标题：机器视觉理论、算法与实践
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
……