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Computer and Machine Vision
京&东&价：
作&&&&者：
丛&书&名：Theory, Algorithms, Practicalities
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字&&&&数：423622
正文语种：英文
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本内容由版权提供，版权所有，侵权必究！
Cover Image
Dedication
Topics Covered in Application Case Studies
Influences Impinging upon Integrated Vision System Design
About the Author
Acknowledgements
Glossary of Acronyms and Abbreviations
Chapter 1: Vision, the Challenge
1.1 Introduction―Man and His Senses
1.2 The Nature of Vision
1.3 From Automated Visual Inspection to Surveillance
1.4 What This Book is About
1.5 The Following Chapters
1.6 Bibliographical Notes
PART 1. Low-level Vision
Chapter 2: Images and Imaging Operations
2.1 Introduction
2.2 Image Processing Operations
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
Chapter 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 SharpCUnsharp Masking
3.8 Shifts Introduced by Median Filters
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
3.13 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
3.17 Problems
Chapter 4: Thresholding Techniques
4.1 Introduction
4.2 Region-Growing Methods
4.3 Thresholding
4.4 Adaptive Thresholding
4.5 More Thoroughgoing Approaches to Threshold Selection
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
4.11 Problems
Chapter 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 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
5.19 Problems
Chapter 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
6.5 The Harris Interest Point Operator
6.6 Corner Orientation
6.7 Local Invariant Feature Detectors and Descriptors
6.8 Concluding Remarks
6.9 Bibliographical and Historical Notes
6.10 Problems
Chapter 7: Mathematical Morphology
7.1 Introduction
7.2 Dilation and Erosion in Binary Images
7.3 Mathematical Morphology
7.4 Grayscale Processing
7.5 Effect of Noise on Morphological Grouping Operations
7.6 Concluding Remarks
7.7 Bibliographical and Historical Notes
7.8 Problem
Chapter 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
PART 2. Intermediate-level Vision
Chapter 9: Binary Shape Analysis
9.1 Introduction
9.2 Connectedness in Binary Images
9.3 Object Labeling and Counting
9.4 Size Filtering
9.5 Distance Functions and their Uses
9.6 Skeletons and Thinning
9.7 Other Measures for Shape Recognition
9.8 Boundary Tracking Procedures
9.9 Concluding Remarks
9.10 Bibliographical and Historical Notes
9.11 Problems
Chapter 10: Boundary Pattern Analysis
10.1 Introduction
10.2 Boundary Tracking Procedures
10.3 Centroidal Profiles
10.4 Problems with the Centroidal Profile Approach
10.5 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
10.10 Problems
Chapter 11: Line Detection
11.1 Introduction
11.2 Application of the Hough Transform to Line Detection
11.3 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
11.10 Problems
Chapter 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
12.4 The Problem of Accurate Center Location
12.5 Overcoming the Speed Problem
12.6 Ellipse Detection
12.7 Human Iris Location
12.8 Hole Detection
12.9 Concluding Remarks
12.10 Bibliographical and Historical Notes
12.11 Problems
Chapter 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
13.7 Summary
13.8 Use of the GHT for Ellipse Detection
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
13.14 Problems
Chapter 14: Pattern Matching Techniques
14.1 Introduction
14.2 A Graph-Theoretic Approach to Object Location
14.3 Possibilities for Saving Computation
14.4 Using the Generalized Hough Transform for Feature Collation
14.5 Generalizing the Maximal Clique and Other Approaches
14.6 Relational Descriptors
14.7 Search
14.8 Concluding Remarks
14.9 Bibliographical and Historical Notes
14.10 Problems
PART 3. 3-D Vision and Motion
Chapter 15: The Three-Dimensional World
3-D vision
15.1 Introduction
15.2 3-D Vision―The Variety of Methods
15.3 Projection Schemes for Three-Dimensional Vision
15.4 Shape from Shading
15.5 Photometric Stereo
15.6 The Assumption of Surface Smoothness
15.7 Shape from Texture
15.8 Use of Structured Lighting
15.9 Three-Dimensional Object Recognition Schemes
15.10 Horaud’s Junction Orientation Technique4
15.11 An Important Paradigm―Location of Industrial Parts
15.12 Concluding Remarks
15.13 Bibliographical and Historical Notes
15.14 Problems
Chapter 16: Tackling the Perspective -point Problem
16.1 Introduction
16.2 The Phenomenon of Perspective Inversion
16.3 Ambiguity of Pose Under Weak Perspective Projection
16.4 Obtaining Unique Solutions to the Pose Problem
16.5 Concluding Remarks
16.6 Bibliographical and Historical Notes
16.7 Problems
Chapter 17: Invariants and Perspective
17.1 Introduction
17.2 Cross-Ratios: The “Ratio of Ratios” Concept
17.3 Invariants for Noncollinear Points
17.4 Invariants for Points on Conics
17.5 Differential and Semi-Differential Invariants
17.6 Symmetric Cross-Ratio Functions
17.7 Vanishing Point Detection
17.8 More on Vanishing Points
17.9 Apparent Centers of Circles and Ellipses
17.10 The Route to Face Recognition
17.11 Perspective Effects in Art and Photography*
17.12 Concluding Remarks
17.13 Bibliographical and Historical Notes
17.14 Problems
Chapter 18: Image Transformations and Camera Calibration
18.1 Introduction
18.2 Image Transformations
18.3 Camera Calibration
18.4 Intrinsic and Extrinsic Parameters
18.5 Correcting for Radial Distortions
18.6 Multiple View Vision
18.7 Generalized Epipolar Geometry
18.8 The Essential Matrix
18.9 The Fundamental Matrix
18.10 Properties of the Essential and Fundamental Matrices
18.11 Estimating the Fundamental Matrix
18.12 An Update on the Eight-Point Algorithm
18.13 Image Rectification
18.14 3-D Reconstruction
18.15 Concluding Remarks
18.16 Bibliographical and Historical Notes
18.17 Problems
Chapter 19: Motion
19.1 Introduction
19.2 Optical Flow
19.3 Interpretation of Optical Flow Fields
19.4 Using Focus of Expansion to Avoid Collision
19.5 Time-To-Adjacency Analysis
19.6 Basic Difficulties with the Optical Flow Model
19.7 Stereo from Motion
19.8 The Kalman Filter
19.9 Wide Baseline Matching
19.10 Concluding Remarks
19.11 Bibliographical and Historical Notes
19.12 Problem
PART 4. Toward Real-time Pattern Recognition Systems
Chapter 20: Automated Visual Inspection
20.1 Introduction
20.2 The Process of Inspection
20.3 The Types of Object to be Inspected
20.4 Summary: The Main Categories of Inspection
20.5 Shape Deviations Relative to a Standard Template
20.6 Inspection of Circular Products
20.7 Inspection of Printed Circuits
20.8 Steel Strip and Wood Inspection
20.9 Inspection of Products with High Levels of Variability
20.10 X-Ray Inspection
20.11 The Importance of Color in Inspection
20.12 Bringing Inspection to the Factory
20.13 Concluding Remarks
20.14 Bibliographical and Historical Notes
Chapter 21: Inspection of Cereal Grains
21.1 Introduction
21.2 Case Study: Location of Dark Contaminants in Cereals
21.3 Case Study: Location of Insects
21.4 Case Study: High-Speed Grain Location
21.5 Optimizing the Output for Sets of Directional Template Masks
21.6 Concluding Remarks
21.7 Bibliographical and Historical Notes
Chapter 22: Surveillance
22.1 Introduction
22.2 Surveillance―The Basic Geometry
22.3 ForegroundCBackground Separation
22.4 Particle Filters
22.5 Use of Color Histograms for Tracking
22.6 Implementation of Particle Filters
22.7 Chamfer Matching, Tracking, and Occlusion
22.8 Combining Views from Multiple Cameras
22.9 Applications to the Monitoring of Traffic Flow
22.10 License Plate Location
22.11 Occlusion Classification for Tracking
22.12 Distinguishing Pedestrians by their Gait
22.13 Human Gait Analysis
22.14 Model-Based Tracking of Animals
22.15 Concluding Remarks
22.16 Bibliographical and Historical Notes
22.17 Problem
Chapter 23: In-Vehicle Vision Systems
23.1 Introduction
23.2 Locating the Roadway
23.3 Location of Road Markings
23.4 Location of Road Signs
23.5 Location of Vehicles
23.6 Information Obtained by Viewing Licence Plates and Other Structural Features
23.7 Locating Pedestrians
23.8 Guidance and Egomotion
23.9 Vehicle Guidance in Agriculture
23.10 Concluding Remarks
23.11 More Detailed Developments and Bibliographies Relating to Advanced Driver Assistance Systems
23.12 Problem
Chapter 24: Statistical Pattern Recognition
24.1 Introduction
24.2 The Nearest Neighbor Algorithm
24.3 Bayes’ Decision Theory
24.4 Relation of the Nearest Neighbor and Bayes’ Approaches
24.5 The Optimum Number of Features
24.6 Cost Functions and ErrorCReject Tradeoff
24.7 The Receiver Operating Characteristic
24.8 Multiple Classifiers
24.9 Cluster Analysis
24.10 Principal Components Analysis
24.11 The Relevance of Probability in Image Analysis
24.12 Another Look at Statistical Pattern Recognition: The Support Vector Machine
24.13 Artificial Neural Networks
24.14 The Back-Propagation Algorithm
24.15 MLP Architectures
24.16 Overfitting to the Training Data
24.17 Concluding Remarks
24.18 Bibliographical and Historical Notes
24.19 Problems
Chapter 25: Image Acquisition
25.1 Introduction
25.2 Illumination Schemes
25.3 Cameras and Digitization
25.4 The Sampling Theorem
25.5 Hyperspectral Imaging
25.6 Concluding Remarks
25.7 Bibliographical and Historical Notes
Chapter 26: Real-Time Hardware and Systems Design Considerations
26.1 Introduction
26.2 Parallel Processing
26.3 SIMD Systems
26.4 The Gain in Speed Attainable with N Processors
26.5 Flynn’s Classification
26.6 Optimal Implementation of Image Analysis Algorithms
26.7 Some Useful Real-Time Hardware Options
26.8 Systems Design Considerations
26.9 Design of Inspection Systems―the Status Quo
26.10 System Optimization
26.11 Concluding Remarks
26.12 Bibliographical and Historical Notes7
Chapter 27: Epilogue―Perspectives in Vision
27.1 Introduction
27.2 Parameters of Importance in Machine Vision
27.3 Tradeoffs
27.4 Moore’s Law in Action
27.5 Hardware, Algorithms, and Processes
27.6 The Importance of Choice of Representation
27.7 Past, Present, and Future
27.8 Bibliographical and Historical Notes
APPENDIX A. Robust Statistics
A.1 Introduction
A.2 Preliminary Definitions and Analysis
A.3 The M-Estimator (Influence Function) Approach
A.4 The Least Median of Squares Approach to Regression
A.5 Overview of the Robustness Problem
A.6 The RANSAC Approach
A.7 Concluding Remarks
A.8 Bibliographical and Historical Notes
A.9 Problem
Author Index
Subject Index
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