Count: 572
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Machine Vision: Theory, Algorithms, Practicalities covers the limitations, constraints, and tradeoffs of vision algorithms. This book is organized into four parts encompassing 21 chapters that tackle general topics, such as noise suppression, edge detection, principles of illumination, feature recognition, Bayes’ theory, and Hough transforms.

Part 1 provides research ideas on imaging and image filtering operations, thresholding techniques, edge detection, and binary shape and boundary pattern analyses. Part 2 deals with the area of intermediate-level vision, the nature of the Hough transform, shape detection, and corner location. Part 3 demonstrates some of the practical applications of the basic work previously covered in the book. This part also discusses some of the principles underlying implementation, including on lighting and hardware systems. Part 4 highlights the limitations and constraints of vision algorithms and their corresponding solutions.

This book will prove useful to students with undergraduate course on vision for electronic engineering or computer science.

Table of Contents



Glossary of Acronyms and Abbreviations

1 Vision, the Challenge

1.1 Introduction—Man and his Senses

1.2 The Nature of Vision

1.3 Automated Visual Inspection

1.4 What this Book is About

1.5 The Following Chapters

1.6 Bibliographical Notes

Part 1 Low-Level Processing

2 Images and Imaging Operations

2.1 Introduction

2.2 Image Processing Operations

2.3 Convolutions and Point Spread Functions

2.4 Sequential Versus Parallel Operations

2.5 Concluding Remarks

2.6 Bibliographical and Historical Notes

2.7 Problems

3 Basic Image Filtering Operations

3.1 Introduction

3.2 Noise Suppression by Gaussian Smoothing

3.3 Median Filtering

3.4 Mode Filtering

3.5 Bias Generated by Noise Suppression Filters

3.6 Reducing Computational Load

3.7 The Role of Filters in Industrial Applications of Vision

3.8 Sharp-Unsharp Masking

3.9 Concluding Remarks

3.10 Bibliographical and Historical Notes

3.11 Problems

4 Thresholding Techniques

4.1 Introduction

4.2 Region-Growing Methods

4.3 Thresholding

4.4 Adaptive Thresholding

4.5 Concluding Remarks

4.6 Bibliographical and Historical Notes

4.7 Problems

5 Locating Objects via Their Edges

5.1 Introduction

5.2 Basic Theory of Edge Detection

5.3 The Template Matching Approach

5.4 Theory of 3 x 3 Template Operators

5.5 Summary—Design Constraints and Conclusions

5.6 The Design of Differential Gradient Operators

5.7 The Concept of a Circular Operator

5.8 Detailed Implementation of Circular Operators

5.9 Structured Bands of Pixels in Neighbourhoods of Various Sizes

5.10 The Systematic Design of Differential Edge Operators

5.11 Problems with the Above Approach—Some Alternative Schemes

5.12 Concluding Remarks

5.13 Bibliographical and Historical Notes

5.14 Problems

6 Binary Shape Analysis

6.1 Introduction

6.2 Connectedness in Binary Images

6.3 Object Labelling and Counting

6.4 Metric Properties in Digital Images

6.5 Size Filtering

6.6 The Convex Hull and its Computation

6.7 Distance Functions and their Uses

6.8 Skeletons and Thinning

6.9 Some Simple Measures for Shape Recognition

6.10 Shape Description by Moments

6.11 Boundary Tracking Procedures

6.12 Concluding Remarks

6.13 Bibliographical and Historical Notes

6.14 Problems

7 Boundary Pattern Analysis

7.1 Introduction

7.2 Boundary Tracking Procedures

7.3 Template Matching—a Reminder

7.4 Centroidal Profiles

7.5 Problems with the Centroidal Profile Approach

7.6 The (s,ψ) Plot

7.7 Tackling the Problems of Occlusion

7.8 Chain Code

7.9 The (r,s) Plot

7.10 Accuracy of Boundary Length Measures

7.11 Concluding Remarks

7.12 Bibliographical and Historical Notes

7.13 Problems

Part 2 Intermediate-Level Processing

8 Line Detection

8.1 Introduction

8.2 Application of the Hough Transform to Line Detection

8.3 The Foot-of-Normal Method

8.4 Longitudinal Line Localization

8.5 Final Line Fitting

8.6 Concluding Remarks

8.7 Bibliographical and Historical Notes

8.8 Problem

9 Circle Detection

9.1 Introduction

9.2 Hough-Based Schemes for Circular Object Detection

9.3 The Problem of Unknown Circle Radius

9.4 The Problem of Accurate Centre Location

9.5 Overcoming the Speed Problem

9.6 Concluding Remarks

9.7 Bibliographical and Historical Notes

9.8 Problem

10 The Hough Transform and Its Nature

10.1 Introduction

10.2 The Generalized Hough Transform

10.3 Setting up the Generalized Hough Transform—Some Relevant Questions

10.4 Spatial Matched Filtering in Images

10.5 From Spatial Matched Filters to Generalized Hough Transforms

10.6 Gradient Weighting Versus Uniform Weighting

10.7 Summary

10.8 Applying the Generalized Hough Transform to Line Detection

10.9 An Instructive Example

10.10 Tradeoffs to Reduce Computational Load

10.11 The Effects of Occlusions for Objects with Straight Edges

10.12 Fast Implementations of the Hough Transform

10.13 The Approach of Gerig and Klein

10.14 Concluding Remarks

10.15 Bibliographical and Historical Notes

11 Ellipse Detection

11.1 Introduction

11.2 The Diameter Bisection Method

11.3 The Chord-Tangent Method

11.4 Finding the Remaining Ellipse Parameters

11.5 Reducing Computational Load for the Generalized Hough Transform Method

11.6 Comparing the Various Methods

11.7 Concluding Remarks

11.8 Bibliographical and Historical Notes

11.9 Problems

12 Polygon Detection

12.1 Introduction

12.2 The Generalized Hough Transform

12.3 Application to the Detection of Regular Polygons

12.4 The Case of an Arbitrary Triangle

12.5 The Case of an Arbitrary Rectangle

12.6 Lower Bounds on the Numbers of Parameter Planes

12.7 An Extension of the Triangle Result

12.8 Discussion

12.9 Determining Orientation

12.10 Concluding Remarks

12.11 Bibliographical and Historical Notes

12.12 Problems

13 Hole Detection

13.1 Introduction

13.2 The Template Matching Approach

13.3 The Lateral Histogram Technique

13.4 The Removal of Ambiguities in the Lateral Histogram Technique

13.5 Application of the Lateral Histogram Technique for Object Location

13.6 A Strategy Based on Applying the Histograms in Turn

13.7 Appraisal of the Hole Detection Problem

13.8 Concluding Remarks

13.9 Bibliographical and Historical Notes

13.10 Problems

14 Corner Detection

14.1 Introduction

14.2 Template Matching

14.3 Second-Order Derivative Schemes

14.4 A Median-Based Corner Detector

14.5 The Hough Transform Approach to Corner Detection

14.6 The Lateral Histogram Approach to Corner Detection

14.7 Corner Orientation

14.8 Concluding Remarks

14.9 Bibliographical and Historical Notes

14.10 Problems

Part 3 Application-Level Processing

15 Abstract Pattern Matching Techniques

15.1 Introduction

15.2 A Graph-Theoretic Approach to Object Location

15.3 Possibilities for Saving Computation

15.4 Using the Generalized Hough Transform for Feature Collation

15.5 Generalizing the Maximal Clique and Other Approaches

15.6 Relational Descriptors

15.7 Search

15.8 Concluding Remarks

15.9 Bibliographical and Historical Notes

15.10 Problems

16 The Three-Dimensional World

16.1 Introduction

16.2 Three-Dimensional Vision—the Variety of Methods

16.3 Projection Schemes for Three-Dimensional Vision

16.4 Shape from Shading

16.5 Photometric Stereo

16.6 The Assumption of Surface Smoothness

16.7 Shape from Texture

16.8 Use of Structured Lighting

16.9 Three-Dimensional Object Recognition Schemes

16.10 The Method of Ballard and Sabbah

16.11 The Method of Silberberg et al.

16.12 Horaud's Junction Orientation Technique

16.13 The 3DPO System of Bolles and Horaud

16.14 The IVISM System

16.15 Lowe's Approach

16.16 Motion and Optical Flow

16.17 Concluding Remarks

16.18 Bibliographical and Historical Notes

16.19 Problems

17 Automated Visual Inspection

17.1 Introduction

17.2 The Process of Inspection

17.3 Review of the Types of Object to be Inspected

17.4 Summary-the Main Categories of Inspection

17.5 Shape Deviations Relative to a Standard Template

17.6 Inspection of Circular Products

17.7 Inspection of Printed Circuits

17.8 Steel Strip and Wood Inspection

17.9 Bringing Inspection to the Factory

17.10 Concluding Remarks

17.11 Bibliographical and Historical Notes

18 Statistical Pattern Recognition

18.1 Introduction

18.2 The Nearest Neighbour Algorithm

18.3 Bayes' Decision Theory

18.4 Relation of the Nearest Neighbour and Bayes' Approaches

18.5 The Optimum Number of Features

18.6 Cost Functions and Error-Reject Tradeoff

18.7 The Relevance of Probability in Image Analysis

18.8 Concluding Remarks

18.9 Bibliographical and Historical Notes

18.10 Problems

19 Image Acquisition

19.1 Introduction

19.2 Illumination Schemes

19.3 Cameras and Digitization

19.4 The Sampling Theorem

19.5 Concluding Remarks

19.6 Bibliographical and Historical Notes

20 The Need for Speed: Real-Time Electronic Hardware Systems

20.1 Introduction

20.2 Parallel Processing

20.3 SIMD Systems

20.4 The Gain in Speed Attainable with N Processors

20.5 Flynn's Classification

20.6 Optimal Implementation of an Image Analysis Algorithm

20.7 Board-Level Processing Systems

20.8 VLSI

20.9 Concluding Remarks

20.10 Bibliographical and Historical Notes

Part 4 Perspectives on Vision

21 Machine Vision, Art or Science?

21.1 Introduction

21.2 Parameters of Importance in Machine Vision

21.3 Tradeoffs

21.4 Future Directions

21.5 Hardware, Algorithms and Processes

21.6 A Retrospective View

21.7 Just a Glimpse of Vision?

21.8 Bibliographical and Historical Notes


Programming Notation

A.1 Introduction

A.2 The Pascal Language

A.3 Special Syntax Embedded in Pascal

A.4 On the Validity of the "Repeat until Finished" Construct


Subject Index

Author Index


No. of pages:
© Academic Press 1990
Academic Press
eBook ISBN:

About the Author

E. R. Davies

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.

Affiliations and Expertise

Royal Holloway, University of London, UK

About the Editor

P. G. Farrell

J. R. Forrest

Ratings and Reviews