《脈衝耦合神經網路及套用》是2010年6月1日高等教育出版社出版的圖書。本書主要對人工智慧、模式識別、電子工程等進行剖析。
基本介紹
- 書名：脈衝耦合神經網路及套用
- 又名：Applications of Pulse Coupled Neural Networks
- ISBN：9787040279788
- 頁數：199頁
- 出版社：高等教育出版社
- 出版時間：2010年6月1日
- 開本： 16
內容簡介,目錄,
內容簡介
《脈衝耦合神經網路及套用(國內英文版)》內容簡介：Applications of Pulse-Coupled Neural Networks explores the fields of image processing, including image filtering, image segmentation, image fusion, image coding, image retrieval, and biometric recognition, and the role of pulse-coupled neural networks in these fields.
This book is intended for researchers and graduate students in artificial intelligence, pattern recognition, electronic engineering, and computer science.
目錄
Chapter1 Pulse-CoupledNeuralNetworks
1.1LinkingFieldModel
1.2PCNN
1.3ModifiedPCNN
1.3.1IntersectionCorticalModel
1.3.2SpikingCorticalModel
1.3.3Multi-channelPCNN
Summary
References
Chapter2 ImageFiltering
2.1TraditionalFilters
2.1.1MeanFilter
2.1.2MedianFilter
2.1.3MorphologicalFilter
2.1.4WienerFilter
2.2ImpulseNoiseFiltering
2.2.1DescriptionofAlgorithmⅠ
2.2.2DescriptionofAlgorithmⅡ
2.2.3ExperimentalResultsandAnalysis
2.3GaussianNoiseFiltering
2.3.1PCNNNIandTimeMatrix
2.3.2DescriptionofAlgorithmⅢ
2.3.3ExperimentalResultsandAnalysis
Summary
References
Chapter3 ImageSegmentation
3.1TraditionalMethodsandEvaluationCriteria
3.1.1ImageSegmentationUsingArithmeticMean
3.1.2ImageSegmentationUsingEntropyandHistogram
3.1.3ImageSegmentationUsingMaximumBetween-clusterVariance
3.1.4ObjectiveEvaluationCriteria
3.2ImageSegmentationUsingPCNNandEntropy
3.3ImageSegmentationUsingSimplifiedPCNNandGA
3.3.1SimplifiedPCNNModel
3.3.2DesignofApplicationSchemeofGA
3.3.3FlowofAlgorithm
3.3.4ExperimentalResultsandAnalysis
Summary
References
Chapter4 ImageCoding
4.1IrregularSegmentedRegionCoding
4.1.1CodingofContoursUsingChainCode
4.1.2BasicTheoriesonOrthogonality
4.1.3OrthonormalizingProcessofBasisFunctions
4.1.4ISRCCodingandDecodingFramework
4.2IrregularSegmentedRegionCodingBasedonPCNN
4.2.1SegmentationMethod
4.2.2ExperimentalResultsandAnalysis
Summary
References
Chapter5 ImageEnhancement
5.1ImageEnhancement
5.1.1ImageEnhancementinSpatialDomain
5.1.2ImageEnhancementinFrequencyDomain
5.1.3HistogramEqualization
5.2PCNNTimeMatrix
5.2.1HumanVisualCharacteristics
5.2.2PCNNandHumanVisualCharacteristics
5.2.3PCNNTimeMatrix
5.3ModifiedPCNNModel
5.4ImageEnhancementUsingPCNNTimeMatrix
5.5ColorImageEnhancementUsingPCNN
Summary
References
Chapter6 ImageFusion
6.1PCNNandImageFusion
6.1.IPreliminaryofImageFusion
6.1.2ApplicationsinImageFusion
6.2MedicalImageFusion
6.2.1DescriptionofModel
6.2.2ImageFusionAlgorithm
6.2.3ExperimentalResultsandAnalysis
6.3Multi-focusImageFusion
6.3.1Dual-channelPCNN
6.3.2ImageSharpnessMeasure
6.3.3PrincipleofFusionAlgorithm
6.3.4ImplementationofMulti-focusImageFusion
6.3.5ExperimentalResultsandAnalysis
Summary
References
Chapter7 FeatureExtraction
7.1FeatureExtractionwithPCNN
7.1.1TimeSeries
7.1.2EntropySeries
7.1.3StatisticSeries
7.1.4OrthogonalTransform
7.2NoiseImageRecognition
7.2.1FeatureExtractionUsingPCNN
7.2.2ExperimentalResultsandAnalysis
7.3ImageRecognitionUsingBarycenterofHistogramVector
7.4InvariantTextureRetrieval
7.4.1TextureFeatureExtractionUsingPCNN
7.4.2ExperimentalResultsandAnalysis
7.5IrisRecognitionSystem
7.5.1IrisRecognition
7.5.2IrisFeatureExtractionUsingPCNN
7.5.3ExperimentalResultsandAnalysis
Summary
References
Chapter8 CombinatorialOptimization
8.1ModifiedPCNNBasedonAuto-wave
8.1.1Auto-waveNatureofPCNN
8.1.2Auto-waveNeuralNetwork
8.1.3TristateCascadingPulseCoupleNeuralNetwork
8.2TheShortestPathProblem
8.2.1AlgorithmforShortestPathProblemsBasedonTCPCNN
8.2.2ExperimentalResultsandAnalysis
8.3TravelingSalesmanProblem
8.3.1AlgorithmforOptimalProblemsBasedonAWNN
8.3.2ExperimentalResultsandAnalysis
Summary
References
Chapter9 FPGAImplementationofPCNNAlgorithm
9.1FndamentalPrincipleofPCNNHardwareImplementation
9.2AlteraDE2-70ImplementationofPCNN
9.2.1PCNNImplementationUsingAlteraDE2-70
9.2.2ExperimentalResultsandAnalysis
Summary
References
Index
