| 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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