Computer and Machine Vision

Computer and Machine Vision

Theory, Algorithms, Practicalities

4th Edition - March 5, 2012

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  • Author: E. R. Davies
  • Hardcover ISBN: 9780123869081
  • eBook ISBN: 9780123869913

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Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject. Key features include: Practical examples and case studies give the ‘ins and outs’ of developing real-world vision systems, giving engineers the realities of implementing the principles in practice New chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer vision Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging The ‘recent developments’ section now included in each chapter will be useful in bringing students and practitioners up to date with the subject

Key Features

  • Mathematics and essential theory are made approachable by careful explanations and well-illustrated examples
  • Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging
  • The ‘recent developments’ section now included in each chapter will be useful in bringing students and practitioners up to date with the subject


Embedded, electronic systems, signal/image processing and computer engineering R&D engineers; post graduates and PhD researchers in machine and computer vision

Table of Contents

  • Dedication

    Topics Covered in Application Case Studies

    Influences Impinging upon Integrated Vision System Design



    About the Author


    Glossary of Acronyms and Abbreviations

    Chapter 1. Vision, the Challenge

    1.1 Introduction—Man and His Senses

    1.2 The Nature of Vision

    1.3 From Automated Visual Inspection to Surveillance

    1.4 What This Book is About

    1.5 The Following Chapters

    1.6 Bibliographical Notes

    PART 1. Low-level Vision

    Chapter 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

    Chapter 3. Basic Image Filtering Operations

    3.1 Introduction

    3.2 Noise Suppression by Gaussian Smoothing

    3.3 Median Filters

    3.4 Mode Filters

    3.5 Rank Order Filters

    3.6 Reducing Computational Load

    3.7 Sharp–Unsharp Masking

    3.8 Shifts Introduced by Median Filters

    3.9 Discrete Model of Median Shifts

    3.10 Shifts Introduced by Mode Filters

    3.11 Shifts Introduced by Mean and Gaussian Filters

    3.12 Shifts Introduced by Rank Order Filters

    3.13 The Role of Filters in Industrial Applications of Vision

    3.14 Color in Image Filtering

    3.15 Concluding Remarks

    3.16 Bibliographical and Historical Notes

    3.17 Problems

    Chapter 4. Thresholding Techniques

    4.1 Introduction

    4.2 Region-Growing Methods

    4.3 Thresholding

    4.4 Adaptive Thresholding

    4.5 More Thoroughgoing Approaches to Threshold Selection

    4.6 The Global Valley Approach to Thresholding

    4.7 Practical Results Obtained Using the Global Valley Method

    4.8 Histogram Concavity Analysis

    4.9 Concluding Remarks

    4.10 Bibliographical and Historical Notes

    4.11 Problems

    Chapter 5. Edge Detection

    5.1 Introduction

    5.2 Basic Theory of Edge Detection

    5.3 The Template Matching Approach

    5.4 Theory of 3×3 Template Operators

    5.5 The Design of Differential Gradient Operators

    5.6 The Concept of a Circular Operator

    5.7 Detailed Implementation of Circular Operators

    5.8 The Systematic Design of Differential Edge Operators

    5.9 Problems with the Above Approach—Some Alternative Schemes

    5.10 Hysteresis Thresholding

    5.11 The Canny Operator

    5.12 The Laplacian Operator

    5.13 Active Contours

    5.14 Practical Results Obtained Using Active Contours

    5.15 The Level Set Approach to Object Segmentation

    5.16 The Graph Cut Approach to Object Segmentation

    5.17 Concluding Remarks

    5.18 Bibliographical and Historical Notes

    5.19 Problems

    Chapter 6. Corner and Interest Point Detection

    6.1 Introduction

    6.2 Template Matching

    6.3 Second-Order Derivative Schemes

    6.4 A Median Filter-Based Corner Detector

    6.5 The Harris Interest Point Operator

    6.6 Corner Orientation

    6.7 Local Invariant Feature Detectors and Descriptors

    6.8 Concluding Remarks

    6.9 Bibliographical and Historical Notes

    6.10 Problems

    Chapter 7. Mathematical Morphology

    7.1 Introduction

    7.2 Dilation and Erosion in Binary Images

    7.3 Mathematical Morphology

    7.4 Grayscale Processing

    7.5 Effect of Noise on Morphological Grouping Operations

    7.6 Concluding Remarks

    7.7 Bibliographical and Historical Notes

    7.8 Problem

    Chapter 8. Texture

    8.1 Introduction

    8.2 Some Basic Approaches to Texture Analysis

    8.3 Graylevel Co-Occurrence Matrices

    8.4 Laws’ Texture Energy Approach

    8.5 Ade’s Eigenfilter Approach

    8.6 Appraisal of the Laws and Ade Approaches

    8.7 Concluding Remarks

    8.8 Bibliographical and Historical Notes

    PART 2. Intermediate-level Vision

    Chapter 9. Binary Shape Analysis

    9.1 Introduction

    9.2 Connectedness in Binary Images

    9.3 Object Labeling and Counting

    9.4 Size Filtering

    9.5 Distance Functions and their Uses

    9.6 Skeletons and Thinning

    9.7 Other Measures for Shape Recognition

    9.8 Boundary Tracking Procedures

    9.9 Concluding Remarks

    9.10 Bibliographical and Historical Notes

    9.11 Problems

    Chapter 10. Boundary Pattern Analysis

    10.1 Introduction

    10.2 Boundary Tracking Procedures

    10.3 Centroidal Profiles

    10.4 Problems with the Centroidal Profile Approach

    10.5 The (s, ψ) Plot

    10.6 Tackling the Problems of Occlusion

    10.7 Accuracy of Boundary Length Measures

    10.8 Concluding Remarks

    10.9 Bibliographical and Historical Notes

    10.10 Problems

    Chapter 11. Line Detection

    11.1 Introduction

    11.2 Application of the Hough Transform to Line Detection

    11.3 The Foot-of-Normal Method

    11.4 Longitudinal Line Localization

    11.5 Final Line Fitting

    11.6 Using RANSAC for Straight Line Detection

    11.7 Location of Laparoscopic Tools

    11.8 Concluding Remarks

    11.9 Bibliographical and Historical Notes

    11.10 Problems

    Chapter 12. Circle and Ellipse Detection

    12.1 Introduction

    12.2 Hough-Based Schemes for Circular Object Detection

    12.3 The Problem of Unknown Circle Radius

    12.4 The Problem of Accurate Center Location

    12.5 Overcoming the Speed Problem

    12.6 Ellipse Detection

    12.7 Human Iris Location

    12.8 Hole Detection

    12.9 Concluding Remarks

    12.10 Bibliographical and Historical Notes

    12.11 Problems

    Chapter 13. The Hough Transform and Its Nature

    13.1 Introduction

    13.2 The Generalized Hough Transform

    13.3 Setting Up the Generalized Hough Transform—Some Relevant Questions

    13.4 Spatial Matched Filtering in Images

    13.5 From Spatial Matched Filters to Generalized Hough Transforms

    13.6 Gradient Weighting Versus Uniform Weighting

    13.7 Summary

    13.8 Use of the GHT for Ellipse Detection

    13.9 Comparing the Various Methods

    13.10 Fast Implementations of the Hough Transform

    13.11 The Approach of Gerig and Klein

    13.12 Concluding Remarks

    13.13 Bibliographical and Historical Notes

    13.14 Problems

    Chapter 14. Pattern Matching Techniques

    14.1 Introduction

    14.2 A Graph-Theoretic Approach to Object Location

    14.3 Possibilities for Saving Computation

    14.4 Using the Generalized Hough Transform for Feature Collation

    14.5 Generalizing the Maximal Clique and Other Approaches

    14.6 Relational Descriptors

    14.7 Search

    14.8 Concluding Remarks

    14.9 Bibliographical and Historical Notes

    14.10 Problems

    PART 3. 3-D Vision and Motion

    Chapter 15. The Three-Dimensional World

    3-D vision

    15.1 Introduction

    15.2 3-D Vision—The Variety of Methods

    15.3 Projection Schemes for Three-Dimensional Vision

    15.4 Shape from Shading

    15.5 Photometric Stereo

    15.6 The Assumption of Surface Smoothness

    15.7 Shape from Texture

    15.8 Use of Structured Lighting

    15.9 Three-Dimensional Object Recognition Schemes

    15.10 Horaud’s Junction Orientation Technique4

    15.11 An Important Paradigm—Location of Industrial Parts

    15.12 Concluding Remarks

    15.13 Bibliographical and Historical Notes

    15.14 Problems

    Chapter 16. Tackling the Perspective -point Problem

    16.1 Introduction

    16.2 The Phenomenon of Perspective Inversion

    16.3 Ambiguity of Pose Under Weak Perspective Projection

    16.4 Obtaining Unique Solutions to the Pose Problem

    16.5 Concluding Remarks

    16.6 Bibliographical and Historical Notes

    16.7 Problems

    Chapter 17. Invariants and Perspective

    17.1 Introduction

    17.2 Cross-Ratios: The “Ratio of Ratios” Concept

    17.3 Invariants for Noncollinear Points

    17.4 Invariants for Points on Conics

    17.5 Differential and Semi-Differential Invariants

    17.6 Symmetric Cross-Ratio Functions

    17.7 Vanishing Point Detection

    17.8 More on Vanishing Points

    17.9 Apparent Centers of Circles and Ellipses

    17.10 The Route to Face Recognition

    17.11 Perspective Effects in Art and Photography*

    17.12 Concluding Remarks

    17.13 Bibliographical and Historical Notes

    17.14 Problems

    Chapter 18. Image Transformations and Camera Calibration

    18.1 Introduction

    18.2 Image Transformations

    18.3 Camera Calibration

    18.4 Intrinsic and Extrinsic Parameters

    18.5 Correcting for Radial Distortions

    18.6 Multiple View Vision

    18.7 Generalized Epipolar Geometry

    18.8 The Essential Matrix

    18.9 The Fundamental Matrix

    18.10 Properties of the Essential and Fundamental Matrices

    18.11 Estimating the Fundamental Matrix

    18.12 An Update on the Eight-Point Algorithm

    18.13 Image Rectification

    18.14 3-D Reconstruction

    18.15 Concluding Remarks

    18.16 Bibliographical and Historical Notes

    18.17 Problems

    Chapter 19. Motion

    19.1 Introduction

    19.2 Optical Flow

    19.3 Interpretation of Optical Flow Fields

    19.4 Using Focus of Expansion to Avoid Collision

    19.5 Time-To-Adjacency Analysis

    19.6 Basic Difficulties with the Optical Flow Model

    19.7 Stereo from Motion

    19.8 The Kalman Filter

    19.9 Wide Baseline Matching

    19.10 Concluding Remarks

    19.11 Bibliographical and Historical Notes

    19.12 Problem

    PART 4. Toward Real-time Pattern Recognition Systems

    Chapter 20. Automated Visual Inspection

    20.1 Introduction

    20.2 The Process of Inspection

    20.3 The Types of Object to be Inspected

    20.4 Summary: The Main Categories of Inspection

    20.5 Shape Deviations Relative to a Standard Template

    20.6 Inspection of Circular Products

    20.7 Inspection of Printed Circuits

    20.8 Steel Strip and Wood Inspection

    20.9 Inspection of Products with High Levels of Variability

    20.10 X-Ray Inspection

    20.11 The Importance of Color in Inspection

    20.12 Bringing Inspection to the Factory

    20.13 Concluding Remarks

    20.14 Bibliographical and Historical Notes

    Chapter 21. Inspection of Cereal Grains

    21.1 Introduction

    21.2 Case Study: Location of Dark Contaminants in Cereals

    21.3 Case Study: Location of Insects

    21.4 Case Study: High-Speed Grain Location

    21.5 Optimizing the Output for Sets of Directional Template Masks

    21.6 Concluding Remarks

    21.7 Bibliographical and Historical Notes

    Chapter 22. Surveillance

    22.1 Introduction

    22.2 Surveillance—The Basic Geometry

    22.3 Foreground–Background Separation

    22.4 Particle Filters

    22.5 Use of Color Histograms for Tracking

    22.6 Implementation of Particle Filters

    22.7 Chamfer Matching, Tracking, and Occlusion

    22.8 Combining Views from Multiple Cameras

    22.9 Applications to the Monitoring of Traffic Flow

    22.10 License Plate Location

    22.11 Occlusion Classification for Tracking

    22.12 Distinguishing Pedestrians by their Gait

    22.13 Human Gait Analysis

    22.14 Model-Based Tracking of Animals

    22.15 Concluding Remarks

    22.16 Bibliographical and Historical Notes

    22.17 Problem

    Chapter 23. In-Vehicle Vision Systems

    23.1 Introduction

    23.2 Locating the Roadway

    23.3 Location of Road Markings

    23.4 Location of Road Signs

    23.5 Location of Vehicles

    23.6 Information Obtained by Viewing Licence Plates and Other Structural Features

    23.7 Locating Pedestrians

    23.8 Guidance and Egomotion

    23.9 Vehicle Guidance in Agriculture

    23.10 Concluding Remarks

    23.11 More Detailed Developments and Bibliographies Relating to Advanced Driver Assistance Systems

    23.12 Problem

    Chapter 24. Statistical Pattern Recognition

    24.1 Introduction

    24.2 The Nearest Neighbor Algorithm

    24.3 Bayes’ Decision Theory

    24.4 Relation of the Nearest Neighbor and Bayes’ Approaches

    24.5 The Optimum Number of Features

    24.6 Cost Functions and Error–Reject Tradeoff

    24.7 The Receiver Operating Characteristic

    24.8 Multiple Classifiers

    24.9 Cluster Analysis

    24.10 Principal Components Analysis

    24.11 The Relevance of Probability in Image Analysis

    24.12 Another Look at Statistical Pattern Recognition: The Support Vector Machine

    24.13 Artificial Neural Networks

    24.14 The Back-Propagation Algorithm

    24.15 MLP Architectures

    24.16 Overfitting to the Training Data

    24.17 Concluding Remarks

    24.18 Bibliographical and Historical Notes

    24.19 Problems

    Chapter 25. Image Acquisition

    25.1 Introduction

    25.2 Illumination Schemes

    25.3 Cameras and Digitization

    25.4 The Sampling Theorem

    25.5 Hyperspectral Imaging

    25.6 Concluding Remarks

    25.7 Bibliographical and Historical Notes

    Chapter 26. Real-Time Hardware and Systems Design Considerations

    26.1 Introduction

    26.2 Parallel Processing

    26.3 SIMD Systems

    26.4 The Gain in Speed Attainable with N Processors

    26.5 Flynn’s Classification

    26.6 Optimal Implementation of Image Analysis Algorithms

    26.7 Some Useful Real-Time Hardware Options

    26.8 Systems Design Considerations

    26.9 Design of Inspection Systems—the Status Quo

    26.10 System Optimization

    26.11 Concluding Remarks

    26.12 Bibliographical and Historical Notes7

    Chapter 27. Epilogue—Perspectives in Vision

    27.1 Introduction

    27.2 Parameters of Importance in Machine Vision

    27.3 Tradeoffs

    27.4 Moore’s Law in Action

    27.5 Hardware, Algorithms, and Processes

    27.6 The Importance of Choice of Representation

    27.7 Past, Present, and Future

    27.8 Bibliographical and Historical Notes

    APPENDIX A. Robust Statistics

    A.1 Introduction

    A.2 Preliminary Definitions and Analysis

    A.3 The M-Estimator (Influence Function) Approach

    A.4 The Least Median of Squares Approach to Regression

    A.5 Overview of the Robustness Problem

    A.6 The RANSAC Approach

    A.7 Concluding Remarks

    A.8 Bibliographical and Historical Notes

    A.9 Problem


    Author Index

    Subject Index

Product details

  • No. of pages: 912
  • Language: English
  • Copyright: © Academic Press 2012
  • Published: March 5, 2012
  • Imprint: Academic Press
  • Hardcover ISBN: 9780123869081
  • eBook ISBN: 9780123869913

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

Emeritus Professor of Machine Vision, Royal Holloway, University of London, UK

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