基本信息出版社：人民邮电出版社
页码：934 页
出版日期：2009年02月
ISBN：7115195498/9787115195494
条形码：9787115195494
版本：第3版
装帧：平装
开本：16
正文语种：中文
丛书名：图灵原版计算机科学系列
外文书名：Machine Vision Thcory，Algorithms，Practicalities Third Edition

内容简介 《机器视觉理论、算法与实践(英文版第3版)》是机器视觉课程的理想教材，作者清晰、系统地阐述了机器视觉的基本概念，介绍理论的基本元素的同时强调算法和实用设计的约束。书中阐述各个主题时，既阐述了基本算法，又介绍了数学工具。此外，《机器视觉理论、算法与实践(英文版第3版)》还使用案例演示具体技术的应用，并阐明设计现实机器视觉系统的关键约束。
《机器视觉理论、算法与实践(英文版第3版)》适合作为高等院校计算机及电子工程相关专业研究生的教材，更是从事机器视觉、计算机视觉和机器人领域研究的人员不可多得的技术参考书。
作者简介 E.R.Davies，著名机器视觉专家。英国物理学会会士、IEE会士、英国机器视觉协会的执行委员。毕业于牛津大学，现任伦敦大学皇家霍洛威学院机器视觉教授。在机器视觉、图像分析、自动视觉检测、噪声抑制技术等方面有丰富的教学和科研经验。
媒体推荐 “本书将图像处理的理论与应用实践完美地结合起来，是机器视觉领域研究人员的必读之作。”
　　——John Billingsley，南昆士兰大学
“前两版已经奠定了本书在机器视觉领域中独一无二的地位，它是对重要的图像处理和计算机视觉算法进行详细分析的知识宝库！这一版在此基础之上增加了最新进展，是一部全面而且与时俱进的权威著作。”
　　——Farzin Deravi，肯特大学
编辑推荐 40年来，机器视觉在各行各业得到了广泛的应用，包括自动检测、机器人组装、行车导引、流量监控、签名验证、生物测量、遥感图像分析等。但是另一方面，面对大量新的研究成果，要充分理解相关的理论和应用，进行算法和系统的设计，却越来越困难。
《机器视觉理论、算法与实践(英文版第3版)》能够满足广大读者学习和掌握机器视觉知识的需求。全书图文并茂，清晰、系统地阐述了基本概念，提供了丰富的应用案例和代码，强调了算法和实用设计的各种约束条件。新版做了全面的更新，反映了最新进展，内容更加全面。《机器视觉理论、算法与实践(英文版第3版)》是机器视觉课程的理想教材，已经成为国内外很多名校的指定教学参考书。同时，《机器视觉理论、算法与实践(英文版第3版)》也是工程技术人员不可或缺的权威参考书。
目录
CHAPTER 1　Vision, the Challenge
1.1　Introduction-The Senses　1
1.2　The Nature of Vision　2
1.2.1　The Process of Recognition　2
1.2.2　Tackling the Recognition Problem　4
1.2.3　Object Location　7
1.2.4　Scene Analysis　9
1.2.5　Vision as Inverse Graphics　10
1.3　From Automated Visual Inspection to Surveillance　11
1.4　What This Book Is About　12
1.5　The Following Chapters　14
1.6　Bibliographical Notes　15

PART 1　LOW-LEVELVISION　17
CHAPTER 2　Images and Imaging Operations
2.1　Introduction　19
2.1.1　Gray-scale versus Color　21*
2.2　Image Processing Operations　24
2.2.1　Some Basic Operations on Gray-scale Images　25
2.2.2　Basic Operations on Binary Images　32
2.2.3　Noise Suppression by Image Accumulation　37
2.3　Convolutions and Point Spread Functions　39
2.4　Sequential versus Parallel Operations　41
2.5　Concluding Remarks　43
2.6　Bibliographical and Historical Notes　44
2.7　Problems　44

CHAPTER 3　Basic Image Filtering Operations
3.1　Introduction　47
3.2　Noise Suppression by Gaussian Smoothing　49
3.3　Median Filters　51
3.4　Mode Filters　54
3.5　Rank Order Filters　61
3.6　Reducing Computational Load　61
3.6.1　A Bit-based Method for Fast Median Filtering　64
3.7　Sharp-Unsharp Masking　65
3.8　Shifts Introduced by Median Filters　66
3.8.1　Continuum Model of Median Shifts　68
3.8.2　Generalization to Gray-scale Images　72
3.8.3　Shifts Arising with Hybrid Median Filters　75
3.8.4　Problems with Statistics　76
3.9　Discrete Model of Median Shifts　78
3.9.1　Generalization to Gray-scale Images　82
3.10　Shifts Introduced by Mode Filters　84
3.11　Shifts Introduced by Mean and Gaussian Filters　86
3.12　Shifts Introduced by Rank Order Filters　86
3.12.1　Shifts in Rectangular Neighborhoods　87
3.12.2　Case of High Curvature　91
3.12.3　Test of the Model in a Discrete Case　91
3.13　The Role of Filters in Industrial Applications of Vision　94
3.14　Color in Image Filtering　94
3.15　Concluding Remarks　96
3.16　Bibliographical and Historical Notes　96
3.17　Problems　98

CHAPTER 4　Thresholding Techniques
4.1　Introduction　103
4.2　Region-growing Methods　104
4.3　Thresholding　105
4.3.1　Finding a Suitable Threshold　105
4.3.2　Tackling the Problem of Bias in Threshold Selection　107
4.3.3　A Convenient Mathematical Model　111
4.3.4　Summary　114
4.4　Adaptive Thresholding　114
4.4.1　The Chow and Kaneko Approach　118
4.4.2　Local Thresholding Methods　119
4.5　More Thoroughgoing Approaches to Threshold Selection　122
4.5.1　Variance-based Thresholding　122
4.5.2　Entropy-based Thresholding　123
4.5.3　Maximum Likelihood Thresholding　125
4.6　Concluding Remarks　126
4.7　Bibliographical and Historical Notes　127
4.8　Problems　129

CHAPTER 5　Edge Detection
5.1　Introduction　131
5.2　Basic Theory of Edge Detection　132
5.3　The Template Matching Approach　133
5.4　Theory of 3×3 Template Operators　135
5.5　Summary-Design Constraints and Conclusions　140
5.6　The Design of Differential Gradient Operators　141
5.7　The Concept of a Circular Operator　143
5.8　Detailed Implementation of Circular Operators　144
5.9　Structured Bands of Pixels in Neighborhoods of Various Sizes　146
5.10　The Systematic Design of Differential Edge Operators　150
5.11　Problems with the above Approach-Some Alternative Schemes　151
5.12　Concluding Remarks　155
5.13　Bibliographical and Historical Notes　156
5.14　Problems　157

CHAPTER 6　Binary Shape Analysis
6.1　Introduction　159
6.2　Connectedness in Binary Images　160
6.3　Object Labeling and Counting　161
6.3.1　Solving the Labeling Problem in a More Complex Case　164
6.4　Metric Properties in Digital Images　168
6.5　Size Filtering　169
6.6　The Convex Hull and Its Computation　171
6.7　Distance Functions and Their Uses　177
6.8　Skeletons and Thinning　181
6.8.1　Crossing Number　183
6.8.2　Parallel and Sequential Implementations of Thinning　186
6.8.3　Guided Thinning　189
6.8.4　A Comment on the Nature of the Skeleton　189
6.8.5　Skeleton Node Analysis　191
6.8.6　Application of Skeletons for Shape Recognition　192
6.9　Some Simple Measures for Shape Recognition　193
6.10　Shape Description by Moments　194
6.11　Boundary Tracking Procedures　195
6.12　More Detail on the Sigma and Chi Functions　196
6.13　Concluding Remarks　197
6.14　Bibliographical and Historical Notes　199
6.15　Problems　200

CHAPTER 7　Boundary Pattern Analysis
7.1　Introduction　207
7.1.1　Hysteresis Thresholding　209
7.2　Boundary Tracking Procedures　212
7.3　Template Matching-A Reminder　212
7.4　Centroidal Profiles　213
7.5　Problems with the Centroidal Profile Approach　214
7.5.1　Some Solutions　216
7.6　The (s,ψ) Plot　218
7.7　Tackling the Problems of Occlusion　220
7.8　Chain Code　223
7.9　The (r, s) Plot　224
7.10　Accuracy of Boundary Length Measures　225
7.11　Concluding Remarks　227
7.12　Bibliographical and Historical Notes　228
7.13　Problems　229

CHAPTER 8　Mathematical Morphology
8.1　Introduction　233
8.2　Dilation and Erosion in Binary Images　234
8.2.1　Dilation and Erosion　234
8.2.2　Cancellation Effects　234
8.2.3　Modified Dilation and Erosion Operators　235
8.3　Mathematical Morphology　235
8.3.1　Generalized Morphological Dilation　235
8.3.2　Generalized Morphological Erosion　237
8.3.3　Duality between Dilation and Erosion　238
8.3.4　Properties of Dilation and Erosion Operators　239
8.3.5　Closing and Opening　242
8.3.6　Summary of Basic Morphological Operations　245
8.3.7　Hit-and-Miss Transform　248
8.3.8　Template Matching　249
8.4　Connectivity-based Analysis of Images　249
8.4.1　Skeletons and Thinning　250
8.5　Gray-scale Processing　251
8.5.1　Morphological Edge Enhancement　252
8.5.2　Further Remarks on the Generalization to Gray-scale Processing　252
8.6　Effect of Noise on Morphological Grouping Operations　255
8.6.1　Detailed Analysis　257
8.6.2　Discussion　259
8.7　Concluding Remarks　259
8.8　Bibliographical and Historical Notes　260
8.9　Problem　261

PART 2　INTERMEDIATE-LEVELVISION　263
CHAPTER 9　Line Detection
9.1　Introduction　265
9.2　Application of the Hough Transform to Line Detection　265
9.3　The Foot-of-Normal Method　269
9.3.1　Error Analysis　272
9.3.2　Quality of the Resulting Data　274
9.3.3　Application of the Foot-of-Normal Method　276
9.4　Longitudinal Line Localization　276
9.5　Final Line Fitting　277
9.6　Concluding Remarks　277
9.7　Bibliographical and Historical Notes　278
9.8　Problems　280

CHAPTER 10　Circle Detection
10.1　Introduction　283
10.2　Hough-based Schemes for Circular Object Detection　284
10.3　The Problem of Unknown Circle Radius　288
10.3.1　Experimental Results　290
10.4　The Problem of Accurate Center Location　295
10.4.1　Obtaining a Method for Reducing Computational Load　296
10.4.2　Improvements on the Basic Scheme　299
10.4.3　Discussion　300
10.4.4　Practical Details　300
10.5　Overcoming the Speed Problem　302
10.5.1　More Detailed Estimates of Speed　303
10.5.2　Robustness　305
10.5.3　Experimental Results　306
10.5.4　Summary　307
10.6　Concluding Remarks　310
10.7　Bibliographical and Historical Notes　311
10.8　Problems　312

CHAPTER 11　The Hough Transform and Its Nature
11.1　Introduction　315
11.2　The Generalized Hough Transform　315
11.3　Setting Up the Generalized Hough Transform-Some Relevant Questions　317
11.4　Spatial Matched Filtering in Images　318
11.5　From Spatial Matched Filters to Generalized Hough Transforms　319
11.6　Gradient Weighting versus Uniform Weighting　320
11.6.1　Calculation of Sensitivity and Computational Load　323
11.7　Summary　324
11.8　Applying the Generalized Hough Transform to Line Detection　325
11.9　The Effects of Occlusions for Objects with Straight Edges　327
11.10　Fast Implementations of the Hough Transform　329
11.11　The Approach of Gerig and Klein　332
11.12　Concluding Remarks　333
11.13　Bibliographical and Historical Notes　334
11.14　Problem　337

CHAPTER 12　Ellipse Detection
12.1　Introduction　339
12.2　The Diameter Bisection Method　339
12.3　The Chord-Tangent Method　341
12.4　Finding the Remaining Ellipse Parameters　343
12.5　Reducing Computational Load for the Generalized Hough Transform Method　345
12.5.1　Practical Details　349
12.6　Comparing the Various Methods　353
12.7　Concluding Remarks　355
12.8　Bibliographical and Historical Notes　357
12.9　Problems　358

CHAPTER 13　Hole Detection
13.1　Introduction　361
13.2　The Template Matching Approach　361
13.3　The Lateral Histogram Technique　363
13.4　The Removal of Ambiguities in the Lateral Histogram Technique　363
13.4.1　Computational Implications of the Need to Check for Ambiguities　364
13.4.2　Further Detail of the Subimage Method　366
13.5　Application of the Lateral Histogram Technique for Object Location　368
13.5.1　Limitations of the Approach　370
13.6　Appraisal of the Hole Detection Problem　372
13.7　Concluding Remarks　374
13.8　Bibliographical and Historical Notes　375
13.9　Problems　376

CHAPTER 14　Polygon and Corner Detection
14.1　Introduction　379
14.2　The Generalized Hough Transform　380
14.2.1　Straight Edge Detection　380
14.3　Application to Polygon Detection　381
14.3.1　The Case of an Arbitrary Triangle　382
14.3.2　The Case of an Arbitrary Rectangle　383
14.3.3　Lower Bounds on the Numbers of Parameter Planes　385
14.4　Determining Polygon Orientation　387
14.5　Why Corner Detection?　389
14.6　Template Matching　390
14.7　Second-order Derivative Schemes　391
14.8　A Median-Filter-Based Corner Detector　393
14.8.1　Analyzing the Operation of the Median Detector　394
14.8.2　Practical Results　396
14.9　The Hough Transform Approach to Corner Detection　399
14.10　The Plessey Corner Detector　402
14.11　Corner Orientation　404
14.12　Concluding Remarks　406
14.13　Bibliographical and Historical Notes　407
14.14　Problems　410

CHAPTER 15　Abstract Pattern Matching Techniques
15.1　Introduction　413
15.2　A Graph-theoretic Approach to Object Location　414
15.2.1　A Practical Example-Locating Cream Biscuits　419
15.3　Possibilities for Saving Computation　422
15.4　Using the Generalized Hough Transform for Feature Collation　424
15.4.1　Computational Load　426
15.5　Generalizing the Maximal Clique and Other Approaches　427
15.6　Relational Descriptors　428
15.7　Search　432
15.8　Concluding Remarks　433
15.9　Bibliographical and Historical Notes　434
15.10　Problems　437

PART 3　3-DVISION AND MOTION　443
CHAPTER 16　The Three-dimensional World
16.1　Introduction　445
16.2　Three-Dimensional Vision-The Variety of Methods　446
16.3　Projection Schemes for Three-dimensional Vision　448
16.3.1　Binocular Images　450
16.3.2　The Correspondence Problem　452
16.4　Shape from Shading　454
16.5　Photometric Stereo　459
16.6　The Assumption of Surface Smoothness　462
16.7　Shape from Texture　464
16.8　Use of Structured Lighting　464
16.9　Three-Dimensional Object Recognition Schemes　466
16.10　The Method of Ballard and Sabbah　468
16.11　The Method of Silberberg et al.　470
16.12　Horaud’s Junction Orientation Technique　472
16.13　An Important Paradigm-Location of Industrial Parts　476
16.14　Concluding Remarks　478
16.15　Bibliographical and Historical Notes　480
16.16　Problems　482

CHAPTER 17　Tackling the Perspective n-Point Problem
17.1　Introduction　487
17.2　The Phenomenon of Perspective Inversion　487
17.3　Ambiguity of Pose under Weak Perspective Projection　489
17.4　Obtaining Unique Solutions to the Pose Problem　493
17.4.1　Solution of the　3-Point Problem　497
17.4.2　Using Symmetrical Trapezia for Estimating Pose　498
17.5　Concluding Remarks　498
17.6　Bibliographical and Historical Notes　501
17.7　Problems　502

CHAPTER 18　Motion
18.1　Introduction　505
18.2　Optical Flow　505
18.3　Interpretation of Optical Flow Fields　509
18.4　Using Focus of Expansion to Avoid Collision　511
18.5　Time-to-Adjacency Analysis　513
18.6　Basic Difficulties with the Optical Flow Model　515
18.7　Stereo from Motion　516
18.8　Applications to the Monitoring of Traffic Flow　518
18.8.1　The System of Bascle et al.　518
18.8.2　The System of Koller et al.　520
18.9　People Tracking　524
18.9.1　Some Basic Techniques　526
18.9.2　Within-vehicle Pedestrian Tracking　528
18.10　Human Gait Analysis　530
18.11　Model-based Tracking of Animals-A Case Study　533
18.12　Snakes　536
18.13　The Kalman Filter　538
18.14　Concluding Remarks　540
18.15　Bibliographical and Historical Notes　542
18.16　Problem　543

CHAPTER 19　Invariants and Their Applications
19.1　Introduction　545
19.2　Cross Ratios: The “Ratio of Ratios” Concept　547
19.3　Invariants for Noncollinear Points　552
19.3.1　Further Remarks about the　5-Point Configuration　554
19.4　Invariants for Points on Conics　556
19.5　Differential and Semidifferential Invariants　560
19.6　Symmetrical Cross Ratio Functions　562
19.7　Concluding Remarks　564
19.8　Bibliographical and Historical Notes　566
19.9　Problems　567

CHAPTER 20　Egomotion and Related Tasks
20.1　Introduction　571
20.2　Autonomous Mobile Robots　572
20.3　Active Vision　573
20.4　Vanishing Point Detection　574
20.5　Navigation for Autonomous Mobile Robots　576
20.6　Constructing the Plan View of Ground Plane　579
20.7　Further Factors Involved in Mobile Robot Navigation　581
20.8　More on Vanishing Points　583
20.9　Centers of Circles and Ellipses　585
20.10　Vehicle Guidance in Agriculture-A Case Study　588
20.10.1　3-D Aspects of the Task　590
20.10.2　Real-time Implementation　591
20.11　Concluding Remarks　592
20.12　Bibliographical and Historical Notes　592
20.13　Problems　593

CHAPTER 21　Image Transformations and Camera Calibration
21.1　Introduction　595
21.2　Image Transformations　596
21.3　Camera Calibration　601
21.4　Intrinsic and Extrinsic Parameters　604
21.5　Correcting for Radial Distortions　607
21.6　Multiple-view Vision　609
21.7　Generalized Epipolar Geometry　610
21.8　The Essential Matrix　611
21.9　The Fundamental Matrix　613
21.10　Properties of the Essential and Fundamental Matrices　614
21.11　Estimating the Fundamental Matrix　615
21.12　Image Rectification　616
21.13　3-D Reconstruction　617
21.14　An update on the　8-Point Algorithm　619
21.15　Concluding Remarks　621
21.16　Bibliographical and Historical Notes　622
21.17　Problems　623

PART 4　TOWARD REAL-TIME PATTERN RECOGNITIONS YSTEMS　625
CHAPTER 22　Automated Visual Inspection
22.1　Introduction　627
22.2　The Process of Inspection　628
22.3　Review of the Types of Objects to Be Inspected　629
22.3.1　Food Products　629
22.3.2　Precision Components　630
22.3.3　Differing Requirements for Size Measurement　630
22.3.4　Three-dimensional Objects　631
22.3.5　Other Products and Materials for Inspection　632
22.4　Summary-The Main Categories of Inspection　632
22.5　Shape Deviations Relative to a Standard Template　634
22.6　Inspection of Circular Products　635
22.6.1　Computation of the Radial Histogram: Statistical Problems　636
22.6.2　Application of Radial Histograms　641
22.7　Inspection of Printed Circuits　642
22.8　Steel Strip and Wood Inspection　643
22.9　Inspection of Products with High Levels of Variability　644
22.10　X-ray Inspection　648
22.11　The Importance of Color in Inspection　651
22.12　Bringing Inspection to the Factory　653
22.13　Concluding Remarks　654
22.14　Bibliographical and Historical Notes　656

CHAPTER 23　Inspection of Cereal Grains
23.1　Introduction　659
23.2　Case Study 1: Location of Dark Contaminants in Cereals　660
23.2.1　Application of Morphological and Nonlinear Filters to Locate Rodent Droppings　　663
23.2.2　Appraisal of the Various Schemas　664
23.2.3　Problems with Closing　665
23.3　Case Study 2: Location of Insects　665
23.3.1　The Vectorial Strategy for Linear Feature Detection　666
23.3.2　Designing Linear Feature Detection Masks for Larger Windows　669
23.3.3　Application to Cereal Inspection　670
23.3.4　Experimental Results　671
23.4　Case Study 3: High-speed Grain Location　673
23.4.1　Extending an Earlier Sampling Approach　673
23.4.2　Application to Grain Inspection　675
23.4.3　Summary　679
23.5　Optimizing the Output for Sets of Directional Template Masks　680
23.5.1　Application of the Formulas　682
23.5.2　Discussion　683
23.6　Concluding Remarks　683
23.7　Bibliographical and Historical Notes　684

CHAPTER 24　Statistical Pattern Recognition
24.1　Introduction　687
24.2　The Nearest Neighbor Algorithm　688
24.3　Bayes’ Decision Theory　691
24.4　Relation of the Nearest Neighbor and Bayes’ Approaches　693
24.4.1　Mathematical Statement of the Problem　693
24.4.2　The Importance of the Nearest Neighbor Classifier　696
24.5　The Optimum Number of Features　696
24.6　Cost Functions and Error-Reject Tradeoff　697
24.7　The Receiver-Operator Characteristic　699
24.8　Multiple Classifiers　702
24.9　Cluster Analysis　705
24.9.1　Supervised and Unsupervised Learning　705
24.9.2　Clustering Procedures　706
24.10　Principal Components Analysis　710
24.11　The Relevance of Probability in Image Analysis　713
24.12　The Route to Face Recognition　715
24.12.1　The Face as Part of a　3-D Object　716
24.13　Another Look at Statistical Pattern Recognition: The Support Vector Machine　719
24.14　Concluding Remarks　720
24.15　Bibliographical and Historical Notes　722
24.16　Problems　723

CHAPTER 25　Biologically Inspired Recognition Schemes
25.1　Introduction　725
25.2　Artificial Neural Networks　726
25.3　The Backpropagation Algorithm　731
25.4　MLP Architectures　735
25.5　Overfitting to the Training Data　736
25.6　Optimizing the Network Architecture　739
25.7　Hebbian Learning　740
25.8　Case Study: Noise Suppression Using ANNs　745
25.9　Genetic Algorithms　750
25.10　Concluding Remarks　752
25.11　Bibliographical and Historical Notes　753

CHAPTER 26　Texture
26.1　Introduction　757
26.2　Some Basic Approaches to Texture Analysis　763
26.3　Gray-level Co-occurrence Matrices　764
26.4　Laws’ Texture Energy Approach　768
26.5　Ade’s Eigenfilter Approach　771
26.6　Appraisal of the Laws and Ade Approaches　772
26.7　Fractal-based Measures of Texture　774
26.8　Shape from Texture　775
26.9　Markov Random Field Models of Texture　776
26.10　Structural Approaches to Texture Analysis　777
26.11　Concluding Remarks　777
26.12　Bibliographical and Historical Notes　778

CHAPTER 27　Image Acquisition
27.1　Introduction　781
27.2　Illumination Schemes　782
27.2.1　Eliminating Shadows　784
27.2.2　Principles for Producing Regions of Uniform Illumination　787
27.2.3　Case of Two Infinite Parallel Strip Lights　790
27.2.4　Overview of the Uniform Illumination Scenario　793
27.2.5　Use of Line-scan Cameras　794
27.3　Cameras and Digitization　796
27.3.1　Digitization　798
27.4　The Sampling Theorem　798
27.5　Concluding Remarks　802
27.6　Bibliographical and Historical Notes　803

CHAPTER 28　Real-time Hardware and Systems Design Considerations
28.1　Introduction　805
28.2　Parallel Processing　806
28.3　SIMD Systems　807
28.4　The Gain in Speed Attainable with N Processors　809
28.5　Flynn’s Classification　810
28.6　Optimal Implementation of an Image Analysis Algorithm　813
28.6.1　Hardware Specification and Design　813
28.6.2　Basic Ideas on Optimal Hardware Implementation　814
28.7　Some Useful Real-time Hardware Options　816
28.8　Systems Design Considerations　818
28.9　Design of Inspection Systems-The Status Quo　818
28.10　System Optimization　822
28.11　The Value of Case Studies　824
28.12　Concluding Remarks　825
28.13　Bibliographical and Historical Notes　827
28.13.1　General Background　827
28.13.2　Recent Highly Relevant Work　829

PART 5　PERSPECTIVES ON VISION　831
CHAPTER 29　Machine Vision: Art or Science?
29.1　Introduction　833
29.2　Parameters of Importance in Machine Vision　834
29.3　Tradeoffs　836
29.3.1　Some Important Tradeoffs　837
29.3.2　Tradeoffs for Two-stage Template Matching　838
29.4　Future Directions　839
29.5　Hardware, Algorithms, and Processes　840
29.6　A Retrospective View　841
29.7　Just a Glimpse of Vision?　842
29.8　Bibliographical and Historical Notes　843

APPENDIX　Robust Statistics
A.1　Introduction　845
A.2　Preliminary Definitions and Analysis　848
A.3　The M-estimator (Influence Function) Approach　850
A.4　The Least Median of Squares Approach to Regression　856
A.5　Overview of the Robustness Problem　860
A.6　The RANSAC Approach　861
A.7　Concluding Remarks　863
A.8　Bibliographical and Historical Notes　864
A.9　Problem　865

List of Acronyms and Abbreviations　867
References　869
Author Index　917
Subject Index　925
……
序言 An important focus of advances in mechatronics and robotics is the addition of sensory inputs to systems with increasing "intelligence." Without doubt, sight is the "sense of choice." In everyday life, whether driving a car or threading a needle , we depend first on sight. The addition of visual perception to machines promises the greatest improvement and at the same time presents the greatest challenge.
Until relatively recently, the volume of data in the images that make up a video stream has been a serious deterrent to progress. A single frame of very modest resolution might occupy a quarter of a megabyte, so the task of handling thirty or more such frames per second requires substantial computer resources.
Fortunately, the computer and communications industries' investment in entertainment has helped address this challenge. The transmission and processing of video signals are an easy justification for selling the consumer increased computing speed and bandwidth. A digital camera, capable of video capture, has already become a fashion accessory as part of a mobile phone. As a result, video signals have become more accessible to the serious engineer. But the task of acquiring a visual image is just the tip of the iceberg.
While generating sounds and pictures is a well-defined process （speech generation is a standard "accessibility" feature of Windows）, the inverse task of recognizing connected speech is still at an unfinished state, a quarter of a century later, as any user of "dictation" software will attest. Still, analyzing sound is not even in the same league with analyzing images, particularly when they are of realworld situations rather than staged pieces with synthetic backgrounds and artificial lighting.
The task is essentially one of data reduction. From the many megabytes of the image stream, the required output might be a simple "All wheel nuts are in place" or "This tomato is ripe." But images tend to be noisy, objects that look sharp to the eye can have broken edges, boundaries can be fuzzy, and straight lines can be illusory. The task of image analysis demands a wealth of background know-how and mathematical analytic tools.
Roy Davies has been developing that rich background for well over two decades. At the time of the UK Robotics Initiative, in the 1980s, Roy had formed a relationship with the company United Biscuits. We fellow researchers might well have been amused by the task of ensuring that the blob of jam on a "Jaffacake" had been placed centrally beneath the enrobing chocolate.
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