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해외주문 [Book] Machine Vision Theory, Algorithms, Practicalities

0003/E 3 | Paperback
Davies, E. , Davies, E. R. 지음 | Morgan Kaufmann Publishers | 2004년 12월 01일

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상품상세정보
ISBN 9780122060939(0122060938)
쪽수 934쪽
언어 English
크기 193(W) X 244(H) X 58(T) (mm)
0003/E 3
삽화유무 삽화있음
총권수 1권
시리즈명 Signal Processing and Its Applications
Textual Format Computer Applications
리딩지수 Level Scholarly/Graduate

책소개

이 책이 속한 분야

In the last 40 years, machine vision has evolved into a mature field embracing a wide range of applications including surveillance, automated inspection, robot assembly, vehicle guidance, traffic monitoring and control, signature verification, biometric measurement, and analysis of remotely sensed images. While researchers and industry specialists continue to document their work in this area, it has become increasingly difficult for professionals and graduate students to understand the essential theory and practicalities well enough to design their own algorithms and systems. This book directly addresses this need. As in earlier editions, E.R. Davies clearly and systematically presents the basic concepts of the field in highly accessible prose and images, covering essential elements of the theory while emphasizing algorithmic and practical design constraints. In this thoroughly updated edition, he divides the material into horizontal levels of a complete machine vision system. Application case studies demonstrate specific techniques and illustrate key constraints for designing real-world machine vision systems.
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목차


Foreword xxiii
Preface xxv
Acknowledgments xxix
Vision, the Challenge
Introduction---The Senses 1(1)
The Nature of Vision 2(9)
The Process of Recognition 2(2)
Tackling the Recognition Problem 4(3)
Object Location 7(2)
Scene Analysis 9(1)
Vision as Inverse Graphics 10(1)
From Automated Visual Inspection to Surveillance 11(1)
What This Book Is About 12(2)
The Following Chapters 14(1)
Bibliographical Notes 15(2)
PART 1 LOW-LEVEL VISION 17(246)
Images and Imaging Operations
Introduction 19(5)
Gray-scale versus Color 21(3)
Image Processing Operations 24(15)
Some Basic Operations on Gray-scale Images 25(7)
Basic Operations on Binary Images 32(5)
Noise Suppression by Image Accumulation 37(2)
Convolutions and Point Spread Functions 39(2)
Sequential versus Parallel Operations 41(2)
Concluding Remarks 43(1)
Bibliographical and Historical Notes 44(1)
Problems 44(3)
Basic Image Filtering Operations
Introduction 47(2)
Noise Suppression by Gaussian Smoothing 49(2)
Median Filters 51(3)
Mode Filters 54(7)
Rank Order Filters 61(1)
Reducing Computational Load 61(4)
A Bit-based Method for Fast Median Filtering 64(1)
Sharp--Unsharp Masking 65(1)
Shifts Introduced by Median Filters 66(12)
Continuum Model of Median Shifts 68(4)
Generalization to Gray-scale Images 72(3)
Shifts Arising with Hybrid Median Filters 75(1)
Problems with Statistics 76(2)
Discrete Model of Median Shifts 78(6)
Generalization to Gray-scale Images 82(2)
Shifts Introduced by Mode Filters 84(2)
Shifts Introduced by Mean and Gaussian Filters 86(1)
Shifts Introduced by Rank Order Filters 86(8)
Shifts in Rectangular Neighborhoods 87(4)
Case of High Curvature 91(1)
Test of the Model in a Discrete Case 91(3)
The Role of Filters in Industrial Applications of Vision 94(1)
Color in Image Filtering 94(2)
Concluding Remarks 96(1)
Bibliographical and Historical Notes 96(2)
Problems 98(5)
Thresholding Techniques
Introduction 103(1)
Region-growing Methods 104(1)
Thresholding 105(9)
Finding a Suitable Threshold 105(2)
Tackling the Problem of Bias in Threshold Selection 107(4)
A Convenient Mathematical Model 111(3)
Summary 114(1)
Adaptive Thresholding 114(8)
The Chow and Kaneko Approach 118(1)
Local Thresholding Methods 119(3)
More Thoroughgoing Approaches to Threshold Selection 122(4)
Variance-based Thresholding 122(1)
Entropy-based Thresholding 123(2)
Maximum Likelihood Thresholding 125(1)
Concluding Remarks 126(1)
Bibliographical and Historical Notes 127(2)
Problems 129(2)
Edge Detection
Introduction 131(1)
Basic Theory of Edge Detection 132(1)
The Template Matching Approach 133(2)
Theory of 3 x 3 Template Operators 135(5)
Summary---Design Constraints and Conclusions 140(1)
The Design of Differential Gradient Operators 141(2)
The Concept of a Circular Operator 143(1)
Detailed Implementation of Circular Operators 144(2)
Structured Bands of Pixels in Neighborhoods of Various Sizes 146(4)
The Systematic Design of Differential Edge Operators 150(1)
Problems with the above Approach---Some Alternative Schemes 151(4)
Concluding Remarks 155(1)
Bibliographical and Historical Notes 156(1)
Problems 157(2)
Binary Shape Analysis
Introduction 159(1)
Connectedness in Binary Images 160(1)
Object Labeling and Counting 161(7)
Solving the Labeling Problem in a More Complex Case 164(4)
Metric Properties in Digital Images 168(1)
Size Filtering 169(2)
The Convex Hull and Its Computation 171(6)
Distance Functions and Their Uses 177(4)
Skeletons and Thinning 181(12)
Crossing Number 183(3)
Parallel and Sequential Implementations of Thinning 186(3)
Guided Thinning 189(1)
A Comment on the Nature of the Skeleton 189(2)
Skeleton Node Analysis 191(1)
Application of Skeletons for Shape Recognition 192(1)
Some Simple Measures for Shape Recognition 193(1)
Shape Description by Moments 194(1)
Boundary Tracking Procedures 195(1)
More Detail on the Sigma and Chi Functions 196(1)
Concluding Remarks 197(2)
Bibliographical and Historical Notes 199(1)
Problems 200(7)
Boundary Pattern Analysis
Introduction 207(5)
Hysteresis Thresholding 209(3)
Boundary Tracking Procedures 212(1)
Template Matching---A Reminder 212(1)
Centroidal Profiles 213(1)
Problems with the Centroidal Profile Approach 214(4)
Some Solutions 216(2)
The (s, ψ) Plot 218(2)
Tackling the Problems of Occlusion 220(3)
Chain Code 223(1)
The (r, s) Plot 224(1)
Accuracy of Boundary Length Measures 225(2)
Concluding Remarks 227(1)
Bibliographical and Historical Notes 228(1)
Problems 229(4)
Mathematical Morphology
Introduction 233(1)
Dilation and Erosion in Binary Images 234(1)
Dilation and Erosion 234(1)
Cancellation Effects 234(1)
Modified Dilation and Erosion Operators 235(1)
Mathematical Morphology 235(14)
Generalized Morphological Dilation 235(2)
Generalized Morphological Erosion 237(1)
Duality between Dilation and Erosion 238(1)
Properties of Dilation and Erosion Operators 239(3)
Closing and Opening 242(3)
Summary of Basic Morphological Operations 245(3)
Hit-and-Miss Transform 248(1)
Template Matching 249(1)
Connectivity-based Analysis of Images 249(2)
Skeletons and Thinning 250(1)
Gray-scale Processing 251(4)
Morphological Edge Enhancement 252(1)
Further Remarks on the Generalization to Gray-scale Processing 252(3)
Effect of Noise on Morphological Grouping Operations 255(4)
Detailed Analysis 257(2)
Discussion 259(1)
Concluding Remarks 259(1)
Bibliographical and Historical Notes 260(1)
Problem 261(2)
PART 2 INTERMEDIATE-LEVEL VISION 263(180)
Line Detection
Introduction 265(1)
Application of the Hough Transform to Line Detection 265(4)
The Foot-of-Normal Method 269(7)
Error Analysis 272(2)
Quality of the Resulting Data 274(2)
Application of the Foot-of-Normal Method 276(1)
Longitudinal Line Localization 276(1)
Final Line Fitting 277(1)
Concluding Remarks 277(1)
Bibliographical and Historical Notes 278(2)
Problems 280(3)
Circle Detection
Introduction 283(1)
Hough-based Schemes for Circular Object Detection 284(4)
The Problem of Unknown Circle Radius 288(7)
Experimental Results 290(5)
The Problem of Accurate Center Location 295(7)
Obtaining a Method for Reducing Computational Load 296(3)
Improvements on the Basic Scheme 299(1)
Discussion 300(1)
Practical Details 300(2)
Overcoming the Speed Problem 302(8)
More Detailed Estimates of Speed 303(2)
Robustness 305(1)
Experimental Results 306(1)
Summary 307(3)
Concluding Remarks 310(1)
Bibliographical and Historical Notes 311(1)
Problems 312(3)
The Hough Transform and Its Nature
Introduction 315(1)
The Generalized Hough Transform 315(2)
Setting Up the Generalized Hough Transform---Some Relevant Questions 317(1)
Spatial Matched Filtering in Images 318(1)
From Spatial Matched Filters to Generalized Hough Transforms 319(1)
Gradient Weighting versus Uniform Weighting 320(4)
Calculation of Sensitivity and Computational Load 323(1)
Summary 324(1)
Applying the Generalized Hough Transform to Line Detection 325(2)
The Effects of Occlusions for Objects with Straight Edges 327(2)
Fast Implementations of the Hough Transform 329(3)
The Approach of Gerig and Klein 332(1)
Concluding Remarks 333(1)
Bibliographical and Historical Notes 334(3)
Problem 337(2)
Ellipse Detection
Introduction 339(1)
The Diameter Bisection Method 339(2)
The Chord--Tangent Method 341(2)
Finding the Remaining Ellipse Parameters 343(2)
Reducing Computational Load for the Generalized Hough Transform Method 345(8)
Practical Details 349(4)
Comparing the Various Methods 353(2)
Concluding Remarks 355(2)
Bibliographical and Historical Notes 357(1)
Problems 358(3)
Hole Detection
Introduction 361(1)
The Template Matching Approach 361(2)
The Lateral Histogram Technique 363(1)
The Removal of Ambiguities in the Lateral Histogram Technique 363(5)
Computational Implications of the Need to Check for Ambiguities 364(2)
Further Detail of the Subimage Method 366(2)
Application of the Lateral Histogram Technique for Object Location 368(4)
Limitations of the Approach 370(2)
Appraisal of the Hole Detection Problem 372(2)
Concluding Remarks 374(1)
Bibliographical and Historical Notes 375(1)
Problems 376(3)
Polygon and Corner Detection
Introduction 379(1)
The Generalized Hough Transform 380(1)
Straight Edge Detection 380(1)
Application to Polygon Detection 381(6)
The Case of an Arbitrary Triangle 382(1)
The Case of an Arbitrary Rectangle 383(2)
Lower Bounds on the Numbers of Parameter Planes 385(2)
Determining Polygon Orientation 387(2)
Why Corner Detection? 389(1)
Template Matching 390(1)
Second-order Derivative Schemes 391(2)
A Median-Filter-Based Corner Detector 393(6)
Analyzing the Operation of the Median Detector 394(2)
Practical Results 396(3)
The Hough Transform Approach to Corner Detection 399(3)
The Plessey Corner Detector 402(2)
Corner Orientation 404(2)
Concluding Remarks 406(1)
Bibliographical and Historical Notes 407(3)
Problems 410(3)
Abstract Pattern Matching Techniques
Introduction 413(1)
A Graph-theoretic Approach to Object Location 414(8)
A Practical Example---Locating Cream Biscuits 419(3)
Possibilities for Saving Computation 422(2)
Using the Generalized Hough Transform for Feature Collation 424(3)
Computational Load 426(1)
Generalizing the Maximal Clique and Other Approaches 427(1)
Relational Descriptors 428(4)
Search 432(1)
Concluding Remarks 433(1)
Bibliographical and Historical Notes 434(3)
Problems 437(6)
PART 3 3-D VISION AND MOTION 443(182)
The Three-dimensional World
Introduction 445(1)
Three-Dimensional Vision---The Variety of Methods 446(2)
Projection Schemes for Three-dimensional Vision 448(6)
Binocular Images 450(2)
The Correspondence Problem 452(2)
Shape from Shading 454(5)
Photometric Stereo 459(3)
The Assumption of Surface Smoothness 462(2)
Shape from Texture 464(1)
Use of Structured Lighting 464(2)
Three-Dimensional Object Recognition Schemes 466(2)
The Method of Ballard and Sabbah 468(2)
The Method of Silberberg et al. 470(2)
Horaud's Junction Orientation Technique 472(4)
An Important Paradigm---Location of Industrial Parts 476(2)
Concluding Remarks 478(2)
Bibliographical and Historical Notes 480(2)
Problems 482(5)
Tackling the Perspective n-Point Problem
Introduction 487(1)
The Phenomenon of Perspective Inversion 487(2)
Ambiguity of Pose under Weak Perspective Projection 489(4)
Obtaining Unique Solutions to the Pose Problem 493(5)
Solution of the 3-Point Problem 497(1)
Using Symmetrical Trapezia for Estimating Pose 498(1)
Concluding Remarks 498(3)
Bibliographical and Historical Notes 501(1)
Problems 502(3)
Motion
Introduction 505(1)
Optical Flow 505(4)
Interpretation of Optical Flow Fields 509(2)
Using Focus of Expansion to Avoid Collision 511(2)
Time-to-Adjacency Analysis 513(2)
Basic Difficulties with the Optical Flow Model 515(1)
Stereo from Motion 516(2)
Applications to the Monitoring of Traffic Flow 518(6)
The System of Bascle et al. 518(2)
The System of Koller et al. 520(4)
People Tracking 524(6)
Some Basic Techniques 526(2)
Within-vehicle Pedestrian Tracking 528(2)
Human Gait Analysis 530(3)
Model-based Tracking of Animals---A Case Study 533(3)
Snakes 536(2)
The Kalman Filter 538(2)
Concluding Remarks 540(2)
Bibliographical and Historical Notes 542(1)
Problem 543(2)
Invariants and Their Applications
Introduction 545(2)
Cross Ratios: The ``Ratio of Ratios'' Concept 547(5)
Invariants for Noncollinear Points 552(4)
Further Remarks about the 5-Point Configuration 554(2)
Invariants for Points on Conics 556(4)
Differential and Semidifferential Invariants 560(2)
Symmetrical Cross Ratio Functions 562(2)
Concluding Remarks 564(2)
Bibliographical and Historical Notes 566(1)
Problems 567(4)
Egomotion and Related Tasks
Introduction 571(1)
Autonomous Mobile Robots 572(1)
Active Vision 573(1)
Vanishing Point Detection 574(2)
Navigation for Autonomous Mobile Robots 576(3)
Constructing the Plan View of Ground Plane 579(2)
Further Factors Involved in Mobile Robot Navigation 581(2)
More on Vanishing Points 583(2)
Centers of Circles and Ellipses 585(3)
Vehicle Guidance in Agriculture---A Case Study 588(4)
3-D Aspects of the Task 590(1)
Real-time Implementation 591(1)
Concluding Remarks 592(1)
Bibliographical and Historical Notes 592(1)
Problems 593(2)
Image Transformations and Camera Calibration
Introduction 595(1)
Image Transformations 596(5)
Camera Calibration 601(3)
Intrinsic and Extrinsic Parameters 604(3)
Correcting for Radial Distortions 607(2)
Multiple-view Vision 609(1)
Generalized Epipolar Geometry 610(1)
The Essential Matrix 611(2)
The Fundamental Matrix 613(1)
Properties of the Essential and Fundamental Matrices 614(1)
Estimating the Fundamental Matrix 615(1)
Image Rectification 616(1)
3-D Reconstruction 617(2)
An Update on the 8-Point Algorithm 619(2)
Concluding Remarks 621(1)
Bibliographical and Historical Notes 622(1)
Problems 623(2)
PART 4 TOWARD REAL-TIME PATTERN RECOGNITION SYSTEMS 625(206)
Automated Visual Inspection
Introduction 627(1)
The Process of Inspection 628(1)
Review of the Types of Objects to Be Inspected 629(3)
Food Products 629(1)
Precision Components 630(1)
Differing Requirements for Size Measurement 630(1)
Three-dimensional Objects 631(1)
Other Products and Materials for Inspection 632(1)
Summary---The Main Categories of Inspection 632(2)
Shape Deviations Relative to a Standard Template 634(1)
Inspection of Circular Products 635(7)
Computation of the Radial Histogram: Statistical Problems 636(5)
Application of Radial Histograms 641(1)
Inspection of Printed Circuits 642(1)
Steel Strip and Wood Inspection 643(1)
Inspection of Products with High Levels of Variability 644(4)
X-ray Inspection 648(3)
The Importance of Color in Inspection 651(2)
Bringing Inspection to the Factory 653(1)
Concluding Remarks 654(2)
Bibliographical and Historical Notes 656(3)
Inspection of Cereal Grains
Introduction 659(1)
Case Study 1: Location of Dark Contaminants in Cereals 660(5)
Application of Morphological and Nonlinear Filters to Locate Rodent Droppings 663(1)
Appraisal of the Various Schemas 664(1)
Problems with Closing 665(1)
Case Study 2: Location of Insects 665(8)
The Vectorical Strategy for Linear Feature Detection 666(3)
Designing Linear Feature Detection Masks for Larger Windows 669(1)
Application to Cereal Inspection 670(1)
Experimental Results 671(2)
Case Study 3: High-speed Grain Location 673(7)
Extending an Earlier Sampling Approach 673(2)
Application to Grain Inspection 675(4)
Summary 679(1)
Optimizing the Output for Sets of Directional Template Masks 680(3)
Application of the Formulas 682(1)
Discussion 683(1)
Concluding Remarks 683(1)
Bibliographical and Historical Notes 684(3)
Statistical Pattern Recognition
Introduction 687(1)
The Nearest Neighbor Algorithm 688(3)
Bayes' Decision Theory 691(2)
Relation of the Nearest Neighbor and Bayes' Approaches 693(3)
Mathematical Statement of the Problem 693(3)
The Importance of the Nearest Neighbor Classifier 696(1)
The Optimum Number of Features 696(1)
Cost Functions and Error--Reject Tradeoff 697(2)
The Receiver--Operator Characteristic 699(3)
Multiple Classifiers 702(3)
Cluster Analysis 705(5)
Supervised and Unsupervised Learning 705(1)
Clustering Procedures 706(4)
Principal Components Analysis 710(3)
The Relevance of Probability in Image Analysis 713(2)
The Route to Face Recognition 715(4)
The Face as Part of a 3-D Object 716(3)
Another Look at Statistical Pattern Recognition: The Support Vector Machine 719(1)
Concluding Remarks 720(2)
Bibliographical and Historical Notes 722(1)
Problems 723(2)
Biologically Inspired Recognition Schemes
Introduction 725(1)
Artificial Neural Networks 726(5)
The Backpropagation Algorithm 731(4)
MLP Architectures 735(1)
Overfitting to the Training Data 736(3)
Optimizing the Network Architecture 739(1)
Hebbian Learning 740(5)
Case Study: Noise Suppression Using ANNs 745(5)
Genetic Algorithms 750(2)
Concluding Remarks 752(1)
Bibliographical and Historical Notes 753(4)
Texture
Introduction 757(6)
Some Basic Approaches to Texture Analysis 763(1)
Gray-level Co-occurrence Matrices 764(4)
Laws' Texture Energy Approach 768(3)
Ade's Eigenfilter Approach 771(1)
Appraisal of the Laws and Ade Approaches 772(2)
Fractal-based Measures of Texture 774(1)
Shape from Texture 775(1)
Markov Random Field Models of Texture 776(1)
Structural Approaches to Texture Analysis 777(1)
Concluding Remarks 777(1)
Bibliographical and Historical Notes 778(3)
Image Acquisition
Introduction 781(1)
Illumination Schemes 782(14)
Eliminating Shadows 784(3)
Principles for Producing Regions of Uniform Illumination 787(3)
Case of Two Infinite Parallel Strip Lights 790(3)
Overview of the Uniform Illumination Scenario 793(1)
Use of Line-scan Cameras 794(2)
Cameras and Digitization 796(2)
Digitization 798(1)
The Sampling Theorem 798(4)
Concluding Remarks 802(1)
Bibliographical and Historical Notes 803(2)
Real-time Hardware and Systems Design Considerations
Introduction 805(1)
Parallel Processing 806(1)
SIMD Systems 807(2)
The Gain in Speed Attainable with N Processors 809(1)
Flynn's Classification 810(3)
Optimal Implementation of an Image Analysis Algorithm 813(3)
Hardware Specification and Design 813(1)
Basic Ideas on Optimal Hardware Implementation 814(2)
Some Useful Real-time Hardware Options 816(2)
Systems Design Considerations 818(1)
Design of Inspection Systems---The Status Quo 818(4)
System Optimization 822(2)
The Value of Case Studies 824(1)
Concluding Remarks 825(2)
Bibliographical and Historical Notes 827(4)
General Background 827(2)
Recent Highly Relevant Work 829(2)
PART 5 PERSPECTIVES ON VISION 831(36)
Machine Vision: Art or Science?
Introduction 833(1)
Parameters of Importance in Machine Vision 834(2)
Tradeoffs 836(3)
Some Important Tradeoffs 837(1)
Tradeoffs for Two-stage Template Matching 838(1)
Future Directions 839(1)
Hardware, Algorithms, and Processes 840(1)
A Retrospective View 841(1)
Just a Glimpse of Vision? 842(1)
Bibliographical and Historical Notes 843(2)
APPENDIX Robust Statistics
A.1 Introduction 845(3)
A.2 Preliminary Definitions and Analysis 848(2)
A.3 The M-estimator (Influence Function) Approach 850(6)
A.4 The Least Median of Squares Approach to Regression 856(4)
A.5 Overview of the Robustness Problem 860(1)
A.6 The RANSAC Approach 861(2)
A.7 Concluding Remarks 863(1)
A.8 Bibliographical and Historical Notes 864(1)
A.9 Problem 865(2)
List of Acronyms and Abbreviations 867(2)
References 869(48)
Author Index 917(8)
Subject Index 925

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  •   나는 machine vision과는 전혀 관련이 있는 학과가 아니다. 방산업체에서 프로젝트를 수행하다 보니 피치않게 꼭 해야만 하는 상황에 봉착하는 바람에 알게 된 것이 바로 이 분야인데… 참 아쉬웠던 것은 이 분야의 책들을 보면 한글로 된 책이나 번역된 계열의 책들은 그다지 마음에 드는 것들이 없다는 사실이었다. 크게 참고할만한 책도 없어서 어찌나 마음이 상하던지… 처음 이 분야를 시작했을 무렵에는 꽤나 스트레스에 시달렸던 것이 사실이다. 책을 보기보다 차라리 네이버에서 이 분야에 대해서 잘 알고 있는 분들이 올려놓... 더보기

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