智能视感学（英文版）

图书标准信息

• 作者 张秀彬、曼苏乐（MUHAM） 著
• 出版社 中国水利水电出版社
• 出版时间 2012-08
• 版次 1
• ISBN 9787517000907
• 定价 39.00元
• 装帧 平装
• 开本 其他
• 纸张 其他

【内容简介】

《智能视感学（英文版）》从计算机视感及其信号处理的基本概念与基础理论出发，阐述了基于图像信息的识别、理解与检测技术原理与方法。《智能视感学（英文版）》根据作者多年来从事智能视感理论与技术研究的成果，结合研究性本科与研究生教学特点编撰而成。全书分为基础篇与应用篇两大部分，其中，基础篇系统地介绍了智能视感的基本原理、方法、关键技术及其算法；应用篇则由配合主要基础理论和方法的应用技术实例所组成。全书遵循理论知识与实用技术的紧密结合、数学方法与实用效果的相互映证等编写原则。《智能视感学（英文版）》涉及的教学内容主要包括：图像处理基础、摄像机数学模型、视感识别与检测原理、智能视感实用技术等。《智能视感学（英文版）》可以作为检测与控制、自动化、计算机、机器人及人工智能等专业的高年级本科生和研究生的教材，也可作为专业技术人员的参考工具书。

【目录】

Foreword
Preface
Basearticle
Chapter1Introduction
1.1Overview
1.1.1ConceptabouttheVisualPerception
1.1.2TheDevelopmentofVisualPerceptionTechnology
1.1.3ClassificationofVisualPerceptionSystem
1.2AVisualPerceptionHardware-base
1.2.1iImageSe ing
1.2.2ImageAcquisition
1.2.3PCHardwareRequirementsforVPS
Exercises
Chapter2Foundatio ofImageProcessing
2.1BasicProcessingMethodsforGrayImage
2.1.1SpatialDomainEnhancementAlgorithm
2.1.2FrequencyDomainEnhancementAlgorithm
2.2EdgeDetectionofGrayImage
2.2.1ThresholdEdgeDetection
2.2.2Gradient-basedEdgeDetection
2.Z.3LaplacianOperator
2.2.4CannyEdgeOperator
2.2.5MathematicalMorphologicalMethod
2.2.6BriefDescriptionofOtherAlgorithms
2.3BinarizationProcessingandSegmentationofImage
2.3.1GeneralDescription
2.3.2Histogram-basedValley-pointThresholdImageBinarization
2.3.3OTSUAlgorithm
2.3.4MinimumErrorMethodofImageSegmentation
2.4ColorImageEnhancement
2.4.1ColorSpaceandItsTra formation
2.4.2HistogramEqualizationofColorLevelsinColorImage
2.5ColorImageEdgeDetection
2.5.1ColorImageEdgeDetectionBasedonGradientExtremeValue
2.5.2PracticalMethodforColorImageEdgeDetection
Exercises
Chapter3MathematicalModeloftheCamera
3.1GeometricTra formatio ofImageSpace
3.1.1HomogeneousCoordinates
3.1.2OrthogonalTra formationandRigidBodyTra formation
3.1.3SimilarityTra formationandAffineTra formation
3.1.4Pe pectiveTra formation
3.2ImageCoordinateSystemandItsTra formation
3.2.1ImageCoordinateSystem
3.2.2ImageCoordinateTra formation
3.3CommonMethodofCalibrationCameraParamete 
3.3.1StepCalibrationMethod
3.3.2CalibrationAlgorithmBasedonMorethanOneFreePlane
3.3.3Non-linearDistortionParameterCalibrationMethod
Exercises
Chapter4VisualPerceptionIdentificationAlgorithms
4.1ImageFeatureExtractionandIdentificationAlgorithm
4.1.1DecisionTheoryApproach
4.1.2StatisticalClassificationMethod
4.1.3FeatureClassificationDiscretionSimilarityabouttheImageRecognitionProcess
4.2PrincipalComponentAnalysis
4.2.1PrincipalComponentAnalysisPrinciple
4.2.2KernelPrincipalComponentAnalysis
4.2.3PCA-basedImageRecognition
4.3SupportVectorMachines
4.3.1MainContentsofStatisticalLearningTheory
4.3.2Classification-SupportVectorMachine~
4.3.3SolutiontotheNonlinearRegressionProblem
4.3.4AlgorithmofSupportVectorMachine
4.3.5ImageCharacteristicsIdentificationBasedonSVM
4.4MomentInvariantsandNormalizedMomentsofInertia
4.4.1MomentTheory
4.4.2NormalizedMomentofInertia
4.5TemplateMatchingandSimilarity
4.5.1SpatialDomainDescriptionofTemplateMatching
4.5.2FrequencyDomainDescriptionofTemplateMatching
4.6ObjectRecognitionBasedonColorFeature
4.6.1ImageColorimetricProcessing
4.6.2Co tructionofColor-Pool
4.6.3ObjectRecognitionBasedonColor
4.7ImageFuzzyRecognitionMethod
4.7.1FuzzyContentFeatureandFuzzySimilarityDegree
4.7.2ExtractionofFuzzyStructure
4.7.3FuzzySynthesisDecision-makingofImageMatching
Exercises
Chapter5DetectionPrincipleofVisualPerception
5.1SingleViewGeometryandDetectionPrincipleofMonocularVisualPerception
5.1.1SingleVisionCoordinateSystem
5.1.2BasicAlgorithmforSingleVisionDetection
5.1.3EngineeringTechnologyBasedonSingleViewGeometry
5.2DetectionPrincipleofBinocularVisualPerception
5.2.1Two-viewGeometryandDetectionofBinocularPerception
5.2.2EpipolarGeometryPrinciple
5.2.3DeterminationMethodofSpatialCoordinates
5.2.4CameraCalibrationinBinocularVisualPerceptionSystem
5.3TheoreticalBasisforMultipleVisualPerceptionDetection
5.3.1Te orGeometryPrinciple
5.3.2GeometricPropertiesofThreeVisualTe or
5.3.3OperationofThree-visualTe or
5.3.4Co traintMatchingFeaturePointsofThree-visualTe or
5.3.5Three-visualTe orRestricttheThreeVisualRestraintFeatureLine'sMatching
Exercises
Applicationarticle
Chapter6PracticalTechnologyofIntelligentVisualPerception
6.1AutomaticMonitoringSystemandMethodofLoadLimitationofTheBridge
6.1.1TheBasicCompositionofTheSystem
6.1.2SystemAlgorithm
6.2IntelligentIdentificationSystemforBilletNumber
6.2.1SystemControlProgram
6.2.2RecognitionAlgorithm
6.3VerificationofBanknotes-SortingBasedonImageInformation
6.3.1PreprocessingoftheBanknotesImage
6.3.2DistinctionBetweenOldandNewBanknotes
6.3.3DistinctionoftheDenominationandDirectionoftheBanknotes
6.3.4BanknotesFinenessDetection
6.4IntelligentCollisionAvoidanceTechnologyofVehicle
6.4.1BasicHardwareConfiguration
6.4.2RoadObstacleRecognitionAlgorithm
6.4.3SmartAlgorithmofAnti-collisiontoPedestria 
6.5IntelligentVisualPerceptionControlofTrafficLights
6.5.1Overview
6.5.2TheCoreAlgorithmofIntelligentVisualPerceptionControlofTrafficLights
Exercises
Appendix
LeastSquareandCommonAlgorithmsinVisualPerceptionDetection
I.1BasicIdeaoftheAlgorithm
I.2CommonLeastSquareAlgorithmsinVisualPerceptionDetection
I.2.1LeastSquareofLinearSystemofEquatio 
I.2.2LeastSquareSolutionofNonlinearHomogeneousSystemofEquatio TheoryandMethodofBAYESDecision
II.1Introduction
II.2BAYESClassificationDecisionMode
II.2.1BAYESClassificationofMinimumErrorRate
II.2.2BAYESClassificationDecisionofMinimumRisk
IIIStatisticalLearningandVC-dime ionTheorem
III.1BoundingTheoryandVC-dime ionPrinciple
III.2GeneralizedCapabilityBounding
III.3StructuralRiskMinimizationPrincipleofInduction
IVOptimalityConditio onCo trainedNonlinearProgrammingProblem
IV.1Kuhn-TuckerCondition
IV.1.1GordonLemma
IV.1.2FritzJohnTheorem
IV.1.3ProofoftheKuhn-TuckerCondition
IV.2Karush-Kuhn-TuckerCondition
SubjectIndex
References