Front Cover -- Computer and Machine Vision: Theory, Algorithms, Practicalities -- Copyright Page -- Contents -- Foreword -- Preface -- About the Author -- Acknowledgements -- Glossary of Acronyms and Abbreviations -- 1. Vision, the Challenge -- 1.1 Introduction-Man and His Senses -- 1.2 The Nature of Vision -- 1.2.1 The Process of Recognition -- 1.2.2 Tackling the Recognition Problem -- 1.2.3 Object Location -- 1.2.4 Scene Analysis -- 1.2.5 Vision as Inverse Graphics -- 1.3 From Automated Visual Inspection to Surveillance -- 1.4 What This Book is About -- 1.5 The Following Chapters -- 1.6 Bibliographical Notes -- 1. Low-Level Vision -- 2 Images and Imaging Operations -- 2.1 Introduction -- 2.1.1 Gray Scale Versus Color -- 2.2 Image Processing Operations -- 2.2.1 Some Basic Operations on Grayscale Images -- 2.2.2 Basic Operations on Binary Images -- 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 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 -- 3.8.1 Continuum Model of Median Shifts -- 3.8.2 Generalization to Grayscale Images -- 3.8.3 Problems with Statistics -- 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 -- 3.12.1 Shifts in Rectangular Neighborhoods -- 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 -- 3.16.1 More Recent Developments.

3.17 Problems -- 4 Thresholding Techniques -- 4.1 Introduction -- 4.2 Region-Growing Methods -- 4.3 Thresholding -- 4.3.1 Finding a Suitable Threshold -- 4.3.2 Tackling the Problem of Bias in Threshold Selection -- 4.3.2.1 Methods Based on Finding a Valley in the Intensity Distribution -- 4.3.3 Summary -- 4.4 Adaptive Thresholding -- 4.4.1 The Chow and Kaneko Approach -- 4.4.2 Local Thresholding Methods -- 4.5 More Thoroughgoing Approaches to Threshold Selection -- 4.5.1 Variance-Based Thresholding -- 4.5.2 Entropy-Based Thresholding -- 4.5.3 Maximum Likelihood Thresholding -- 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 -- 4.10.1 More Recent Developments -- 4.11 Problems -- 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 Operators -- 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 -- 5.18.1 More Recent Developments -- 5.19 Problems -- 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.

6.4.1 Analyzing the Operation of the Median Detector -- 6.4.2 Practical Results -- 6.5 The Harris Interest Point Operator -- 6.5.1 Corner Signals and Shifts for Various Geometric Configurations -- 6.5.2 Performance with Crossing Points and Junctions -- 6.5.3 Different Forms of the Harris Operator -- 6.6 Corner Orientation -- 6.7 Local Invariant Feature Detectors and Descriptors -- 6.7.1 Harris Scale and Affine-Invariant Detectors and Descriptors -- 6.7.2 Hessian Scale and Affine-Invariant Detectors and Descriptors -- 6.7.3 The SIFT Operator -- 6.7.4 The SURF Operator -- 6.7.5 Maximally Stable Extremal Regions -- 6.7.6 Comparison of the Various Invariant Feature Detectors -- 6.8 Concluding Remarks -- 6.9 Bibliographical and Historical Notes -- 6.9.1 More Recent Developments -- 6.10 Problems -- 7 Mathematical Morphology -- 7.1 Introduction -- 7.2 Dilation and Erosion in Binary Images -- 7.2.1 Dilation and Erosion -- 7.2.2 Cancellation Effects -- 7.2.3 Modified Dilation and Erosion Operators -- 7.3 Mathematical Morphology -- 7.3.1 Generalized Morphological Dilation -- 7.3.2 Generalized Morphological Erosion -- 7.3.3 Duality Between Dilation and Erosion -- 7.3.4 Properties of Dilation and Erosion Operators -- 7.3.5 Closing and Opening -- 7.3.6 Summary of Basic Morphological Operations -- 7.4 Grayscale Processing -- 7.4.1 Morphological Edge Enhancement -- 7.4.2 Further Remarks on the Generalization to Grayscale Processing -- 7.5 Effect of Noise on Morphological Grouping Operations -- 7.5.1 Detailed Analysis -- 7.5.2 Discussion -- 7.6 Concluding Remarks -- 7.7 Bibliographical and Historical Notes -- 7.7.1 More Recent Developments -- 7.8 Problem -- 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 -- 8.8.1 More Recent Developments -- 2. Intermediate-Level Vision -- 9 Binary Shape Analysis -- 9.1 Introduction -- 9.2 Connectedness in Binary Images -- 9.3 Object Labeling and Counting -- 9.3.1 Solving the Labeling Problem in a More Complex Case -- 9.4 Size Filtering -- 9.5 Distance Functions and Their Uses -- 9.5.1 Local Maxima and Data Compression -- 9.6 Skeletons and Thinning -- 9.6.1 Crossing Number -- 9.6.2 Parallel and Sequential Implementations of Thinning -- 9.6.3 Guided Thinning -- 9.6.4 A Comment on the Nature of the Skeleton -- 9.6.5 Skeleton Node Analysis -- 9.6.6 Application of Skeletons for Shape Recognition -- 9.7 Other Measures for Shape Recognition -- 9.8 Boundary Tracking Procedures -- 9.9 Concluding Remarks -- 9.10 Bibliographical and Historical Notes -- 9.10.1 More Recent Developments -- 9.11 Problems -- 10 Boundary Pattern Analysis -- 10.1 Introduction -- 10.2 Boundary Tracking Procedures -- 10.3 Centroidal Profiles -- 10.4 Problems with the Centroidal Profile Approach -- 10.4.1 Some Solutions -- 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 -- 10.9.1 More Recent Developments -- 10.10 Problems -- 11 Line Detection -- 11.1 Introduction -- 11.2 Application of the Hough Transform to Line Detection -- 11.3 The Foot-of-Normal Method -- 11.3.1 Application of 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 -- 11.9.1 More Recent Developments -- 11.10 Problems.

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 -- 12.3.1 Some Practical Results -- 12.4 The Problem of Accurate Center Location -- 12.4.1 A Solution Requiring Minimal Computation -- 12.5 Overcoming the Speed Problem -- 12.5.1 More Detailed Estimates of Speed -- 12.5.2 Robustness -- 12.5.3 Practical Results -- 12.5.4 Summary -- 12.6 Ellipse Detection -- 12.6.1 The Diameter Bisection Method -- 12.6.2 The Chord-Tangent Method -- 12.6.3 Finding the Remaining Ellipse Parameters -- 12.7 Human Iris Location -- 12.8 Hole Detection -- 12.9 Concluding Remarks -- 12.10 Bibliographical and Historical Notes -- 12.10.1 More Recent Developments -- 12.11 Problems -- 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 -- 13.6.1 Calculation of Sensitivity and Computational Load -- 13.7 Summary -- 13.8 Use of the GHT for Ellipse Detection -- 13.8.1 Practical Details -- 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 -- 13.13.1 More Recent Developments -- 13.14 Problems -- 14 Pattern Matching Techniques -- 14.1 Introduction -- 14.2 A Graph-Theoretic Approach to Object Location -- 14.2.1 A Practical Example-Locating Cream Biscuits -- 14.3 Possibilities for Saving Computation -- 14.4 Using the Generalized Hough Transform for Feature Collation -- 14.4.1 Computational Load -- 14.5 Generalizing the Maximal Clique and Other Approaches.

14.6 Relational Descriptors.