机器视觉理论、算法与实践（英文版·第3版）
CHAPTER 1 Vision，theChallenge
1.1 Introduction-TheSenses
1.2 TheNatureofVision
1.2.1 TheProcessofRecognition
1.2.2 TacklingtheRecognitionProblem
1.2.3 ObjectLocation
1.2.4 SceneAnalysis
1.2.5 VisionasInverseGraphics
1.3 FromAutomatedVisualInspectiontoSurveillance
1.4 WhatThisBookIsAbout
1.5 TheFollowingChapters
1.6 BibliographicalNotes
PART1LOW-LEVELVISION
CHAPTER 2 ImagesandImagingOperations
2.1 Introduction
2.1.1 Gray-scaleversusColor21*
2.2 ImageProcessingOperations
2.2.1 SomeBasicOperationsonGray-scaleImages
2.2.2 BasicOperationsonBinaryImages
2.2.3 NoiseSuppressionbyImageAccumulation
2.3 ConvolutionsandPointSpreadFunctions
2.4 SequentialversusParallelOperations
2.5 ConcludingRemarks
2.6 BibliographicalandHistoricalNotes
2.7 Problems
CHAPTER 3 BasicImageFilteringOperations
3.1 Introduction
3.2 NoiseSuppressionbyGaussianSmoothing
3.3 MedianFilters
3.4 ModeFilters
3.5 RankOrderFilters
3.6 ReducingComputationalLoad
3.6.1 ABit-basedMethodforFastMedianFiltering
3.7 Sharp-UnsharpMasking
3.8 ShiftsIntroducedbyMedianFilters
3.8.1 ContinuumModelofMedianShifts
3.8.2 GeneralizationtoGray-scaleImages
3.8.3 ShiftsArisingwithHybridMedianFilters
3.8.4 ProblemswithStatistics
3.9 DiscreteModelofMedianShifts
3.9.1 GeneralizationtoGray-scaleImages
3.10 ShiftsIntroducedbyModeFilters
3.11 ShiftsIntroducedbyMeanandGaussianFilters
3.12 ShiftsIntroducedbyRankOrderFilters
3.12.1 ShiftsinRectangularNeighborhoods
3.12.2 CaseofHighCurvature
3.12.3 TestoftheModelinaDiscreteCase
3.13 TheRoleofFiltersinIndustrialApplicationsofVision
3.14 ColorinImageFiltering
3.15 ConcludingRemarks
3.16 BibliographicalandHistoricalNotes
3.17 Problems
CHAPTER 4 ThresholdingTechniques
4.1 Introduction
4.2 Region-growingMethods
4.3 Thresholding
4.3.1 FindingaSuitableThreshold
4.3.2 TacklingtheProblemofBiasinThresholdSelection
4.3.3 AConvenientMathematicalModel
4.3.4 Summary
4.4 AdaptiveThresholding
4.4.1 TheChowandKanekoApproach
4.4.2 LocalThresholdingMethods
4.5 MoreThoroughgoingApproachestoThresholdSelection
4.5.1 Variance-basedThresholding
4.5.2 Entropy-basedThresholding
4.5.3 MaximumLikelihoodThresholding
4.6 ConcludingRemarks
4.7 BibliographicalandHistoricalNotes
4.8 Problems
CHAPTER 5 EdgeDetection
5.1 Introduction
5.2 BasicTheoryofEdgeDetection
5.3 TheTemplateMatchingApproach
5.4 Theoryof3×3TemplateOperators
5.5 Summary-DesignConstraintsandConclusions
5.6 TheDesignofDifferentialGradientOperators
5.7 TheConceptofaCircularOperator
5.8 DetailedImplementationofCircularOperators
5.9 StructuredBandsofPixelsinNeighborhoodsofVariousSizes
5.10 TheSystematicDesignofDifferentialEdgeOperators
5.11 ProblemswiththeaboveApproach-SomeAlternativeSchemes
5.12 ConcludingRemarks
5.13 BibliographicalandHistoricalNotes
5.14 Problems
CHAPTER 6 BinaryShapeAnalysis
6.1 Introduction
6.2 ConnectednessinBinaryImages
6.3 ObjectLabelingandCounting
6.3.1 SolvingtheLabelingProbleminaMoreComplexCase
6.4 MetricPropertiesinDigitalImages
6.5 SizeFiltering
6.6 TheConvexHullandItsComputation
6.7 DistanceFunctionsandTheirUses
6.8 SkeletonsandThinning
6.8.1 CrossingNumber
6.8.2 ParallelandSequentialImplementationsofThinning
6.8.3 GuidedThinning
6.8.4 ACommentontheNatureoftheSkeleton
6.8.5 SkeletonNodeAnalysis
6.8.6 ApplicationofSkeletonsforShapeRecognition
6.9 SomeSimpleMeasuresforShapeRecognition
6.10 ShapeDescriptionbyMoments
6.11 BoundaryTrackingProcedures
6.12 MoreDetailontheSigmaandChiFunctions
6.13 ConcludingRemarks
6.14 BibliographicalandHistoricalNotes
6.15 Problems
CHAPTER 7 BoundaryPatternAnalysis
7.1 Introduction
7.1.1 HysteresisThresholding
7.2 BoundaryTrackingProcedures
7.3 TemplateMatching-AReminder
7.4 CentroidalProfiles
7.5 ProblemswiththeCentroidalProfileApproach
7.5.1 SomeSolutions
7.6 The（s，）Plot
7.7 TacklingtheProblemsofOcclusion
7.8 ChainCode
7.9 The（r，s）Plot
7.1 0AccuracyofBoundaryLengthMeasures
7.1 1ConcludingRemarks
7.1 2BibliographicalandHistoricalNotes
7.1 3Problems
CHAPTER 8 MathematicalMorphology
8.1 Introduction
8.2 DilationandErosioninBinaryImages
8.2.1 DilationandErosion
8.2.2 CancellationEffects
8.2.3 ModifiedDilationandErosionOperators
8.3 MathematicalMorphology
8.3.1 GeneralizedMorphologicalDilation
8.3.2 GeneralizedMorphologicalErosion
8.3.3 DualitybetweenDilationandErosion
8.3.4 PropertiesofDilationandErosionOperators
8.3.5 ClosingandOpening
8.3.6 SummaryofBasicMorphologicalOperations
8.3.7 Hit-and-MissTransform
8.3.8 TemplateMatching
8.4 Connectivity-basedAnalysisofImages
8.4.1 SkeletonsandThinning
8.5 Gray-scaleProcessing
8.5.1 MorphologicalEdgeEnhancement
8.5.2 FurtherRemarksontheGeneralizationtoGray-scaleProcessing
8.6 EffectofNoiseonMorphologicalGroupingOperations
8.6.1 DetailedAnalysis
8.6.2 Discussion
8.7 ConcludingRemarks
8.8 BibliographicalandHistoricalNotes
8.9 Problem
PART 2 INTERMEDIATE-LEVELVISION
CHAPTER 9 LineDetection
9.1 Introduction
9.2 ApplicationoftheHoughTransformtoLineDetection
9.3 TheFoot-of-NormalMethod
9.3.1 ErrorAnalysis
9.3.2 QualityoftheResultingData
9.3.3 ApplicationoftheFoot-of-NormalMethod
9.4 LongitudinalLineLocalization
9.5 FinalLineFitting
9.6 ConcludingRemarks
9.7 BibliographicalandHistoricalNotes
9.8 Problems
CHAPTER 10 CircleDetection
10.1 Introduction
10.2 Hough-basedSchemesforCircularObjectDetection
10.3 TheProblemofUnknownCircleRadius
10.3.1 ExperimentalResults
10.4 TheProblemofAccurateCenterLocation
10.4.1 ObtainingaMethodforReducingComputationalLoad
10.4.2 ImprovementsontheBasicScheme
10.4.3 Discussion
10.4.4 PracticalDetails
10.5 OvercomingtheSpeedProblem
10.5.1 MoreDetailedEstimatesofSpeed
10.5.2 Robustness
10.5.3 ExperimentalResults
10.5.4 Summary
10.6 ConcludingRemarks
10.7 BibliographicalandHistoricalNotes
10.8 Problems
CHAPTER 11 TheHoughTransformandItsNature
11.1 Introduction
11.2 TheGeneralizedHoughTransform
11.3 SettingUptheGeneralizedHoughTransform-SomeRelevantQuestions
11.4 SpatialMatchedFilteringinImages
11.5 FromSpatialMatchedFilterstoGeneralizedHoughTransforms
11.6 GradientWeightingversusUniformWeighting
11.6.1 CalculationofSensitivityandComputationalLoad
11.7 Summary
11.8 ApplyingtheGeneralizedHoughTransformtoLineDetection
11.9 TheEffectsofOcclusionsforObjectswithStraightEdges
11.10 FastImplementationsoftheHoughTransform
11.11 TheApproachofGerigandKlein
11.12 ConcludingRemarks
11.13 BibliographicalandHistoricalNotes
11.14 Problem
CHAPTER 12 EllipseDetection
12.1 Introduction
12.2 TheDiameterBisectionMethod
12.3 TheChord-TangentMethod
12.4 FindingtheRemainingEllipseParameters
12.5 ReducingComputationalLoadfortheGeneralizedHoughTransformMethod
12.5.1 PracticalDetails
12.6 ComparingtheVariousMethods
12.7 ConcludingRemarks
12.8 BibliographicalandHistoricalNotes
12.9 Problems
CHAPTER 13 HoleDetection
13.1 Introduction
13.2 TheTemplateMatchingApproach
13.3 TheLateralHistogramTechnique
13.4 TheRemovalofAmbiguitiesintheLateralHistogramTechnique
13.4.1 ComputationalImplicationsoftheNeedtoCheckforAmbiguities
13.4.2 FurtherDetailoftheSubimageMethod
13.5 ApplicationoftheLateralHistogramTechniqueforObjectLocation
13.5.1 LimitationsoftheApproach
13.6 AppraisaloftheHoleDetectionProblem
13.7 ConcludingRemarks
13.8 BibliographicalandHistoricalNotes
13.9 Problems
CHAPTER 14 PolygonandCornerDetection
14.1 Introduction
14.2 TheGeneralizedHoughTransform
14.2.1 StraightEdgeDetection
14.3 ApplicationtoPolygonDetection
14.3.1 TheCaseofanArbitraryTriangle
14.3.2 TheCaseofanArbitraryRectangle
14.3.3 LowerBoundsontheNumbersofParameterPlanes
14.4 DeterminingPolygonOrientation
14.5 WhyCornerDetection？
14.6 TemplateMatching
14.7 Second-orderDerivativeSchemes
14.8 AMedian-Filter-BasedCornerDetector
14.8.1 AnalyzingtheOperationoftheMedianDetector
14.8.2 PracticalResults
14.9 TheHoughTransformApproachtoCornerDetection
14.10 ThePlesseyCornerDetector
14.11 CornerOrientation
14.12 ConcludingRemarks
14.13 BibliographicalandHistoricalNotes
14.14 Problems
CHAPTER 15 AbstractPatternMatchingTechniques
15.1 Introduction
15.2 AGraph-theoreticApproachtoObjectLocation
15.2.1 APracticalExample-LocatingCreamBiscuits
15.3 PossibilitiesforSavingComputation
15.4 UsingtheGeneralizedHoughTransformforFeatureCollation
15.4.1 ComputationalLoad
15.5 GeneralizingtheMaximalCliqueandOtherApproaches
15.6 RelationalDescriptors
15.7 Search
15.8 ConcludingRemarks
15.9 BibliographicalandHistoricalNotes
15.10 Problems
PART3 3 -DVISIONANDMOTION
CHAPTER 16 TheThree-dimensionalWorld
16.1 Introduction
16.2 Three-DimensionalVision-TheVarietyofMethods
16.3 ProjectionSchemesforThree-dimensionalVision
16.3.1 BinocularImages
16.3.2 TheCorrespondenceProblem
16.4 ShapefromShading
16.5 PhotometricStereo
16.6 TheAssumptionofSurfaceSmoothness
16.7 ShapefromTexture
16.8 UseofStructuredLighting
16.9 Three-DimensionalObjectRecognitionSchemes
16.10 TheMethodofBallardandSabbah
16.11 TheMethodofSilberbergetal.
16.12 HoraudsJunctionOrientationTechnique
16.13 AnImportantParadigm-LocationofIndustrialParts
16.14 ConcludingRemarks
16.15 BibliographicalandHistoricalNotes
16.16 Problems
CHAPTER 17 TacklingthePerspectiven-PointProblem
17.1 Introduction
17.2 ThePhenomenonofPerspectiveInversion
17.3 AmbiguityofPoseunderWeakPerspectiveProjection
17.4 ObtainingUniqueSolutionstothePoseProblem
17.4.1 Solutionofthe3-PointProblem
17.4.2 UsingSymmetricalTrapeziaforEstimatingPose
17.5 ConcludingRemarks
17.6 BibliographicalandHistoricalNotes
17.7 Problems
CHAPTER18Motion
18.1 Introduction
18.2 OpticalFlow
18.3 InterpretationofOpticalFlowFields
18.4 UsingFocusofExpansiontoAvoidCollision
18.5 Time-to-AdjacencyAnalysis
18.6 BasicDifficultieswiththeOpticalFlowModel
18.7 StereofromMotion
18.8 ApplicationstotheMonitoringofTrafficFlow
18.8.1 TheSystemofBascleetal.
18.8.2 TheSystemofKolleretal.
18.9 PeopleTracking
18.9.1 SomeBasicTechniques
18.9.2 Within-vehiclePedestrianTracking
18.10 HumanGaitAnalysis
18.11 Model-basedTrackingofAnimals-ACaseStudy
18.12 Snakes
18.13 TheKalmanFilter
18.14 ConcludingRemarks
18.15 BibliographicalandHistoricalNotes
18.16 Problem
CHAPTER 19 InvariantsandTheirApplications
19.1 Introduction
19.2 CrossRatios：The“RatioofRatios”Concept
19.3 InvariantsforNoncollinearPoints
19.3.1 FurtherRemarksaboutthe5-PointConfiguration
19.4 InvariantsforPointsonConics
19.5 DifferentialandSemidifferentialInvariants
19.6 SymmetricalCrossRatioFunctions
19.7 ConcludingRemarks
19.8 BibliographicalandHistoricalNotes
19.9 Problems
CHAPTER 20 EgomotionandRelatedTasks
20.1 Introduction
20.2 AutonomousMobileRobots
20.3 ActiveVision
20.4 VanishingPointDetection
20.5 NavigationforAutonomousMobileRobots
20.6 ConstructingthePlanViewofGroundPlane
20.7 FurtherFactorsInvolvedinMobileRobotNavigation
20.8 MoreonVanishingPoints
20.9 CentersofCirclesandEllipses
20.10 VehicleGuidanceinAgriculture-ACaseStudy
20.10.1 3-DAspectsoftheTask
20.10.2 Real-timeImplementation
20.11 ConcludingRemarks
20.12 BibliographicalandHistoricalNotes
20.13 Problems
CHAPTER 21 ImageTransformationsandCameraCalibration
21.1 Introduction
21.2 ImageTransformations
21.3 CameraCalibration
21.4 IntrinsicandExtrinsicParameters
21.5 CorrectingforRadialDistortions
21.6 Multiple-viewVision
21.7 GeneralizedEpipolarGeometry
21.8 TheEssentialMatrix
21.9 TheFundamentalMatrix
21.10 PropertiesoftheEssentialandFundamentalMatrices
21.11 EstimatingtheFundamentalMatrix
21.12 ImageRectification
21.13 3-DReconstruction
21.14 AnUpdateonthe8-PointAlgorithm
21.15 ConcludingRemarks
21.16 BibliographicalandHistoricalNotes
21.17 Problems
PART 4 TOWARDREAL-TIMEPATTERNRECOGNITIONSYSTEMS
CHAPTER 22 AutomatedVisualInspection
22.1 Introduction
22.2 TheProcessofInspection
22.3 ReviewoftheTypesofObjectstoBeInspected
22.3.1 FoodProducts
22.3.2 PrecisionComponents
22.3.3 DifferingRequirementsforSizeMeasurement
22.3.4 Three-dimensionalObjects
22.3.5 OtherProductsandMaterialsforInspection
22.4 Summary-TheMainCategoriesofInspection
22.5 ShapeDeviationsRelativetoaStandardTemplate
22.6 InspectionofCircularProducts
22.6.1 ComputationoftheRadialHistogram：StatisticalProblems
22.6.2 ApplicationofRadialHistograms
22.7 InspectionofPrintedCircuits
22.8 SteelStripandWoodInspection
22.9 InspectionofProductswithHighLevelsofVariability
22.10 X-rayInspection
22.11 TheImportanceofColorinInspection
22.12 BringingInspectiontotheFactory
22.13 ConcludingRemarks
22.14 BibliographicalandHistoricalNotes
CHAPTER 23 InspectionofCerealGrains
23.1 Introduction
23.2 CaseStudy1：LocationofDarkContaminantsinCereals
23.2.1 ApplicationofMorphologicalandNonlinearFilterstoLocateRodentDroppings
23.2.2 AppraisaloftheVariousSchemas
23.2.3 ProblemswithClosing
23.3 CaseStudy2：LocationofInsects
23.3.1 TheVectorialStrategyforLinearFeatureDetection
23.3.2 DesigningLinearFeatureDetectionMasksforLargerWindows
23.3.3 ApplicationtoCerealInspection
23.3.4 ExperimentalResults
23.4 CaseStudy3：High-speedGrainLocation
23.4.1 ExtendinganEarlierSamplingApproach
23.4.2 ApplicationtoGrainInspection
23.4.3 Summary
23.5 OptimizingtheOutputforSetsofDirectionalTemplateMasks
23.5.1 ApplicationoftheFormulas
23.5.2 Discussion
23.6 ConcludingRemarks
23.7 BibliographicalandHistoricalNotes
CHAPTER 24 StatisticalPatternRecognition
24.1 Introduction
24.2 TheNearestNeighborAlgorithm
24.3 BayesDecisionTheory
24.4 RelationoftheNearestNeighborandBayesApproaches
24.4.1 MathematicalStatementoftheProblem
24.4.2 TheImportanceoftheNearestNeighborClassifier
24.5 TheOptimumNumberofFeatures
24.6 CostFunctionsandError-RejectTradeoff
24.7 TheReceiver-OperatorCharacteristic
24.8 MultipleClassifiers
24.9 ClusterAnalysis
24.9.1 SupervisedandUnsupervisedLearning
24.9.2 ClusteringProcedures
24.10 PrincipalComponentsAnalysis
24.11 TheRelevanceofProbabilityinImageAnalysis
24.12 TheRoutetoFaceRecognition
24.12.1 TheFaceasPartofa3-DObject
24.13 AnotherLookatStatisticalPatternRecognition：TheSupportVectorMachine
24.14 ConcludingRemarks
24.15 BibliographicalandHistoricalNotes
24.16 Problems
CHAPTER 25 BiologicallyInspiredRecognitionSchemes
25.1 Introduction
25.2 ArtificialNeuralNetworks
25.3 TheBackpropagationAlgorithm
25.4 MLPArchitectures
25.5 OverfittingtotheTrainingData
25.6 OptimizingtheNetworkArchitecture
25.7 HebbianLearning
25.8 CaseStudy：NoiseSuppressionUsingANNs
25.9 GeneticAlgorithms
25.10 ConcludingRemarks
25.11 BibliographicalandHistoricalNotes
CHAPTER 26 Texture
26.1 Introduction
26.2 SomeBasicApproachestoTextureAnalysis
26.3 Gray-levelCo-occurrenceMatrices
26.4 LawsTextureEnergyApproach
26.5 AdesEigenfilterApproach
26.6 AppraisaloftheLawsandAdeApproaches
26.7 Fractal-basedMeasuresofTexture
26.8 ShapefromTexture
26.9 MarkovRandomFieldModelsofTexture
26.10 StructuralApproachestoTextureAnalysis
26.11 ConcludingRemarks
26.12 BibliographicalandHistoricalNotes
CHAPTER 27 ImageAcquisition
27.1 Introduction
27.2 IlluminationSchemes
27.2.1 EliminatingShadows
27.2.2 PrinciplesforProducingRegionsofUniformIllumination
27.2.3 CaseofTwoInfiniteParallelStripLights
27.2.4 OverviewoftheUniformIlluminationScenario
27.2.5 UseofLine-scanCameras
27.3 CamerasandDigitization
27.3.1 Digitization
27.4 TheSamplingTheorem
27.5 ConcludingRemarks
27.6 BibliographicalandHistoricalNotes
CHAPTER 28 Real-timeHardwareandSystemsDesignConsiderations
28.1 Introduction
28.2 ParallelProcessing
28.3 SIMDSystems
28.4 TheGaininSpeedAttainablewithNProcessors
28.5 FlynnsClassification
28.6 OptimalImplementationofanImageAnalysisAlgorithm
28.6.1 HardwareSpecificationandDesign
28.6.2 BasicIdeasonOptimalHardwareImplementation
28.7 SomeUsefulReal-timeHardwareOptions
28.8 SystemsDesignConsiderations
28.9 DesignofInspectionSystems-TheStatusQuo
28.10 SystemOptimization
28.11 TheValueofCaseStudies
28.12 ConcludingRemarks
28.13 BibliographicalandHistoricalNotes
28.13.1 GeneralBackground
28.13.2 RecentHighlyRelevantWork
PART5PERSPECTIVESONVISION
CHAPTER 29 MachineVision：ArtorScience？
29.1 Introduction
29.2 ParametersofImportanceinMachineVision
29.3 Tradeoffs
29.3.1 SomeImportantTradeoffs
29.3.2 TradeoffsforTwo-stageTemplateMatching
29.4 FutureDirections
29.5 Hardware，Algorithms，andProcesses
29.6 ARetrospectiveView
29.7 JustaGlimpseofVision？
29.8 BibliographicalandHistoricalNotes
APPENDIXRobustStatistics
A.1 Introduction
A.2 PreliminaryDefinitionsandAnalysis
A.3 TheM-estimator（InfluenceFunction）Approach
A.4 TheLeastMedianofSquaresApproachtoRegression
A.5 OverviewoftheRobustnessProblem
A.6 TheRANSACApproach
A.7 ConcludingRemarks
A.8 BibliographicalandHistoricalNotes
A.9 Problem
ListofAcronymsandAbbreviations
References
AuthorIndex
SubjectIndex
1.1 Introduction-TheSenses
1.2 TheNatureofVision
1.2.1 TheProcessofRecognition
1.2.2 TacklingtheRecognitionProblem
1.2.3 ObjectLocation
1.2.4 SceneAnalysis
1.2.5 VisionasInverseGraphics
1.3 FromAutomatedVisualInspectiontoSurveillance
1.4 WhatThisBookIsAbout
1.5 TheFollowingChapters
1.6 BibliographicalNotes
PART1LOW-LEVELVISION
CHAPTER 2 ImagesandImagingOperations
2.1 Introduction
2.1.1 Gray-scaleversusColor21*
2.2 ImageProcessingOperations
2.2.1 SomeBasicOperationsonGray-scaleImages
2.2.2 BasicOperationsonBinaryImages
2.2.3 NoiseSuppressionbyImageAccumulation
2.3 ConvolutionsandPointSpreadFunctions
2.4 SequentialversusParallelOperations
2.5 ConcludingRemarks
2.6 BibliographicalandHistoricalNotes
2.7 Problems
CHAPTER 3 BasicImageFilteringOperations
3.1 Introduction
3.2 NoiseSuppressionbyGaussianSmoothing
3.3 MedianFilters
3.4 ModeFilters
3.5 RankOrderFilters
3.6 ReducingComputationalLoad
3.6.1 ABit-basedMethodforFastMedianFiltering
3.7 Sharp-UnsharpMasking
3.8 ShiftsIntroducedbyMedianFilters
3.8.1 ContinuumModelofMedianShifts
3.8.2 GeneralizationtoGray-scaleImages
3.8.3 ShiftsArisingwithHybridMedianFilters
3.8.4 ProblemswithStatistics
3.9 DiscreteModelofMedianShifts
3.9.1 GeneralizationtoGray-scaleImages
3.10 ShiftsIntroducedbyModeFilters
3.11 ShiftsIntroducedbyMeanandGaussianFilters
3.12 ShiftsIntroducedbyRankOrderFilters
3.12.1 ShiftsinRectangularNeighborhoods
3.12.2 CaseofHighCurvature
3.12.3 TestoftheModelinaDiscreteCase
3.13 TheRoleofFiltersinIndustrialApplicationsofVision
3.14 ColorinImageFiltering
3.15 ConcludingRemarks
3.16 BibliographicalandHistoricalNotes
3.17 Problems
CHAPTER 4 ThresholdingTechniques
4.1 Introduction
4.2 Region-growingMethods
4.3 Thresholding
4.3.1 FindingaSuitableThreshold
4.3.2 TacklingtheProblemofBiasinThresholdSelection
4.3.3 AConvenientMathematicalModel
4.3.4 Summary
4.4 AdaptiveThresholding
4.4.1 TheChowandKanekoApproach
4.4.2 LocalThresholdingMethods
4.5 MoreThoroughgoingApproachestoThresholdSelection
4.5.1 Variance-basedThresholding
4.5.2 Entropy-basedThresholding
4.5.3 MaximumLikelihoodThresholding
4.6 ConcludingRemarks
4.7 BibliographicalandHistoricalNotes
4.8 Problems
CHAPTER 5 EdgeDetection
5.1 Introduction
5.2 BasicTheoryofEdgeDetection
5.3 TheTemplateMatchingApproach
5.4 Theoryof3×3TemplateOperators
5.5 Summary-DesignConstraintsandConclusions
5.6 TheDesignofDifferentialGradientOperators
5.7 TheConceptofaCircularOperator
5.8 DetailedImplementationofCircularOperators
5.9 StructuredBandsofPixelsinNeighborhoodsofVariousSizes
5.10 TheSystematicDesignofDifferentialEdgeOperators
5.11 ProblemswiththeaboveApproach-SomeAlternativeSchemes
5.12 ConcludingRemarks
5.13 BibliographicalandHistoricalNotes
5.14 Problems
CHAPTER 6 BinaryShapeAnalysis
6.1 Introduction
6.2 ConnectednessinBinaryImages
6.3 ObjectLabelingandCounting
6.3.1 SolvingtheLabelingProbleminaMoreComplexCase
6.4 MetricPropertiesinDigitalImages
6.5 SizeFiltering
6.6 TheConvexHullandItsComputation
6.7 DistanceFunctionsandTheirUses
6.8 SkeletonsandThinning
6.8.1 CrossingNumber
6.8.2 ParallelandSequentialImplementationsofThinning
6.8.3 GuidedThinning
6.8.4 ACommentontheNatureoftheSkeleton
6.8.5 SkeletonNodeAnalysis
6.8.6 ApplicationofSkeletonsforShapeRecognition
6.9 SomeSimpleMeasuresforShapeRecognition
6.10 ShapeDescriptionbyMoments
6.11 BoundaryTrackingProcedures
6.12 MoreDetailontheSigmaandChiFunctions
6.13 ConcludingRemarks
6.14 BibliographicalandHistoricalNotes
6.15 Problems
CHAPTER 7 BoundaryPatternAnalysis
7.1 Introduction
7.1.1 HysteresisThresholding
7.2 BoundaryTrackingProcedures
7.3 TemplateMatching-AReminder
7.4 CentroidalProfiles
7.5 ProblemswiththeCentroidalProfileApproach
7.5.1 SomeSolutions
7.6 The（s，）Plot
7.7 TacklingtheProblemsofOcclusion
7.8 ChainCode
7.9 The（r，s）Plot
7.1 0AccuracyofBoundaryLengthMeasures
7.1 1ConcludingRemarks
7.1 2BibliographicalandHistoricalNotes
7.1 3Problems
CHAPTER 8 MathematicalMorphology
8.1 Introduction
8.2 DilationandErosioninBinaryImages
8.2.1 DilationandErosion
8.2.2 CancellationEffects
8.2.3 ModifiedDilationandErosionOperators
8.3 MathematicalMorphology
8.3.1 GeneralizedMorphologicalDilation
8.3.2 GeneralizedMorphologicalErosion
8.3.3 DualitybetweenDilationandErosion
8.3.4 PropertiesofDilationandErosionOperators
8.3.5 ClosingandOpening
8.3.6 SummaryofBasicMorphologicalOperations
8.3.7 Hit-and-MissTransform
8.3.8 TemplateMatching
8.4 Connectivity-basedAnalysisofImages
8.4.1 SkeletonsandThinning
8.5 Gray-scaleProcessing
8.5.1 MorphologicalEdgeEnhancement
8.5.2 FurtherRemarksontheGeneralizationtoGray-scaleProcessing
8.6 EffectofNoiseonMorphologicalGroupingOperations
8.6.1 DetailedAnalysis
8.6.2 Discussion
8.7 ConcludingRemarks
8.8 BibliographicalandHistoricalNotes
8.9 Problem
PART 2 INTERMEDIATE-LEVELVISION
CHAPTER 9 LineDetection
9.1 Introduction
9.2 ApplicationoftheHoughTransformtoLineDetection
9.3 TheFoot-of-NormalMethod
9.3.1 ErrorAnalysis
9.3.2 QualityoftheResultingData
9.3.3 ApplicationoftheFoot-of-NormalMethod
9.4 LongitudinalLineLocalization
9.5 FinalLineFitting
9.6 ConcludingRemarks
9.7 BibliographicalandHistoricalNotes
9.8 Problems
CHAPTER 10 CircleDetection
10.1 Introduction
10.2 Hough-basedSchemesforCircularObjectDetection
10.3 TheProblemofUnknownCircleRadius
10.3.1 ExperimentalResults
10.4 TheProblemofAccurateCenterLocation
10.4.1 ObtainingaMethodforReducingComputationalLoad
10.4.2 ImprovementsontheBasicScheme
10.4.3 Discussion
10.4.4 PracticalDetails
10.5 OvercomingtheSpeedProblem
10.5.1 MoreDetailedEstimatesofSpeed
10.5.2 Robustness
10.5.3 ExperimentalResults
10.5.4 Summary
10.6 ConcludingRemarks
10.7 BibliographicalandHistoricalNotes
10.8 Problems
CHAPTER 11 TheHoughTransformandItsNature
11.1 Introduction
11.2 TheGeneralizedHoughTransform
11.3 SettingUptheGeneralizedHoughTransform-SomeRelevantQuestions
11.4 SpatialMatchedFilteringinImages
11.5 FromSpatialMatchedFilterstoGeneralizedHoughTransforms
11.6 GradientWeightingversusUniformWeighting
11.6.1 CalculationofSensitivityandComputationalLoad
11.7 Summary
11.8 ApplyingtheGeneralizedHoughTransformtoLineDetection
11.9 TheEffectsofOcclusionsforObjectswithStraightEdges
11.10 FastImplementationsoftheHoughTransform
11.11 TheApproachofGerigandKlein
11.12 ConcludingRemarks
11.13 BibliographicalandHistoricalNotes
11.14 Problem
CHAPTER 12 EllipseDetection
12.1 Introduction
12.2 TheDiameterBisectionMethod
12.3 TheChord-TangentMethod
12.4 FindingtheRemainingEllipseParameters
12.5 ReducingComputationalLoadfortheGeneralizedHoughTransformMethod
12.5.1 PracticalDetails
12.6 ComparingtheVariousMethods
12.7 ConcludingRemarks
12.8 BibliographicalandHistoricalNotes
12.9 Problems
CHAPTER 13 HoleDetection
13.1 Introduction
13.2 TheTemplateMatchingApproach
13.3 TheLateralHistogramTechnique
13.4 TheRemovalofAmbiguitiesintheLateralHistogramTechnique
13.4.1 ComputationalImplicationsoftheNeedtoCheckforAmbiguities
13.4.2 FurtherDetailoftheSubimageMethod
13.5 ApplicationoftheLateralHistogramTechniqueforObjectLocation
13.5.1 LimitationsoftheApproach
13.6 AppraisaloftheHoleDetectionProblem
13.7 ConcludingRemarks
13.8 BibliographicalandHistoricalNotes
13.9 Problems
CHAPTER 14 PolygonandCornerDetection
14.1 Introduction
14.2 TheGeneralizedHoughTransform
14.2.1 StraightEdgeDetection
14.3 ApplicationtoPolygonDetection
14.3.1 TheCaseofanArbitraryTriangle
14.3.2 TheCaseofanArbitraryRectangle
14.3.3 LowerBoundsontheNumbersofParameterPlanes
14.4 DeterminingPolygonOrientation
14.5 WhyCornerDetection？
14.6 TemplateMatching
14.7 Second-orderDerivativeSchemes
14.8 AMedian-Filter-BasedCornerDetector
14.8.1 AnalyzingtheOperationoftheMedianDetector
14.8.2 PracticalResults
14.9 TheHoughTransformApproachtoCornerDetection
14.10 ThePlesseyCornerDetector
14.11 CornerOrientation
14.12 ConcludingRemarks
14.13 BibliographicalandHistoricalNotes
14.14 Problems
CHAPTER 15 AbstractPatternMatchingTechniques
15.1 Introduction
15.2 AGraph-theoreticApproachtoObjectLocation
15.2.1 APracticalExample-LocatingCreamBiscuits
15.3 PossibilitiesforSavingComputation
15.4 UsingtheGeneralizedHoughTransformforFeatureCollation
15.4.1 ComputationalLoad
15.5 GeneralizingtheMaximalCliqueandOtherApproaches
15.6 RelationalDescriptors
15.7 Search
15.8 ConcludingRemarks
15.9 BibliographicalandHistoricalNotes
15.10 Problems
PART3 3 -DVISIONANDMOTION
CHAPTER 16 TheThree-dimensionalWorld
16.1 Introduction
16.2 Three-DimensionalVision-TheVarietyofMethods
16.3 ProjectionSchemesforThree-dimensionalVision
16.3.1 BinocularImages
16.3.2 TheCorrespondenceProblem
16.4 ShapefromShading
16.5 PhotometricStereo
16.6 TheAssumptionofSurfaceSmoothness
16.7 ShapefromTexture
16.8 UseofStructuredLighting
16.9 Three-DimensionalObjectRecognitionSchemes
16.10 TheMethodofBallardandSabbah
16.11 TheMethodofSilberbergetal.
16.12 HoraudsJunctionOrientationTechnique
16.13 AnImportantParadigm-LocationofIndustrialParts
16.14 ConcludingRemarks
16.15 BibliographicalandHistoricalNotes
16.16 Problems
CHAPTER 17 TacklingthePerspectiven-PointProblem
17.1 Introduction
17.2 ThePhenomenonofPerspectiveInversion
17.3 AmbiguityofPoseunderWeakPerspectiveProjection
17.4 ObtainingUniqueSolutionstothePoseProblem
17.4.1 Solutionofthe3-PointProblem
17.4.2 UsingSymmetricalTrapeziaforEstimatingPose
17.5 ConcludingRemarks
17.6 BibliographicalandHistoricalNotes
17.7 Problems
CHAPTER18Motion
18.1 Introduction
18.2 OpticalFlow
18.3 InterpretationofOpticalFlowFields
18.4 UsingFocusofExpansiontoAvoidCollision
18.5 Time-to-AdjacencyAnalysis
18.6 BasicDifficultieswiththeOpticalFlowModel
18.7 StereofromMotion
18.8 ApplicationstotheMonitoringofTrafficFlow
18.8.1 TheSystemofBascleetal.
18.8.2 TheSystemofKolleretal.
18.9 PeopleTracking
18.9.1 SomeBasicTechniques
18.9.2 Within-vehiclePedestrianTracking
18.10 HumanGaitAnalysis
18.11 Model-basedTrackingofAnimals-ACaseStudy
18.12 Snakes
18.13 TheKalmanFilter
18.14 ConcludingRemarks
18.15 BibliographicalandHistoricalNotes
18.16 Problem
CHAPTER 19 InvariantsandTheirApplications
19.1 Introduction
19.2 CrossRatios：The“RatioofRatios”Concept
19.3 InvariantsforNoncollinearPoints
19.3.1 FurtherRemarksaboutthe5-PointConfiguration
19.4 InvariantsforPointsonConics
19.5 DifferentialandSemidifferentialInvariants
19.6 SymmetricalCrossRatioFunctions
19.7 ConcludingRemarks
19.8 BibliographicalandHistoricalNotes
19.9 Problems
CHAPTER 20 EgomotionandRelatedTasks
20.1 Introduction
20.2 AutonomousMobileRobots
20.3 ActiveVision
20.4 VanishingPointDetection
20.5 NavigationforAutonomousMobileRobots
20.6 ConstructingthePlanViewofGroundPlane
20.7 FurtherFactorsInvolvedinMobileRobotNavigation
20.8 MoreonVanishingPoints
20.9 CentersofCirclesandEllipses
20.10 VehicleGuidanceinAgriculture-ACaseStudy
20.10.1 3-DAspectsoftheTask
20.10.2 Real-timeImplementation
20.11 ConcludingRemarks
20.12 BibliographicalandHistoricalNotes
20.13 Problems
CHAPTER 21 ImageTransformationsandCameraCalibration
21.1 Introduction
21.2 ImageTransformations
21.3 CameraCalibration
21.4 IntrinsicandExtrinsicParameters
21.5 CorrectingforRadialDistortions
21.6 Multiple-viewVision
21.7 GeneralizedEpipolarGeometry
21.8 TheEssentialMatrix
21.9 TheFundamentalMatrix
21.10 PropertiesoftheEssentialandFundamentalMatrices
21.11 EstimatingtheFundamentalMatrix
21.12 ImageRectification
21.13 3-DReconstruction
21.14 AnUpdateonthe8-PointAlgorithm
21.15 ConcludingRemarks
21.16 BibliographicalandHistoricalNotes
21.17 Problems
PART 4 TOWARDREAL-TIMEPATTERNRECOGNITIONSYSTEMS
CHAPTER 22 AutomatedVisualInspection
22.1 Introduction
22.2 TheProcessofInspection
22.3 ReviewoftheTypesofObjectstoBeInspected
22.3.1 FoodProducts
22.3.2 PrecisionComponents
22.3.3 DifferingRequirementsforSizeMeasurement
22.3.4 Three-dimensionalObjects
22.3.5 OtherProductsandMaterialsforInspection
22.4 Summary-TheMainCategoriesofInspection
22.5 ShapeDeviationsRelativetoaStandardTemplate
22.6 InspectionofCircularProducts
22.6.1 ComputationoftheRadialHistogram：StatisticalProblems
22.6.2 ApplicationofRadialHistograms
22.7 InspectionofPrintedCircuits
22.8 SteelStripandWoodInspection
22.9 InspectionofProductswithHighLevelsofVariability
22.10 X-rayInspection
22.11 TheImportanceofColorinInspection
22.12 BringingInspectiontotheFactory
22.13 ConcludingRemarks
22.14 BibliographicalandHistoricalNotes
CHAPTER 23 InspectionofCerealGrains
23.1 Introduction
23.2 CaseStudy1：LocationofDarkContaminantsinCereals
23.2.1 ApplicationofMorphologicalandNonlinearFilterstoLocateRodentDroppings
23.2.2 AppraisaloftheVariousSchemas
23.2.3 ProblemswithClosing
23.3 CaseStudy2：LocationofInsects
23.3.1 TheVectorialStrategyforLinearFeatureDetection
23.3.2 DesigningLinearFeatureDetectionMasksforLargerWindows
23.3.3 ApplicationtoCerealInspection
23.3.4 ExperimentalResults
23.4 CaseStudy3：High-speedGrainLocation
23.4.1 ExtendinganEarlierSamplingApproach
23.4.2 ApplicationtoGrainInspection
23.4.3 Summary
23.5 OptimizingtheOutputforSetsofDirectionalTemplateMasks
23.5.1 ApplicationoftheFormulas
23.5.2 Discussion
23.6 ConcludingRemarks
23.7 BibliographicalandHistoricalNotes
CHAPTER 24 StatisticalPatternRecognition
24.1 Introduction
24.2 TheNearestNeighborAlgorithm
24.3 BayesDecisionTheory
24.4 RelationoftheNearestNeighborandBayesApproaches
24.4.1 MathematicalStatementoftheProblem
24.4.2 TheImportanceoftheNearestNeighborClassifier
24.5 TheOptimumNumberofFeatures
24.6 CostFunctionsandError-RejectTradeoff
24.7 TheReceiver-OperatorCharacteristic
24.8 MultipleClassifiers
24.9 ClusterAnalysis
24.9.1 SupervisedandUnsupervisedLearning
24.9.2 ClusteringProcedures
24.10 PrincipalComponentsAnalysis
24.11 TheRelevanceofProbabilityinImageAnalysis
24.12 TheRoutetoFaceRecognition
24.12.1 TheFaceasPartofa3-DObject
24.13 AnotherLookatStatisticalPatternRecognition：TheSupportVectorMachine
24.14 ConcludingRemarks
24.15 BibliographicalandHistoricalNotes
24.16 Problems
CHAPTER 25 BiologicallyInspiredRecognitionSchemes
25.1 Introduction
25.2 ArtificialNeuralNetworks
25.3 TheBackpropagationAlgorithm
25.4 MLPArchitectures
25.5 OverfittingtotheTrainingData
25.6 OptimizingtheNetworkArchitecture
25.7 HebbianLearning
25.8 CaseStudy：NoiseSuppressionUsingANNs
25.9 GeneticAlgorithms
25.10 ConcludingRemarks
25.11 BibliographicalandHistoricalNotes
CHAPTER 26 Texture
26.1 Introduction
26.2 SomeBasicApproachestoTextureAnalysis
26.3 Gray-levelCo-occurrenceMatrices
26.4 LawsTextureEnergyApproach
26.5 AdesEigenfilterApproach
26.6 AppraisaloftheLawsandAdeApproaches
26.7 Fractal-basedMeasuresofTexture
26.8 ShapefromTexture
26.9 MarkovRandomFieldModelsofTexture
26.10 StructuralApproachestoTextureAnalysis
26.11 ConcludingRemarks
26.12 BibliographicalandHistoricalNotes
CHAPTER 27 ImageAcquisition
27.1 Introduction
27.2 IlluminationSchemes
27.2.1 EliminatingShadows
27.2.2 PrinciplesforProducingRegionsofUniformIllumination
27.2.3 CaseofTwoInfiniteParallelStripLights
27.2.4 OverviewoftheUniformIlluminationScenario
27.2.5 UseofLine-scanCameras
27.3 CamerasandDigitization
27.3.1 Digitization
27.4 TheSamplingTheorem
27.5 ConcludingRemarks
27.6 BibliographicalandHistoricalNotes
CHAPTER 28 Real-timeHardwareandSystemsDesignConsiderations
28.1 Introduction
28.2 ParallelProcessing
28.3 SIMDSystems
28.4 TheGaininSpeedAttainablewithNProcessors
28.5 FlynnsClassification
28.6 OptimalImplementationofanImageAnalysisAlgorithm
28.6.1 HardwareSpecificationandDesign
28.6.2 BasicIdeasonOptimalHardwareImplementation
28.7 SomeUsefulReal-timeHardwareOptions
28.8 SystemsDesignConsiderations
28.9 DesignofInspectionSystems-TheStatusQuo
28.10 SystemOptimization
28.11 TheValueofCaseStudies
28.12 ConcludingRemarks
28.13 BibliographicalandHistoricalNotes
28.13.1 GeneralBackground
28.13.2 RecentHighlyRelevantWork
PART5PERSPECTIVESONVISION
CHAPTER 29 MachineVision：ArtorScience？
29.1 Introduction
29.2 ParametersofImportanceinMachineVision
29.3 Tradeoffs
29.3.1 SomeImportantTradeoffs
29.3.2 TradeoffsforTwo-stageTemplateMatching
29.4 FutureDirections
29.5 Hardware，Algorithms，andProcesses
29.6 ARetrospectiveView
29.7 JustaGlimpseofVision？
29.8 BibliographicalandHistoricalNotes
APPENDIXRobustStatistics
A.1 Introduction
A.2 PreliminaryDefinitionsandAnalysis
A.3 TheM-estimator（InfluenceFunction）Approach
A.4 TheLeastMedianofSquaresApproachtoRegression
A.5 OverviewoftheRobustnessProblem
A.6 TheRANSACApproach
A.7 ConcludingRemarks
A.8 BibliographicalandHistoricalNotes
A.9 Problem
ListofAcronymsandAbbreviations
References
AuthorIndex
SubjectIndex
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。
《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，本书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。
《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。
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