数字图像处理:第三版 英文版

数字图像处理:第三版 英文版
分享
扫描下方二维码分享到微信
打开微信,点击右上角”+“,
使用”扫一扫“即可将网页分享到朋友圈。
作者: [美] , [美]
2010-01
版次: 1
ISBN: 9787121102073
定价: 79.80
装帧: 平装
开本: 16开
纸张: 胶版纸
页数: 976页
字数: 1776千字
正文语种: 英语
原版书名: Digital Image Processing Third Edition
  •   《数字图像处理(第3版)(英文版)》是数字图像处理经典著作,作者在对32个国家的134个院校和研究所的教师、学生及自学者进行广泛调查的基础上编写了第三版。除保留了第二版的大部分主要内容外,还根据收集的建议从13个方面进行了修订,新增400多幅图像、200多个图表和80多道习题,同时融入了近年来本科学领域的重要发展,使《数字图像处理(第3版)(英文版)》具有相当的特色与先进性。全书分为12章,包括绪论、数字图像基础、灰度变换与空间滤波、频域滤波、图像复原与重建、彩色图像处理、小波及多分辨率处理、图像压缩、形态学图像处理、图像分割、表现与描述、目标识别。   RafaelC.Gonzalez,美国田纳西大学电气和计算机工程系教授,田纳西大学图像和模式分析实验室、机器人和计算机视觉实验室的创始人,IEEE会士。研究领域为模式识别、图像处理和机器人。其著作已在世界范围内500大学和研完所采用。
      RichardE.Woods,美国田纳西大学电气工程系获博士学位,IEEE会员。 Preface15
    Acknowledgments19
    TheBookWebSite20
    AbouttheAuthors21
    Chapter1Introduction23
    1.1WhatIsDigitalImageProcessing?23
    1.2TheOriginsofDigitalImageProcessing25
    1.3ExamplesofFieldsthatUseDigitalImageProcessing29
    1.3.1Gamma-RayImaging30
    1.3.2X-RayImaging31
    1.3.3ImagingintheUltravioletBand33
    1.3.4ImagingintheVisibleandInfraredBands34
    1.3.5ImagingintheMicrowaveBand40
    1.3.6ImagingintheRadioBand42
    1.3.7ExamplesinwhichOtherImagingModalitiesAreUsed42
    1.4FundamentalStepsinDigitalImageProcessing47
    1.5ComponentsofanImageProcessingSystem50
    Summary53
    ReferencesandFurtherReading53

    Chapter2DigitalImageFundamentals57
    2.1ElementsofVisualPerception58
    2.1.1StructureoftheHumanEye58
    2.1.2ImageFormationintheEye60
    2.1.3BrightnessAdaptationandDiscrimination61
    2.2LightandtheElectromagneticSpectrum65
    2.3ImageSensingandAcquisition68
    2.3.1ImageAcquisitionUsingaSingleSensor70
    2.3.2ImageAcquisitionUsingSensorStrips70
    2.3.3ImageAcquisitionUsingSensorArrays72
    2.3.4ASimpleImageFormationModel72
    2.4ImageSamplingandQuantization74
    2.4.1BasicConceptsinSamplingandQuantization74
    2.4.2RepresentingDigitalImages77
    2.4.3SpatialandIntensityResolution81
    2.4.4ImageInterpolation87
    2.5SomeBasicRelationshipsbetweenPixels90
    2.5.1NeighborsofaPixel90
    2.5.2Adjacency,Connectivity,Regions,andBoundaries90
    2.5.3DistanceMeasures93
    2.6AnIntroductiontotheMathematicalToolsUsedinDigitalImageProcessing94
    2.6.1ArrayversusMatrixOperations94
    2.6.2LinearversusNonlinearOperations95
    2.6.3ArithmeticOperations96
    2.6.4SetandLogicalOperations102
    2.6.5SpatialOperations107
    2.6.6VectorandMatrixOperations114
    2.6.7ImageTransforms115
    2.6.8ProbabilisticMethods118
    Summary120
    ReferencesandFurtherReading120
    Problems121

    Chapter3IntensityTransformationsandSpatialFiltering126
    3.1Background127
    3.1.1TheBasicsofIntensityTransformationsandSpatialFiltering127
    3.1.2AbouttheExamplesinThisChapter129
    3.2SomeBasicIntensityTransformationFunctions129
    3.2.1ImageNegatives130
    3.2.2LogTransformations131
    3.2.3Power-Law(Gamma)Transformations132
    3.2.4Piecewise-LinearTransformationFunctions137
    3.3HistogramProcessing142
    3.3.1HistogramEqualization144
    3.3.2HistogramMatching(Specification)150
    3.3.3LocalHistogramProcessing161
    3.3.4UsingHistogramStatisticsforImageEnhancement161
    3.4FundamentalsofSpatialFiltering166
    3.4.1TheMechanicsofSpatialFiltering167
    3.4.2SpatialCorrelationandConvolution168
    3.4.3VectorRepresentationofLinearFiltering172
    3.4.4GeneratingSpatialFilterMasks173
    3.5SmoothingSpatialFilters174
    3.5.1SmoothingLinearFilters174
    3.5.2Order-Statistic(Nonlinear)Filters178
    3.6SharpeningSpatialFilters179
    3.6.1Foundation180
    3.6.2UsingtheSecondDerivativeforImageSharpening-TheLaplacian182
    3.6.3UnsharpMaskingandHighboostFiltering184
    3.6.4UsingFirst-OrderDerivativesfor(Nonlinear)ImageSharpening—TheGradient187
    3.7CombiningSpatialEnhancementMethods191
    3.8UsingFuzzyTechniquesforIntensityTransformationsandSpatialFiltering195
    3.8.1Introduction195
    3.8.2PrinciplesofFuzzySetTheory196
    3.8.3UsingFuzzySets200
    3.8.4UsingFuzzySetsforIntensityTransformations208
    3.8.5UsingFuzzySetsforSpatialFiltering211
    Summary214
    ReferencesandFurtherReading214
    Problems215

    Chapter4FilteringintheFrequencyDomain221
    4.1Background222
    4.1.1ABriefHistoryoftheFourierSeriesandTransform222
    4.1.2AbouttheExamplesinthisChapter223
    4.2PreliminaryConcepts224
    4.2.1ComplexNumbers224
    4.2.2FourierSeries225
    4.2.3ImpulsesandTheirSiftingProperty225
    4.2.4TheFourierTransformofFunctionsofOneContinuousVariable227
    4.2.5Convolution231
    4.3SamplingandtheFourierTransformofSampledFunctions233
    4.3.1Sampling233
    4.3.2TheFourierTransformofSampledFunctions234
    4.3.3TheSamplingTheorem235
    4.3.4Aliasing239
    4.3.5FunctionReconstruction(Recovery)fromSampledData241
    4.4TheDiscreteFourierTransform(DFT)ofOneVariable242
    4.4.1ObtainingtheDFTfromtheContinuousTransformofaSampledFunction243
    4.4.2RelationshipBetweentheSamplingandFrequencyIntervals245
    4.5ExtensiontoFunctionsofTwoVariables247
    4.5.1The2-DImpulseandItsSiftingProperty247
    4.5.2The2-DContinuousFourierTransformPair248
    4.5.3Two-DimensionalSamplingandthe2-DSamplingTheorem249
    4.5.4AliasinginImages250
    4.5.5The2-DDiscreteFourierTransformandItsInverse257
    4.6SomePropertiesofthe2-DDiscreteFourierTransform258
    4.6.1RelationshipsBetweenSpatialandFrequencyIntervals258
    4.6.2TranslationandRotation258
    4.6.3Periodicity259
    4.6.4SymmetryProperties261
    4.6.5FourierSpectrumandPhaseAngle267
    4.6.6The2-DConvolutionTheorem271
    4.6.7Summaryof2-DDiscreteFourierTransformProperties275
    4.7TheBasicsofFilteringintheFrequencyDomain277
    4.7.1AdditionalCharacteristicsoftheFrequencyDomain277
    4.7.2FrequencyDomainFilteringFundamentals279
    4.7.3SummaryofStepsforFilteringintheFrequencyDomain285
    4.7.4CorrespondenceBetweenFilteringintheSpatialandFrequencyDomains285
    4.8ImageSmoothingUsingFrequencyDomainFilters291
    4.8.1IdealLowpassFilters291
    4.8.2ButterworthLowpassFilters295
    4.8.3GaussianLowpassFilters298
    4.8.4AdditionalExamplesofLowpassFiltering299
    4.9ImageSharpeningUsingFrequencyDomainFilters302
    4.9.1IdealHighpassFilters303
    4.9.2ButterworthHighpassFilters306
    4.9.3GaussianHighpassFilters307
    4.9.4TheLaplacianintheFrequencyDomain308
    4.9.5UnsharpMasking,HighboostFiltering,andHigh-Frequency-EmphasisFiltering310
    4.9.6HomomorphicFiltering311
    4.10SelectiveFiltering316
    4.10.1BandrejectandBandpassFilters316
    4.10.2NotchFilters316
    4.11Implementation320
    4.11.1Separabilityofthe2-DDFT320
    4.11.2ComputingtheIDFTUsingaDFTAlgorithm321
    4.11.3TheFastFourierTransform(FFT)321
    4.11.4SomeCommentsonFilterDesign325
    Summary325
    ReferencesandFurtherReading326
    Problems326

    Chapter5ImageRestorationandReconstruction333
    5.1AModeloftheImageDegradation/RestorationProcess334
    5.2NoiseModels335
    5.2.1SpatialandFrequencyPropertiesofNoise335
    5.2.2SomeImportantNoiseProbabilityDensityFunctions336
    5.2.3PeriodicNoise340
    5.2.4EstimationofNoiseParameters341
    5.3RestorationinthePresenceofNoiseOnly—SpatialFiltering344
    5.3.1MeanFilters344
    5.3.2Order-StatisticFilters347
    5.3.3AdaptiveFilters352
    5.4PeriodicNoiseReductionbyFrequencyDomainFiltering357
    5.4.1BandrejectFilters357
    5.4.2BandpassFilters358
    5.4.3NotchFilters359
    5.4.4OptimumNotchFiltering360
    5.5Linear,Position-InvariantDegradations365
    5.6EstimatingtheDegradationFunction368
    5.6.1EstimationbyImageObservation368
    5.6.2EstimationbyExperimentation369
    5.6.3EstimationbyModeling369
    5.7InverseFiltering373
    5.8MinimumMeanSquareError(Wiener)Filtering374
    5.9ConstrainedLeastSquaresFiltering379
    5.10GeometricMeanFilter383
    5.11ImageReconstructionfromProjections384
    5.11.1Introduction384
    5.11.2PrinciplesofComputedTomography(CT)387
    5.11.3ProjectionsandtheRadonTransform390
    5.11.4TheFourier-SliceTheorem396
    5.11.5ReconstructionUsingParallel-BeamFilteredBackprojections397
    5.11.6ReconstructionUsingFan-BeamFilteredBackprojections403
    Summary409
    ReferencesandFurtherReading410
    Problems411

    Chapter6ColorImageProcessing416
    6.1ColorFundamentals417
    6.2ColorModels423
    6.2.1TheRGBColorModel424
    6.2.2TheCMYandCMYKColorModels428
    6.2.3TheHSIColorModel429
    6.3PseudocolorImageProcessing436
    6.3.1IntensitySlicing437
    6.3.2IntensitytoColorTransformations440
    6.4BasicsofFull-ColorImageProcessing446
    6.5ColorTransformations448
    6.5.1Formulation448
    6.5.2ColorComplements452
    6.5.3ColorSlicing453
    6.5.4ToneandColorCorrections455
    6.5.5HistogramProcessing460
    6.6SmoothingandSharpening461
    6.6.1ColorImageSmoothing461
    6.6.2ColorImageSharpening464
    6.7ImageSegmentationBasedonColor465
    6.7.1SegmentationinHSIColorSpace465
    6.7.2SegmentationinRGBVectorSpace467
    6.7.3ColorEdgeDetection469
    6.8NoiseinColorImages473
    6.9ColorImageCompression476
    Summary477
    ReferencesandFurtherReading478
    Problems478

    Chapter7WaveletsandMultiresolutionProcessing483
    7.1Background484
    7.1.1ImagePyramids485
    7.1.2SubbandCoding488
    7.1.3TheHaarTransform496
    7.2MultiresolutionExpansions499
    7.2.1SeriesExpansions499
    7.2.2ScalingFunctions501
    7.2.3WaveletFunctions505
    7.3WaveletTransformsinOneDimension508
    7.3.1TheWaveletSeriesExpansions508
    7.3.2TheDiscreteWaveletTransform510
    7.3.3TheContinuousWaveletTransform513
    7.4TheFastWaveletTransform515
    7.5WaveletTransformsinTwoDimensions523
    7.6WaveletPackets532
    Summary542
    ReferencesandFurtherReading542
    Problems543

    Chapter8ImageCompression547
    8.1Fundamentals548
    8.1.1CodingRedundancy550
    8.1.2SpatialandTemporalRedundancy551
    8.1.3IrrelevantInformation552
    8.1.4MeasuringImageInformation553
    8.1.5FidelityCriteria556
    8.1.6ImageCompressionModels558
    8.1.7ImageFormats,Containers,andCompressionStandards560
    8.2SomeBasicCompressionMethods564
    8.2.1HuffmanCoding564
    8.2.2GolombCoding566
    8.2.3ArithmeticCoding570
    8.2.4LZWCoding573
    8.2.5Run-LengthCoding575
    8.2.6Symbol-BasedCoding581
    8.2.7Bit-PlaneCoding584
    8.2.8BlockTransformCoding588
    8.2.9PredictiveCoding606
    8.2.10WaveletCoding626
    8.3DigitalImageWatermarking636
    Summary643
    ReferencesandFurtherReading644
    Problems645

    Chapter9MorphologicalImageProcessing649
    9.1Preliminaries650
    9.2ErosionandDilation652
    9.2.1Erosion653
    9.2.2Dilation655
    9.2.3Duality657
    9.3OpeningandClosing657
    9.4TheHit-or-MissTransformation662
    9.5SomeBasicMorphologicalAlgorithms664
    9.5.1BoundaryExtraction664
    9.5.2HoleFilling665
    9.5.3ExtractionofConnectedComponents667
    9.5.4ConvexHull669
    9.5.5Thinning671
    9.5.6Thickening672
    9.5.7Skeletons673
    9.5.8Pruning676
    9.5.9MorphologicalReconstruction678
    9.5.10SummaryofMorphologicalOperationsonBinaryImages684
    9.6Gray-ScaleMorphology687
    9.6.1ErosionandDilation688
    9.6.2OpeningandClosing690
    9.6.3SomeBasicGray-ScaleMorphologicalAlgorithms692
    9.6.4Gray-ScaleMorphologicalReconstruction698
    Summary701
    ReferencesandFurtherReading701
    Problems702

    Chapter10ImageSegmentation711
    10.1Fundamentals712
    10.2Point,Line,andEdgeDetection714
    10.2.1Background714
    10.2.2DetectionofIsolatedPoints718
    10.2.3LineDetection719
    10.2.4EdgeModels722
    10.2.5BasicEdgeDetection728
    10.2.6MoreAdvancedTechniquesforEdgeDetection736
    10.2.7EdgeLinkingandBoundaryDetection747
    10.3Thresholding760
    10.3.1Foundation760
    10.3.2BasicGlobalThresholding763
    10.3.3OptimumGlobalThresholdingUsingOtsu’sMethod764
    10.3.4UsingImageSmoothingtoImproveGlobalThresholding769
    10.3.5UsingEdgestoImproveGlobalThresholding771
    10.3.6MultipleThresholds774
    10.3.7VariableThresholding778
    10.3.8MultivariableThresholding783
    10.4Region-BasedSegmentation785
    10.4.1RegionGrowing785
    10.4.2RegionSplittingandMerging788
    10.5SegmentationUsingMorphologicalWatersheds791
    10.5.1Background791
    10.5.2DamConstruction794
    10.5.3WatershedSegmentationAlgorithm796
    10.5.4TheUseofMarkers798
    10.6TheUseofMotioninSegmentation800
    10.6.1SpatialTechniques800
    10.6.2FrequencyDomainTechniques804
    Summary807
    ReferencesandFurtherReading807
    Problems809

    Chapter11RepresentationandDescription817
    11.1Representation818
    11.1.1Boundary(Border)Following818
    11.1.2ChainCodes820
    11.1.3PolygonalApproximationsUsingMinimum-PerimeterPolygons823
    11.1.4OtherPolygonalApproximationApproaches829
    11.1.5Signatures830
    11.1.6BoundarySegments832
    11.1.7Skeletons834
    11.2BoundaryDescriptors837
    11.2.1SomeSimpleDescriptors837
    11.2.2ShapeNumbers838
    11.2.3FourierDescriptors840
    11.2.4StatisticalMoments843
    11.3RegionalDescriptors844
    11.3.1SomeSimpleDescriptors844
    11.3.2TopologicalDescriptors845
    11.3.3Texture849
    11.3.4MomentInvariants861
    11.4UseofPrincipalComponentsforDescription864
    11.5RelationalDescriptors874
    Summary878
    ReferencesandFurtherReading878
    Problems879

    Chapter12ObjectRecognition883
    12.1PatternsandPatternClasses883
    12.2RecognitionBasedonDecision-TheoreticMethods888
    12.2.1Matching888
    12.2.2OptimumStatisticalClassifiers894
    12.2.3NeuralNetworks904
    12.3StructuralMethods925
    12.3.1MatchingShapeNumbers925
    12.3.2StringMatching926
    Summary928
    ReferencesandFurtherReading928
    Problems929
    AppendixA932
    Bibliography937
    Index965
  • 内容简介:
      《数字图像处理(第3版)(英文版)》是数字图像处理经典著作,作者在对32个国家的134个院校和研究所的教师、学生及自学者进行广泛调查的基础上编写了第三版。除保留了第二版的大部分主要内容外,还根据收集的建议从13个方面进行了修订,新增400多幅图像、200多个图表和80多道习题,同时融入了近年来本科学领域的重要发展,使《数字图像处理(第3版)(英文版)》具有相当的特色与先进性。全书分为12章,包括绪论、数字图像基础、灰度变换与空间滤波、频域滤波、图像复原与重建、彩色图像处理、小波及多分辨率处理、图像压缩、形态学图像处理、图像分割、表现与描述、目标识别。
  • 作者简介:
      RafaelC.Gonzalez,美国田纳西大学电气和计算机工程系教授,田纳西大学图像和模式分析实验室、机器人和计算机视觉实验室的创始人,IEEE会士。研究领域为模式识别、图像处理和机器人。其著作已在世界范围内500大学和研完所采用。
      RichardE.Woods,美国田纳西大学电气工程系获博士学位,IEEE会员。
  • 目录:
    Preface15
    Acknowledgments19
    TheBookWebSite20
    AbouttheAuthors21
    Chapter1Introduction23
    1.1WhatIsDigitalImageProcessing?23
    1.2TheOriginsofDigitalImageProcessing25
    1.3ExamplesofFieldsthatUseDigitalImageProcessing29
    1.3.1Gamma-RayImaging30
    1.3.2X-RayImaging31
    1.3.3ImagingintheUltravioletBand33
    1.3.4ImagingintheVisibleandInfraredBands34
    1.3.5ImagingintheMicrowaveBand40
    1.3.6ImagingintheRadioBand42
    1.3.7ExamplesinwhichOtherImagingModalitiesAreUsed42
    1.4FundamentalStepsinDigitalImageProcessing47
    1.5ComponentsofanImageProcessingSystem50
    Summary53
    ReferencesandFurtherReading53

    Chapter2DigitalImageFundamentals57
    2.1ElementsofVisualPerception58
    2.1.1StructureoftheHumanEye58
    2.1.2ImageFormationintheEye60
    2.1.3BrightnessAdaptationandDiscrimination61
    2.2LightandtheElectromagneticSpectrum65
    2.3ImageSensingandAcquisition68
    2.3.1ImageAcquisitionUsingaSingleSensor70
    2.3.2ImageAcquisitionUsingSensorStrips70
    2.3.3ImageAcquisitionUsingSensorArrays72
    2.3.4ASimpleImageFormationModel72
    2.4ImageSamplingandQuantization74
    2.4.1BasicConceptsinSamplingandQuantization74
    2.4.2RepresentingDigitalImages77
    2.4.3SpatialandIntensityResolution81
    2.4.4ImageInterpolation87
    2.5SomeBasicRelationshipsbetweenPixels90
    2.5.1NeighborsofaPixel90
    2.5.2Adjacency,Connectivity,Regions,andBoundaries90
    2.5.3DistanceMeasures93
    2.6AnIntroductiontotheMathematicalToolsUsedinDigitalImageProcessing94
    2.6.1ArrayversusMatrixOperations94
    2.6.2LinearversusNonlinearOperations95
    2.6.3ArithmeticOperations96
    2.6.4SetandLogicalOperations102
    2.6.5SpatialOperations107
    2.6.6VectorandMatrixOperations114
    2.6.7ImageTransforms115
    2.6.8ProbabilisticMethods118
    Summary120
    ReferencesandFurtherReading120
    Problems121

    Chapter3IntensityTransformationsandSpatialFiltering126
    3.1Background127
    3.1.1TheBasicsofIntensityTransformationsandSpatialFiltering127
    3.1.2AbouttheExamplesinThisChapter129
    3.2SomeBasicIntensityTransformationFunctions129
    3.2.1ImageNegatives130
    3.2.2LogTransformations131
    3.2.3Power-Law(Gamma)Transformations132
    3.2.4Piecewise-LinearTransformationFunctions137
    3.3HistogramProcessing142
    3.3.1HistogramEqualization144
    3.3.2HistogramMatching(Specification)150
    3.3.3LocalHistogramProcessing161
    3.3.4UsingHistogramStatisticsforImageEnhancement161
    3.4FundamentalsofSpatialFiltering166
    3.4.1TheMechanicsofSpatialFiltering167
    3.4.2SpatialCorrelationandConvolution168
    3.4.3VectorRepresentationofLinearFiltering172
    3.4.4GeneratingSpatialFilterMasks173
    3.5SmoothingSpatialFilters174
    3.5.1SmoothingLinearFilters174
    3.5.2Order-Statistic(Nonlinear)Filters178
    3.6SharpeningSpatialFilters179
    3.6.1Foundation180
    3.6.2UsingtheSecondDerivativeforImageSharpening-TheLaplacian182
    3.6.3UnsharpMaskingandHighboostFiltering184
    3.6.4UsingFirst-OrderDerivativesfor(Nonlinear)ImageSharpening—TheGradient187
    3.7CombiningSpatialEnhancementMethods191
    3.8UsingFuzzyTechniquesforIntensityTransformationsandSpatialFiltering195
    3.8.1Introduction195
    3.8.2PrinciplesofFuzzySetTheory196
    3.8.3UsingFuzzySets200
    3.8.4UsingFuzzySetsforIntensityTransformations208
    3.8.5UsingFuzzySetsforSpatialFiltering211
    Summary214
    ReferencesandFurtherReading214
    Problems215

    Chapter4FilteringintheFrequencyDomain221
    4.1Background222
    4.1.1ABriefHistoryoftheFourierSeriesandTransform222
    4.1.2AbouttheExamplesinthisChapter223
    4.2PreliminaryConcepts224
    4.2.1ComplexNumbers224
    4.2.2FourierSeries225
    4.2.3ImpulsesandTheirSiftingProperty225
    4.2.4TheFourierTransformofFunctionsofOneContinuousVariable227
    4.2.5Convolution231
    4.3SamplingandtheFourierTransformofSampledFunctions233
    4.3.1Sampling233
    4.3.2TheFourierTransformofSampledFunctions234
    4.3.3TheSamplingTheorem235
    4.3.4Aliasing239
    4.3.5FunctionReconstruction(Recovery)fromSampledData241
    4.4TheDiscreteFourierTransform(DFT)ofOneVariable242
    4.4.1ObtainingtheDFTfromtheContinuousTransformofaSampledFunction243
    4.4.2RelationshipBetweentheSamplingandFrequencyIntervals245
    4.5ExtensiontoFunctionsofTwoVariables247
    4.5.1The2-DImpulseandItsSiftingProperty247
    4.5.2The2-DContinuousFourierTransformPair248
    4.5.3Two-DimensionalSamplingandthe2-DSamplingTheorem249
    4.5.4AliasinginImages250
    4.5.5The2-DDiscreteFourierTransformandItsInverse257
    4.6SomePropertiesofthe2-DDiscreteFourierTransform258
    4.6.1RelationshipsBetweenSpatialandFrequencyIntervals258
    4.6.2TranslationandRotation258
    4.6.3Periodicity259
    4.6.4SymmetryProperties261
    4.6.5FourierSpectrumandPhaseAngle267
    4.6.6The2-DConvolutionTheorem271
    4.6.7Summaryof2-DDiscreteFourierTransformProperties275
    4.7TheBasicsofFilteringintheFrequencyDomain277
    4.7.1AdditionalCharacteristicsoftheFrequencyDomain277
    4.7.2FrequencyDomainFilteringFundamentals279
    4.7.3SummaryofStepsforFilteringintheFrequencyDomain285
    4.7.4CorrespondenceBetweenFilteringintheSpatialandFrequencyDomains285
    4.8ImageSmoothingUsingFrequencyDomainFilters291
    4.8.1IdealLowpassFilters291
    4.8.2ButterworthLowpassFilters295
    4.8.3GaussianLowpassFilters298
    4.8.4AdditionalExamplesofLowpassFiltering299
    4.9ImageSharpeningUsingFrequencyDomainFilters302
    4.9.1IdealHighpassFilters303
    4.9.2ButterworthHighpassFilters306
    4.9.3GaussianHighpassFilters307
    4.9.4TheLaplacianintheFrequencyDomain308
    4.9.5UnsharpMasking,HighboostFiltering,andHigh-Frequency-EmphasisFiltering310
    4.9.6HomomorphicFiltering311
    4.10SelectiveFiltering316
    4.10.1BandrejectandBandpassFilters316
    4.10.2NotchFilters316
    4.11Implementation320
    4.11.1Separabilityofthe2-DDFT320
    4.11.2ComputingtheIDFTUsingaDFTAlgorithm321
    4.11.3TheFastFourierTransform(FFT)321
    4.11.4SomeCommentsonFilterDesign325
    Summary325
    ReferencesandFurtherReading326
    Problems326

    Chapter5ImageRestorationandReconstruction333
    5.1AModeloftheImageDegradation/RestorationProcess334
    5.2NoiseModels335
    5.2.1SpatialandFrequencyPropertiesofNoise335
    5.2.2SomeImportantNoiseProbabilityDensityFunctions336
    5.2.3PeriodicNoise340
    5.2.4EstimationofNoiseParameters341
    5.3RestorationinthePresenceofNoiseOnly—SpatialFiltering344
    5.3.1MeanFilters344
    5.3.2Order-StatisticFilters347
    5.3.3AdaptiveFilters352
    5.4PeriodicNoiseReductionbyFrequencyDomainFiltering357
    5.4.1BandrejectFilters357
    5.4.2BandpassFilters358
    5.4.3NotchFilters359
    5.4.4OptimumNotchFiltering360
    5.5Linear,Position-InvariantDegradations365
    5.6EstimatingtheDegradationFunction368
    5.6.1EstimationbyImageObservation368
    5.6.2EstimationbyExperimentation369
    5.6.3EstimationbyModeling369
    5.7InverseFiltering373
    5.8MinimumMeanSquareError(Wiener)Filtering374
    5.9ConstrainedLeastSquaresFiltering379
    5.10GeometricMeanFilter383
    5.11ImageReconstructionfromProjections384
    5.11.1Introduction384
    5.11.2PrinciplesofComputedTomography(CT)387
    5.11.3ProjectionsandtheRadonTransform390
    5.11.4TheFourier-SliceTheorem396
    5.11.5ReconstructionUsingParallel-BeamFilteredBackprojections397
    5.11.6ReconstructionUsingFan-BeamFilteredBackprojections403
    Summary409
    ReferencesandFurtherReading410
    Problems411

    Chapter6ColorImageProcessing416
    6.1ColorFundamentals417
    6.2ColorModels423
    6.2.1TheRGBColorModel424
    6.2.2TheCMYandCMYKColorModels428
    6.2.3TheHSIColorModel429
    6.3PseudocolorImageProcessing436
    6.3.1IntensitySlicing437
    6.3.2IntensitytoColorTransformations440
    6.4BasicsofFull-ColorImageProcessing446
    6.5ColorTransformations448
    6.5.1Formulation448
    6.5.2ColorComplements452
    6.5.3ColorSlicing453
    6.5.4ToneandColorCorrections455
    6.5.5HistogramProcessing460
    6.6SmoothingandSharpening461
    6.6.1ColorImageSmoothing461
    6.6.2ColorImageSharpening464
    6.7ImageSegmentationBasedonColor465
    6.7.1SegmentationinHSIColorSpace465
    6.7.2SegmentationinRGBVectorSpace467
    6.7.3ColorEdgeDetection469
    6.8NoiseinColorImages473
    6.9ColorImageCompression476
    Summary477
    ReferencesandFurtherReading478
    Problems478

    Chapter7WaveletsandMultiresolutionProcessing483
    7.1Background484
    7.1.1ImagePyramids485
    7.1.2SubbandCoding488
    7.1.3TheHaarTransform496
    7.2MultiresolutionExpansions499
    7.2.1SeriesExpansions499
    7.2.2ScalingFunctions501
    7.2.3WaveletFunctions505
    7.3WaveletTransformsinOneDimension508
    7.3.1TheWaveletSeriesExpansions508
    7.3.2TheDiscreteWaveletTransform510
    7.3.3TheContinuousWaveletTransform513
    7.4TheFastWaveletTransform515
    7.5WaveletTransformsinTwoDimensions523
    7.6WaveletPackets532
    Summary542
    ReferencesandFurtherReading542
    Problems543

    Chapter8ImageCompression547
    8.1Fundamentals548
    8.1.1CodingRedundancy550
    8.1.2SpatialandTemporalRedundancy551
    8.1.3IrrelevantInformation552
    8.1.4MeasuringImageInformation553
    8.1.5FidelityCriteria556
    8.1.6ImageCompressionModels558
    8.1.7ImageFormats,Containers,andCompressionStandards560
    8.2SomeBasicCompressionMethods564
    8.2.1HuffmanCoding564
    8.2.2GolombCoding566
    8.2.3ArithmeticCoding570
    8.2.4LZWCoding573
    8.2.5Run-LengthCoding575
    8.2.6Symbol-BasedCoding581
    8.2.7Bit-PlaneCoding584
    8.2.8BlockTransformCoding588
    8.2.9PredictiveCoding606
    8.2.10WaveletCoding626
    8.3DigitalImageWatermarking636
    Summary643
    ReferencesandFurtherReading644
    Problems645

    Chapter9MorphologicalImageProcessing649
    9.1Preliminaries650
    9.2ErosionandDilation652
    9.2.1Erosion653
    9.2.2Dilation655
    9.2.3Duality657
    9.3OpeningandClosing657
    9.4TheHit-or-MissTransformation662
    9.5SomeBasicMorphologicalAlgorithms664
    9.5.1BoundaryExtraction664
    9.5.2HoleFilling665
    9.5.3ExtractionofConnectedComponents667
    9.5.4ConvexHull669
    9.5.5Thinning671
    9.5.6Thickening672
    9.5.7Skeletons673
    9.5.8Pruning676
    9.5.9MorphologicalReconstruction678
    9.5.10SummaryofMorphologicalOperationsonBinaryImages684
    9.6Gray-ScaleMorphology687
    9.6.1ErosionandDilation688
    9.6.2OpeningandClosing690
    9.6.3SomeBasicGray-ScaleMorphologicalAlgorithms692
    9.6.4Gray-ScaleMorphologicalReconstruction698
    Summary701
    ReferencesandFurtherReading701
    Problems702

    Chapter10ImageSegmentation711
    10.1Fundamentals712
    10.2Point,Line,andEdgeDetection714
    10.2.1Background714
    10.2.2DetectionofIsolatedPoints718
    10.2.3LineDetection719
    10.2.4EdgeModels722
    10.2.5BasicEdgeDetection728
    10.2.6MoreAdvancedTechniquesforEdgeDetection736
    10.2.7EdgeLinkingandBoundaryDetection747
    10.3Thresholding760
    10.3.1Foundation760
    10.3.2BasicGlobalThresholding763
    10.3.3OptimumGlobalThresholdingUsingOtsu’sMethod764
    10.3.4UsingImageSmoothingtoImproveGlobalThresholding769
    10.3.5UsingEdgestoImproveGlobalThresholding771
    10.3.6MultipleThresholds774
    10.3.7VariableThresholding778
    10.3.8MultivariableThresholding783
    10.4Region-BasedSegmentation785
    10.4.1RegionGrowing785
    10.4.2RegionSplittingandMerging788
    10.5SegmentationUsingMorphologicalWatersheds791
    10.5.1Background791
    10.5.2DamConstruction794
    10.5.3WatershedSegmentationAlgorithm796
    10.5.4TheUseofMarkers798
    10.6TheUseofMotioninSegmentation800
    10.6.1SpatialTechniques800
    10.6.2FrequencyDomainTechniques804
    Summary807
    ReferencesandFurtherReading807
    Problems809

    Chapter11RepresentationandDescription817
    11.1Representation818
    11.1.1Boundary(Border)Following818
    11.1.2ChainCodes820
    11.1.3PolygonalApproximationsUsingMinimum-PerimeterPolygons823
    11.1.4OtherPolygonalApproximationApproaches829
    11.1.5Signatures830
    11.1.6BoundarySegments832
    11.1.7Skeletons834
    11.2BoundaryDescriptors837
    11.2.1SomeSimpleDescriptors837
    11.2.2ShapeNumbers838
    11.2.3FourierDescriptors840
    11.2.4StatisticalMoments843
    11.3RegionalDescriptors844
    11.3.1SomeSimpleDescriptors844
    11.3.2TopologicalDescriptors845
    11.3.3Texture849
    11.3.4MomentInvariants861
    11.4UseofPrincipalComponentsforDescription864
    11.5RelationalDescriptors874
    Summary878
    ReferencesandFurtherReading878
    Problems879

    Chapter12ObjectRecognition883
    12.1PatternsandPatternClasses883
    12.2RecognitionBasedonDecision-TheoreticMethods888
    12.2.1Matching888
    12.2.2OptimumStatisticalClassifiers894
    12.2.3NeuralNetworks904
    12.3StructuralMethods925
    12.3.1MatchingShapeNumbers925
    12.3.2StringMatching926
    Summary928
    ReferencesandFurtherReading928
    Problems929
    AppendixA932
    Bibliography937
    Index965
查看详情
12
相关图书 / 更多
数字图像处理:第三版 英文版
数字影视后期制作(黄卓)(第二版)
黄卓、李晶晶 编
数字图像处理:第三版 英文版
数字货币与日常生活
李晶 著
数字图像处理:第三版 英文版
数字化转型百问(第一辑)
点亮智库·中信联数字化转型百问联合工作组 编
数字图像处理:第三版 英文版
数字媒体资产管理及版权开发研究
宋培义 著
数字图像处理:第三版 英文版
数字经济新动能与新就业
杨伟国
数字图像处理:第三版 英文版
数字图书馆多粒度集成知识服务理论与实现
王忠义
数字图像处理:第三版 英文版
数字化中台(用友数智化转型实践)(博文视点出品)
用友云平台团队 著
数字图像处理:第三版 英文版
数字逻辑习题解析与实验教程(第七版)
白中英、朱正东 著
数字图像处理:第三版 英文版
数字测图与GNSS测量综合实习
陈智勇、付建红、艾明耀 著
数字图像处理:第三版 英文版
数字电子电路分析与应用(第2版)(附微课视频)
谢永超
数字图像处理:第三版 英文版
数字法学论——原则、路径与架构
赵骏 著;赵骏、魏斌 编
数字图像处理:第三版 英文版
数字法治:数字经济时代的法律思维
高艳东、王莹、陆青、连斌 编
您可能感兴趣 / 更多
数字图像处理:第三版 英文版
启微·通往权力之路:康熙和他的继承人
[美]吴秀良(Silas H.L.Wu) 著;张震久、吴伯娅、董建中 译
数字图像处理:第三版 英文版
元分析:数据分析的共识方法与系统模式
[美]史蒂文·西姆斯克(Steven Simske) 著;倪泳鑫 潘微科 明仲 译
数字图像处理:第三版 英文版
哥伦比亚中国文学史(全8卷)(2版)
[美]梅维恒 编;马小悟、张治、刘文楠 译
数字图像处理:第三版 英文版
论弗洛伊德的《女性气质》—国际精神分析协会《当代弗洛伊德转折点与重要议题》系列
[美]格拉谢拉·阿贝林-萨斯·罗斯(Graciela Abelin-Sas Rose) 编;[阿根廷]利蒂西娅·格洛瑟·菲奥里尼(Leticia Glocer Fiorini)、闪小春 译
数字图像处理:第三版 英文版
新纪元科学:超自然及其捍卫者、揭露者与美国文化
[美]戴维·J.赫斯(David J.Hess) 著;王挺 编;郑念、潘涛 译
数字图像处理:第三版 英文版
爱书猪宝宝
[美]葛瑞格·皮佐利著 董欣佳 译
数字图像处理:第三版 英文版
探寻复杂问题中的关键X:公共卫生与医疗服务体系建模
[美]桑杰·巴苏(Sanjay Basu) 著;王力男、陈玉倩、徐嘉婕 译
数字图像处理:第三版 英文版
通过解题学习代数几何
[美]托马斯.嘉里蒂 著
数字图像处理:第三版 英文版
健康老年人的沟通与吞咽变化
[美]安吉拉·N.布尔达(Angela N.Burda) 著;曹宜璠、袁玉芹 译
数字图像处理:第三版 英文版
髋关节后方紊乱:临床评估与治疗
[美]哈尔·D.马丁(Hal D. Martin) (美)胡安·戈麦斯-霍约斯(Juan Gómez-Hoyos);李春宝
数字图像处理:第三版 英文版
时代广场的蟋蟀
[美]乔治·塞尔登 著;傅湘雯 译
数字图像处理:第三版 英文版
国际大奖儿童文学童年爱阅读系列:疯狂麦基
[美]杰瑞·史宾尼利 著