Computer and machine vision : theory, algorithms, practicalities
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....
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| Main Author | |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Amsterdam ; Tokyo
Elsevier
2012
Elsevier Science & Technology Academic Press |
| Edition | 4 |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. 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 and crime detection. He has published more than 200 papers, and three books - Machine Vision: Theory, Algorithms, Practicalities (1990), Electronics, Noise and Signal Recovery (1993), and Image Processing for the Food Industry (2000); the first of these has been widely used internationally for more than 20 years, and is now out in this much enhanced fourth 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.
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. |
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| AbstractList | 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. 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 and crime detection. He has published more than 200 papers, and three books - Machine Vision: Theory, Algorithms, Practicalities (1990), Electronics, Noise and Signal Recovery (1993), and Image Processing for the Food Industry (2000); the first of these has been widely used internationally for more than 20 years, and is now out in this much enhanced fourth 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.
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. |
| Author | Davies, E. R. |
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| Notes | Includes bibliographical references (p. 796-844) and indexes |
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| PublicationDate | 2012 2012-04-18 |
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| PublicationPlace | Amsterdam ; Tokyo |
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| PublicationYear | 2012 |
| Publisher | Elsevier Elsevier Science & Technology Academic Press |
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| Snippet | Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology... |
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| SubjectTerms | Computer algorithms Computer vision Information visualization |
| TableOfContents | 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 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 14.6 Relational Descriptors 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 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 |
| Title | Computer and machine vision : theory, algorithms, practicalities |
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