标题：脉冲耦合神经网络及应用
图书信息
出版社: 高等教育出版社; 第1版 (2010年6月1日)
外文书名: Applications of Pulse Coupled Neural Networks
精装: 199页
正文语种: 英语
开本: 16
ISBN: 9787040279788
条形码: 9787040279788
尺寸: 23.6 x 15.6 x 2 cm
重量: 499 g
内容简介
《脉冲耦合神经网络及应用(国内英文版)》内容简介：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.
目录
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