Computer and machine vision : theory, algorithms, practicalities.

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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....

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Davies, E. R. (E. Roy)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Waltham : Academic Press, 2012.
©2012
Edición:4th ed.
Temas:
Computer vision.
Computer algorithms.
Algorithms.
Vision par ordinateur.
Algorithmes.
algorithms.
Algorithms
Computer algorithms
Computer vision
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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.
  • 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.

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