数字图像处理:第三版 英文版

数字图像处理
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作者: [美] [美]
出版社: 电子工业出版社
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
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