数字图像处理：第三版英文版

作者: [美] 冈萨雷斯著 , [美] 伍兹著

出版社: 电子工业出版社

出版时间: 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

分类: 教材教辅考试>教材>大学教材>计算机与互联网

　　《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