Details

Autor(en) / Beteiligte

• Davies, E. R

Titel

Computer Vision : Principles, Algorithms, Applications, Learning

Auflage

5th ed

Ort / Verlag

San Diego : Elsevier Science & Technology,

Erscheinungsjahr

2017

Beschreibungen/Notizen

• 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 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 Image filtering and morphology -- 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 Sharp-Unsharp Masking -- 3.7 Shifts Introduced by Median Filters -- 3.7.1 Continuum Model of Median Shifts -- 3.7.2 Generalization to Grayscale Images -- 3.7.3 Discrete Model of Median Shifts -- 3.8 Shifts Introduced by Rank Order Filters -- 3.8.1 Shifts in Rectangular Neighborhoods -- 3.9 The Role of Filters in Industrial Applications of Vision -- 3.10 Color in Image Filtering -- 3.11 Dilation and Erosion in Binary Images -- 3.11.1 Dilation and Erosion -- 3.11.2 Cancellation Effects.
• 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 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 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 Concluding Remarks -- 5.14 Bibliographical and Historical Notes.
• 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 the Various Invariant Feature Detectors -- 6.7.8 Histograms of Oriented Gradients -- 6.8 Concluding Remarks -- 6.9 Bibliographical and Historical Notes -- 6.9.1 More Recent Developments -- 6.10 Problems -- 7 Texture analysis -- 7.1 Introduction -- 7.2 Some Basic Approaches to Texture Analysis -- 7.3 Graylevel Co-occurrence Matrices -- 7.4 Laws' Texture Energy Approach -- 7.5 Ade's Eigenfilter Approach -- 7.6 Appraisal of the Laws and Ade approaches -- 7.7 Concluding Remarks -- 7.8 Bibliographical and Historical Notes -- 7.8.1 More Recent Developments -- 2 Intermediate-level vision -- 8 Binary shape analysis -- 8.1 Introduction -- 8.2 Connectedness in Binary Images -- 8.3 Object Labeling and Counting -- 8.3.1 Solving the Labeling Problem in a More Complex Case -- 8.4 Size Filtering -- 8.5 Distance Functions and Their Uses -- 8.5.1 Local Maxima and Data Compression.
• 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 Transform to Line Detection -- 10.2.1 Longitudinal Line Localization -- 10.3 The Foot-of-Normal Method -- 10.3.1 Application of the Foot-of-Normal Method -- 10.4 Using RANSAC for Straight Line Detection -- 10.5 Location of Laparoscopic Tools -- 10.6 Hough-Based Schemes for Circular Object Detection -- 10.7 The Problem of Unknown Circle Radius -- 10.7.1 Practical Results -- 10.8 Overcoming the Speed Problem -- 10.8.1 Practical Results -- 10.9 Ellipse Detection -- 10.9.1 The Diameter Bisection Method -- 10.9.2 The Chord-Tangent Method -- 10.9.3 Finding the Remaining Ellipse Parameters -- 10.10 Human Iris Location -- 10.11 Concluding Remarks -- 10.12 Bibliographical and Historical Notes -- 10.12.1 More Recent Developments -- 10.13 Problems -- 11 The generalized Hough transform -- 11.1 Introduction -- 11.2 The Generalized Hough Transform.
• 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 Shape Models -- 12.6 Concluding Remarks -- 12.7 Bibliographical and Historical Notes -- 3 Machine learning and deep learning networks -- 13 Basic classification concepts -- 13.1 Introduction -- 13.2 The Nearest Neighbor Algorithm -- 13.3 Bayes' Decision Theory -- 13.3.1 The Naïve Bayes' Classifier -- 13.4 Relation of the Nearest Neighbor and Bayes' Approaches -- 13.4.1 Mathematical Statement of the Problem -- 13.4.2 The Importance of the Nearest Neighbor Algorithm -- 13.5 The Optimum Number of Features -- 13.6 Cost Functions and Error-Reject Tradeoff -- 13.7 Supervised and Unsupervised Learning -- 13.8 Cluster Analysis -- 13.9 The Support Vector Machine -- 13.10 Artificial Neural Networks -- 13.11 The Back-Propagation Algorithm -- 13.12 Multilayer Perceptron Architectures -- 13.13 Overfitting to the Training Data -- 13.14 Concluding Remarks.
• 13.15 Bibliographical and Historical Notes.
• Description based on publisher supplied metadata and other sources.