Umfang:
1 online resource (902 pages)
Ausgabe:
5th ed
ISBN:
9780128095751
Anmerkung:
Front Cover -- Computer Vision -- Copyright Page -- Dedication -- Contents -- About the Author -- Foreword -- Preface to the Fifth Edition -- Preface to the First Edition -- Acknowledgments -- Topics Covered in Application Case Studies -- Influences Impinging Upon Integrated Vision System Design -- 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 Part Played by Machine Learning -- 1.6 The Following Chapters -- 1.7 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 O
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3.11.3 Modified Dilation and Erosion Operators -- 3.12 Mathematical Morphology -- 3.12.1 Generalized Morphological Dilation -- 3.12.2 Generalized Morphological Erosion -- 3.12.3 Duality Between Dilation and Erosion -- 3.12.4 Properties of Dilation and Erosion Operators -- 3.12.5 Closing and Opening -- 3.12.6 Summary of Basic Morphological Operations -- 3.13 Morphological Grouping -- 3.14 Morphology in Grayscale Images -- 3.15 Concluding Remarks -- 3.16 Bibliographical and Historical Notes -- 3.16.1 More Recent Developments -- 3.17 Problems -- 4 The role of thresholding -- 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 -- 4.4 Adaptive Thresholding -- 4.4.1 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 Gl
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5.14.1 More Recent Developments -- 5.15 Problems -- 6 Corner, interest point, and invariant feature 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 T-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 Geometric Transformations and Feature Normalization -- 6.7.2 Harris Scale and Affine Invariant Detectors and Descriptors -- 6.7.3 Hessian Scale and Affine Invariant Detectors and Descriptors -- 6.7.4 The Scale Invariant Feature Transforms Operator -- 6.7.5 The Speeded-Up Robust Features Operator -- 6.7.6 Maximally Stable Extremal Regions -- 6.7.7 Comparison of
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8.6 Skeletons and Thinning -- 8.6.1 Crossing Number -- 8.6.2 Parallel and Sequential Implementations of Thinning -- 8.6.3 Guided Thinning -- 8.6.4 A Comment on the Nature of the Skeleton -- 8.6.5 Skeleton Node Analysis -- 8.6.6 Application of Skeletons for Shape Recognition -- 8.7 Other Measures for Shape Recognition -- 8.8 Boundary Tracking Procedures -- 8.9 Concluding Remarks -- 8.10 Bibliographical and Historical Notes -- 8.10.1 More Recent Developments -- 8.11 Problems -- 9 Boundary pattern analysis -- 9.1 Introduction -- 9.2 Boundary Tracking Procedures -- 9.3 Centroidal Profiles -- 9.4 Problems With the Centroidal Profile Approach -- 9.4.1 Some Solutions -- 9.5 The (s,ψ) Plot -- 9.6 Tackling the Problems of Occlusion -- 9.7 Accuracy of Boundary Length Measures -- 9.8 Concluding Remarks -- 9.9 Bibliographical and Historical Notes -- 9.9.1 More Recent Developments -- 9.10 Problems -- 10 Line, circle, and ellipse detection -- 10.1 Introduction -- 10.2 Application of the Hough Tr
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11.3 The Relevance of Spatial Matched Filtering -- 11.4 Gradient Weighting Versus Uniform Weighting -- 11.4.1 Calculation of Sensitivity and Computational Load -- 11.4.2 Summary -- 11.5 Use of the GHT for Ellipse Detection -- 11.5.1 Practical Details -- 11.6 Comparing the Various Methods for Ellipse Detection -- 11.7 A Graph-Theoretic Approach to Object Location -- 11.7.1 A Practical Example-Locating Cream Biscuits -- 11.8 Possibilities for Saving Computation -- 11.9 Using the GHT for Feature Collation -- 11.9.1 Computational Load -- 11.10 Generalizing the Maximal Clique and Other Approaches -- 11.11 Search -- 11.12 Concluding Remarks -- 11.13 Bibliographical and Historical Notes -- 11.13.1 More Recent Developments -- 11.14 Problems -- 12 Object segmentation and shape models -- 12.1 Introduction -- 12.2 Active Contours -- 12.3 Practical Results Obtained Using Active Contours -- 12.4 The Level-Set Approach to Object Segmentation -- 12.5 Shape Models -- 12.5.1 Locating Objects Using S
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13.15 Bibliographical and Historical Notes
Sprache:
Englisch
