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词条 机器视觉理论、算法与实践
释义

《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。

版权信息

书 名: 机器视觉理论、算法与实践

作 者:(英国)E.R.Davies

出版社: 人民邮电出版社

出版时间: 2009

ISBN: 9787115195494

开本: 16

定价: 128.00 元

内容简介

《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。

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

CHAPTER1 Vision,theChallenge

1.1 Introduction-TheSenses 1

1.2 TheNatureofVision 2

1.2.1 TheProcessofRecognition 2

1.2.2 TacklingtheRecognitionProblem 4

1.2.3 ObjectLocation 7

1.2.4 SceneAnalysis 9

1.2.5 VisionasInverseGraphics 10

1.3 FromAutomatedVisualInspectiontoSurveillance 11

1.4 WhatThisBookIsAbout 12

1.5 TheFollowingChapters 14

1.6 BibliographicalNotes 15

PART1 LOW-LEVELVISION 17

CHAPTER2 ImagesandImagingOperations

2.1 Introduction 19

2.1.1 Gray-scaleversusColor 21*

2.2 ImageProcessingOperations 24

2.2.1 SomeBasicOperationsonGray-scaleImages 25

2.2.2 BasicOperationsonBinaryImages 32

2.2.3 NoiseSuppressionbyImageAccumulation 37

2.3 ConvolutionsandPointSpreadFunctions 39

2.4 SequentialversusParallelOperations 41

2.5 ConcludingRemarks 43

2.6 BibliographicalandHistoricalNotes 44

2.7 Problems 44

CHAPTER3 BasicImageFilteringOperations

3.1 Introduction 47

3.2 NoiseSuppressionbyGaussianSmoothing 49

3.3 MedianFilters 51

3.4 ModeFilters 54

3.5 RankOrderFilters 61

3.6 ReducingComputationalLoad 61

3.6.1 ABit-basedMethodforFastMedianFiltering 64

3.7 Sharp-UnsharpMasking 65

3.8 ShiftsIntroducedbyMedianFilters 66

3.8.1 ContinuumModelofMedianShifts 68

3.8.2 GeneralizationtoGray-scaleImages 72

3.8.3 ShiftsArisingwithHybridMedianFilters 75

3.8.4 ProblemswithStatistics 76

3.9 DiscreteModelofMedianShifts 78

3.9.1 GeneralizationtoGray-scaleImages 82

3.10 ShiftsIntroducedbyModeFilters 84

3.11 ShiftsIntroducedbyMeanandGaussianFilters 86

3.12 ShiftsIntroducedbyRankOrderFilters 86

3.12.1 ShiftsinRectangularNeighborhoods 87

3.12.2 CaseofHighCurvature 91

3.12.3 TestoftheModelinaDiscreteCase 91

3.13 TheRoleofFiltersinIndustrialApplicationsofVision 94

3.14 ColorinImageFiltering 94

3.15 ConcludingRemarks 96

3.16 BibliographicalandHistoricalNotes 96

3.17 Problems 98

CHAPTER4 ThresholdingTechniques

4.1 Introduction 103

4.2 Region-growingMethods 104

4.3 Thresholding 105

4.3.1 FindingaSuitableThreshold 105

4.3.2 TacklingtheProblemofBiasinThresholdSelection 107

4.3.3 AConvenientMathematicalModel 111

4.3.4 Summary 114

4.4 AdaptiveThresholding 114

4.4.1 TheChowandKanekoApproach 118

4.4.2 LocalThresholdingMethods 119

4.5 MoreThoroughgoingApproachestoThresholdSelection 122

4.5.1 Variance-basedThresholding 122

4.5.2 Entropy-basedThresholding 123

4.5.3 MaximumLikelihoodThresholding 125

4.6 ConcludingRemarks 126

4.7 BibliographicalandHistoricalNotes 127

4.8 Problems 129

CHAPTER5 EdgeDetection

5.1 Introduction 131

5.2 BasicTheoryofEdgeDetection 132

5.3 TheTemplateMatchingApproach 133

5.4 Theoryof3×3TemplateOperators 135

5.5 Summary-DesignConstraintsandConclusions 140

5.6 TheDesignofDifferentialGradientOperators 141

5.7 TheConceptofaCircularOperator 143

5.8 DetailedImplementationofCircularOperators 144

5.9 StructuredBandsofPixelsinNeighborhoodsofVariousSizes 146

5.10 TheSystematicDesignofDifferentialEdgeOperators 150

5.11 ProblemswiththeaboveApproach-SomeAlternativeSchemes 151

5.12 ConcludingRemarks 155

5.13 BibliographicalandHistoricalNotes 156

5.14 Problems 157

CHAPTER6 BinaryShapeAnalysis

6.1 Introduction 159

6.2 ConnectednessinBinaryImages 160

6.3 ObjectLabelingandCounting 161

6.3.1 SolvingtheLabelingProbleminaMoreComplexCase 164

6.4 MetricPropertiesinDigitalImages 168

6.5 SizeFiltering 169

6.6 TheConvexHullandItsComputation 171

6.7 DistanceFunctionsandTheirUses 177

6.8 SkeletonsandThinning 181

6.8.1 CrossingNumber 183

6.8.2 ParallelandSequentialImplementationsofThinning 186

6.8.3 GuidedThinning 189

6.8.4 ACommentontheNatureoftheSkeleton 189

6.8.5 SkeletonNodeAnalysis 191

6.8.6 ApplicationofSkeletonsforShapeRecognition 192

6.9 SomeSimpleMeasuresforShapeRecognition 193

6.10 ShapeDescriptionbyMoments 194

6.11 BoundaryTrackingProcedures 195

6.12 MoreDetailontheSigmaandChiFunctions 196

6.13 ConcludingRemarks 197

6.14 BibliographicalandHistoricalNotes 199

6.15 Problems 200

CHAPTER7 BoundaryPatternAnalysis

7.1 Introduction 207

7.1.1 HysteresisThresholding 209

7.2 BoundaryTrackingProcedures 212

7.3 TemplateMatching-AReminder 212

7.4 CentroidalProfiles 213

7.5 ProblemswiththeCentroidalProfileApproach 214

7.5.1 SomeSolutions 216

7.6 The(s,ψ)Plot 218

7.7 TacklingtheProblemsofOcclusion 220

7.8 ChainCode 223

7.9 The(r,s)Plot 224

7.10 AccuracyofBoundaryLengthMeasures 225

7.11 ConcludingRemarks 227

7.12 BibliographicalandHistoricalNotes 228

7.13 Problems 229

CHAPTER8 MathematicalMorphology

8.1 Introduction 233

8.2 DilationandErosioninBinaryImages 234

8.2.1 DilationandErosion 234

8.2.2 CancellationEffects 234

8.2.3 ModifiedDilationandErosionOperators 235

8.3 MathematicalMorphology 235

8.3.1 GeneralizedMorphologicalDilation 235

8.3.2 GeneralizedMorphologicalErosion 237

8.3.3 DualitybetweenDilationandErosion 238

8.3.4 PropertiesofDilationandErosionOperators 239

8.3.5 ClosingandOpening 242

8.3.6 SummaryofBasicMorphologicalOperations 245

8.3.7 Hit-and-MissTransform 248

8.3.8 TemplateMatching 249

8.4 Connectivity-basedAnalysisofImages 249

8.4.1 SkeletonsandThinning 250

8.5 Gray-scaleProcessing 251

8.5.1 MorphologicalEdgeEnhancement 252

8.5.2 FurtherRemarksontheGeneralizationtoGray-scaleProcessing 252

8.6 EffectofNoiseonMorphologicalGroupingOperations 255

8.6.1 DetailedAnalysis 257

8.6.2 Discussion 259

8.7 ConcludingRemarks 259

8.8 BibliographicalandHistoricalNotes 260

8.9 Problem 261

PART2 INTERMEDIATE-LEVELVISION 263

CHAPTER9 LineDetection

9.1 Introduction 265

9.2 ApplicationoftheHoughTransformtoLineDetection 265

9.3 TheFoot-of-NormalMethod 269

9.3.1 ErrorAnalysis 272

9.3.2 QualityoftheResultingData 274

9.3.3 ApplicationoftheFoot-of-NormalMethod 276

9.4 LongitudinalLineLocalization 276

9.5 FinalLineFitting 277

9.6 ConcludingRemarks 277

9.7 BibliographicalandHistoricalNotes 278

9.8 Problems 280

CHAPTER10 CircleDetection

10.1 Introduction 283

10.2 Hough-basedSchemesforCircularObjectDetection 284

10.3 TheProblemofUnknownCircleRadius 288

10.3.1 ExperimentalResults 290

10.4 TheProblemofAccurateCenterLocation 295

10.4.1 ObtainingaMethodforReducingComputationalLoad 296

10.4.2 ImprovementsontheBasicScheme 299

10.4.3 Discussion 300

10.4.4 PracticalDetails 300

10.5 OvercomingtheSpeedProblem 302

10.5.1 MoreDetailedEstimatesofSpeed 303

10.5.2 Robustness 305

10.5.3 ExperimentalResults 306

10.5.4 Summary 307

10.6 ConcludingRemarks 310

10.7 BibliographicalandHistoricalNotes 311

10.8 Problems 312

CHAPTER11 TheHoughTransformandItsNature

11.1 Introduction 315

11.2 TheGeneralizedHoughTransform 315

11.3 SettingUptheGeneralizedHoughTransform-SomeRelevantQuestions 317

11.4 SpatialMatchedFilteringinImages 318

11.5 FromSpatialMatchedFilterstoGeneralizedHoughTransforms 319

11.6 GradientWeightingversusUniformWeighting 320

11.6.1 CalculationofSensitivityandComputationalLoad 323

11.7 Summary 324

11.8 ApplyingtheGeneralizedHoughTransformtoLineDetection 325

11.9 TheEffectsofOcclusionsforObjectswithStraightEdges 327

11.10 FastImplementationsoftheHoughTransform 329

11.11 TheApproachofGerigandKlein 332

11.12 ConcludingRemarks 333

11.13 BibliographicalandHistoricalNotes 334

11.14 Problem 337

CHAPTER12 EllipseDetection

12.1 Introduction 339

12.2 TheDiameterBisectionMethod 339

12.3 TheChord-TangentMethod 341

12.4 FindingtheRemainingEllipseParameters 343

12.5 ReducingComputationalLoadfortheGeneralizedHoughTransformMethod 345

12.5.1 PracticalDetails 349

12.6 ComparingtheVariousMethods 353

12.7 ConcludingRemarks 355

12.8 BibliographicalandHistoricalNotes 357

12.9 Problems 358

CHAPTER13 HoleDetection

13.1 Introduction 361

13.2 TheTemplateMatchingApproach 361

13.3 TheLateralHistogramTechnique 363

13.4 TheRemovalofAmbiguitiesintheLateralHistogramTechnique 363

13.4.1 ComputationalImplicationsoftheNeedtoCheckforAmbiguities 364

13.4.2 FurtherDetailoftheSubimageMethod 366

13.5 ApplicationoftheLateralHistogramTechniqueforObjectLocation 368

13.5.1 LimitationsoftheApproach 370

13.6 AppraisaloftheHoleDetectionProblem 372

13.7 ConcludingRemarks 374

13.8 BibliographicalandHistoricalNotes 375

13.9 Problems 376

CHAPTER14 PolygonandCornerDetection

14.1 Introduction 379

14.2 TheGeneralizedHoughTransform 380

14.2.1 StraightEdgeDetection 380

14.3 ApplicationtoPolygonDetection 381

14.3.1 TheCaseofanArbitraryTriangle 382

14.3.2 TheCaseofanArbitraryRectangle 383

14.3.3 LowerBoundsontheNumbersofParameterPlanes 385

14.4 DeterminingPolygonOrientation 387

14.5 WhyCornerDetection? 389

14.6 TemplateMatching 390

14.7 Second-orderDerivativeSchemes 391

14.8 AMedian-Filter-BasedCornerDetector 393

14.8.1 AnalyzingtheOperationoftheMedianDetector 394

14.8.2 PracticalResults 396

14.9 TheHoughTransformApproachtoCornerDetection 399

14.10 ThePlesseyCornerDetector 402

14.11 CornerOrientation 404

14.12 ConcludingRemarks 406

14.13 BibliographicalandHistoricalNotes 407

14.14 Problems 410

CHAPTER15 AbstractPatternMatchingTechniques

15.1 Introduction 413

15.2 AGraph-theoreticApproachtoObjectLocation 414

15.2.1 APracticalExample-LocatingCreamBiscuits 419

15.3 PossibilitiesforSavingComputation 422

15.4 UsingtheGeneralizedHoughTransformforFeatureCollation 424

15.4.1 ComputationalLoad 426

15.5 GeneralizingtheMaximalCliqueandOtherApproaches 427

15.6 RelationalDescriptors 428

15.7 Search 432

15.8 ConcludingRemarks 433

15.9 BibliographicalandHistoricalNotes 434

15.10 Problems 437

PART3 3-DVISIONANDMOTION 443

CHAPTER16 TheThree-dimensionalWorld

16.1 Introduction 445

16.2 Three-DimensionalVision-TheVarietyofMethods 446

16.3 ProjectionSchemesforThree-dimensionalVision 448

16.3.1 BinocularImages 450

16.3.2 TheCorrespondenceProblem 452

16.4 ShapefromShading 454

16.5 PhotometricStereo 459

16.6 TheAssumptionofSurfaceSmoothness 462

16.7 ShapefromTexture 464

16.8 UseofStructuredLighting 464

16.9 Three-DimensionalObjectRecognitionSchemes 466

16.10 TheMethodofBallardandSabbah 468

16.11 TheMethodofSilberbergetal. 470

16.12 Horaud’sJunctionOrientationTechnique 472

16.13 AnImportantParadigm-LocationofIndustrialParts 476

16.14 ConcludingRemarks 478

16.15 BibliographicalandHistoricalNotes 480

16.16 Problems 482

CHAPTER17 TacklingthePerspectiven-PointProblem

17.1 Introduction 487

17.2 ThePhenomenonofPerspectiveInversion 487

17.3 AmbiguityofPoseunderWeakPerspectiveProjection 489

17.4 ObtainingUniqueSolutionstothePoseProblem 493

17.4.1 Solutionofthe 3-PointProblem 497

17.4.2 UsingSymmetricalTrapeziaforEstimatingPose 498

17.5 ConcludingRemarks 498

17.6 BibliographicalandHistoricalNotes 501

17.7 Problems 502

CHAPTER18 Motion

18.1 Introduction 505

18.2 OpticalFlow 505

18.3 InterpretationofOpticalFlowFields 509

18.4 UsingFocusofExpansiontoAvoidCollision 511

18.5 Time-to-AdjacencyAnalysis 513

18.6 BasicDifficultieswiththeOpticalFlowModel 515

18.7 StereofromMotion 516

18.8 ApplicationstotheMonitoringofTrafficFlow 518

18.8.1 TheSystemofBascleetal. 518

18.8.2 TheSystemofKolleretal. 520

18.9 PeopleTracking 524

18.9.1 SomeBasicTechniques 526

18.9.2 Within-vehiclePedestrianTracking 528

18.10 HumanGaitAnalysis 530

18.11 Model-basedTrackingofAnimals-ACaseStudy 533

18.12 Snakes 536

18.13 TheKalmanFilter 538

18.14 ConcludingRemarks 540

18.15 BibliographicalandHistoricalNotes 542

18.16 Problem 543

CHAPTER19 InvariantsandTheirApplications

19.1 Introduction 545

19.2 CrossRatios:The“RatioofRatios”Concept 547

19.3 InvariantsforNoncollinearPoints 552

19.3.1 FurtherRemarksaboutthe 5-PointConfiguration 554

19.4 InvariantsforPointsonConics 556

19.5 DifferentialandSemidifferentialInvariants 560

19.6 SymmetricalCrossRatioFunctions 562

19.7 ConcludingRemarks 564

19.8 BibliographicalandHistoricalNotes 566

19.9 Problems 567

CHAPTER20 EgomotionandRelatedTasks

20.1 Introduction 571

20.2 AutonomousMobileRobots 572

20.3 ActiveVision 573

20.4 VanishingPointDetection 574

20.5 NavigationforAutonomousMobileRobots 576

20.6 ConstructingthePlanViewofGroundPlane 579

20.7 FurtherFactorsInvolvedinMobileRobotNavigation 581

20.8 MoreonVanishingPoints 583

20.9 CentersofCirclesandEllipses 585

20.10 VehicleGuidanceinAgriculture-ACaseStudy 588

20.10.1 3-DAspectsoftheTask 590

20.10.2 Real-timeImplementation 591

20.11 ConcludingRemarks 592

20.12 BibliographicalandHistoricalNotes 592

20.13 Problems 593

CHAPTER21 ImageTransformationsandCameraCalibration

21.1 Introduction 595

21.2 ImageTransformations 596

21.3 CameraCalibration 601

21.4 IntrinsicandExtrinsicParameters 604

21.5 CorrectingforRadialDistortions 607

21.6 Multiple-viewVision 609

21.7 GeneralizedEpipolarGeometry 610

21.8 TheEssentialMatrix 611

21.9 TheFundamentalMatrix 613

21.10 PropertiesoftheEssentialandFundamentalMatrices 614

21.11 EstimatingtheFundamentalMatrix 615

21.12 ImageRectification 616

21.13 3-DReconstruction 617

21.14 AnUpdateonthe 8-PointAlgorithm 619

21.15 ConcludingRemarks 621

21.16 BibliographicalandHistoricalNotes 622

21.17 Problems 623

PART4 TOWARDREAL-TIMEPATTERNRECOGNITIONSYSTEMS 625

CHAPTER22 AutomatedVisualInspection

22.1 Introduction 627

22.2 TheProcessofInspection 628

22.3 ReviewoftheTypesofObjectstoBeInspected 629

22.3.1 FoodProducts 629

22.3.2 PrecisionComponents 630

22.3.3 DifferingRequirementsforSizeMeasurement 630

22.3.4 Three-dimensionalObjects 631

22.3.5 OtherProductsandMaterialsforInspection 632

22.4 Summary-TheMainCategoriesofInspection 632

22.5 ShapeDeviationsRelativetoaStandardTemplate 634

22.6 InspectionofCircularProducts 635

22.6.1 ComputationoftheRadialHistogram:StatisticalProblems 636

22.6.2 ApplicationofRadialHistograms 641

22.7 InspectionofPrintedCircuits 642

22.8 SteelStripandWoodInspection 643

22.9 InspectionofProductswithHighLevelsofVariability 644

22.10 X-rayInspection 648

22.11 TheImportanceofColorinInspection 651

22.12 BringingInspectiontotheFactory 653

22.13 ConcludingRemarks 654

22.14 BibliographicalandHistoricalNotes 656

CHAPTER23 InspectionofCerealGrains

23.1 Introduction 659

23.2 CaseStudy1:LocationofDarkContaminantsinCereals 660

23.2.1 ApplicationofMorphologicalandNonlinearFilterstoLocateRodentDroppings 663

23.2.2 AppraisaloftheVariousSchemas 664

23.2.3 ProblemswithClosing 665

23.3 CaseStudy2:LocationofInsects 665

23.3.1 TheVectorialStrategyforLinearFeatureDetection 666

23.3.2 DesigningLinearFeatureDetectionMasksforLargerWindows 669

23.3.3 ApplicationtoCerealInspection 670

23.3.4 ExperimentalResults 671

23.4 CaseStudy3:High-speedGrainLocation 673

23.4.1 ExtendinganEarlierSamplingApproach 673

23.4.2 ApplicationtoGrainInspection 675

23.4.3 Summary 679

23.5 OptimizingtheOutputforSetsofDirectionalTemplateMasks 680

23.5.1 ApplicationoftheFormulas 682

23.5.2 Discussion 683

23.6 ConcludingRemarks 683

23.7 BibliographicalandHistoricalNotes 684

CHAPTER24 StatisticalPatternRecognition

24.1 Introduction 687

24.2 TheNearestNeighborAlgorithm 688

24.3 Bayes’DecisionTheory 691

24.4 RelationoftheNearestNeighborandBayes’Approaches 693

24.4.1 MathematicalStatementoftheProblem 693

24.4.2 TheImportanceoftheNearestNeighborClassifier 696

24.5 TheOptimumNumberofFeatures 696

24.6 CostFunctionsandError-RejectTradeoff 697

24.7 TheReceiver-OperatorCharacteristic 699

24.8 MultipleClassifiers 702

24.9 ClusterAnalysis 705

24.9.1 SupervisedandUnsupervisedLearning 705

24.9.2 ClusteringProcedures 706

24.10 PrincipalComponentsAnalysis 710

24.11 TheRelevanceofProbabilityinImageAnalysis 713

24.12 TheRoutetoFaceRecognition 715

24.12.1 TheFaceasPartofa 3-DObject 716

24.13 AnotherLookatStatisticalPatternRecognition:TheSupportVectorMachine 719

24.14 ConcludingRemarks 720

24.15 BibliographicalandHistoricalNotes 722

24.16 Problems 723

CHAPTER25 BiologicallyInspiredRecognitionSchemes

25.1 Introduction 725

25.2 ArtificialNeuralNetworks 726

25.3 TheBackpropagationAlgorithm 731

25.4 MLPArchitectures 735

25.5 OverfittingtotheTrainingData 736

25.6 OptimizingtheNetworkArchitecture 739

25.7 HebbianLearning 740

25.8 CaseStudy:NoiseSuppressionUsingANNs 745

25.9 GeneticAlgorithms 750

25.10 ConcludingRemarks 752

25.11 BibliographicalandHistoricalNotes 753

CHAPTER26 Texture

26.1 Introduction 757

26.2 SomeBasicApproachestoTextureAnalysis 763

26.3 Gray-levelCo-occurrenceMatrices 764

26.4 Laws’TextureEnergyApproach 768

26.5 Ade’sEigenfilterApproach 771

26.6 AppraisaloftheLawsandAdeApproaches 772

26.7 Fractal-basedMeasuresofTexture 774

26.8 ShapefromTexture 775

26.9 MarkovRandomFieldModelsofTexture 776

26.10 StructuralApproachestoTextureAnalysis 777

26.11 ConcludingRemarks 777

26.12 BibliographicalandHistoricalNotes 778

CHAPTER27 ImageAcquisition

27.1 Introduction 781

27.2 IlluminationSchemes 782

27.2.1 EliminatingShadows 784

27.2.2 PrinciplesforProducingRegionsofUniformIllumination 787

27.2.3 CaseofTwoInfiniteParallelStripLights 790

27.2.4 OverviewoftheUniformIlluminationScenario 793

27.2.5 UseofLine-scanCameras 794

27.3 CamerasandDigitization 796

27.3.1 Digitization 798

27.4 TheSamplingTheorem 798

27.5 ConcludingRemarks 802

27.6 BibliographicalandHistoricalNotes 803

CHAPTER28 Real-timeHardwareandSystemsDesignConsiderations

28.1 Introduction 805

28.2 ParallelProcessing 806

28.3 SIMDSystems 807

28.4 TheGaininSpeedAttainablewithNProcessors 809

28.5 Flynn’sClassification 810

28.6 OptimalImplementationofanImageAnalysisAlgorithm 813

28.6.1 HardwareSpecificationandDesign 813

28.6.2 BasicIdeasonOptimalHardwareImplementation 814

28.7 SomeUsefulReal-timeHardwareOptions 816

28.8 SystemsDesignConsiderations 818

28.9 DesignofInspectionSystems-TheStatusQuo 818

28.10 SystemOptimization 822

28.11 TheValueofCaseStudies 824

28.12 ConcludingRemarks 825

28.13 BibliographicalandHistoricalNotes 827

28.13.1 GeneralBackground 827

28.13.2 RecentHighlyRelevantWork 829

PART5 PERSPECTIVESONVISION 831

CHAPTER29 MachineVision:ArtorScience?

29.1 Introduction 833

29.2 ParametersofImportanceinMachineVision 834

29.3 Tradeoffs 836

29.3.1 SomeImportantTradeoffs 837

29.3.2 TradeoffsforTwo-stageTemplateMatching 838

29.4 FutureDirections 839

29.5 Hardware,Algorithms,andProcesses 840

29.6 ARetrospectiveView 841

29.7 JustaGlimpseofVision? 842

29.8 BibliographicalandHistoricalNotes 843

APPENDIX RobustStatistics

A.1 Introduction 845

A.2 PreliminaryDefinitionsandAnalysis 848

A.3 TheM-estimator(InfluenceFunction)Approach 850

A.4 TheLeastMedianofSquaresApproachtoRegression 856

A.5 OverviewoftheRobustnessProblem 860

A.6 TheRANSACApproach 861

A.7 ConcludingRemarks 863

A.8 BibliographicalandHistoricalNotes 864

A.9 Problem 865

ListofAcronymsandAbbreviations 867

References 869

AuthorIndex 917

SubjectIndex 925

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