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ID:34151109
大小:7.85 MB
页数:137页
时间:2019-03-03
《Bayesian Approach to Image Interpretation.pdf》由会员上传分享,免费在线阅读,更多相关内容在学术论文-天天文库。
1、BayesianApproachtoImageInterpretationTHEKLUWERINTERNATIONALSERIESINENGINEERINGANDCOMPUTERSCIENCEBAYESIANAPPROACHTOIMAGEINTERPRETATIONSUNILK.KOPPARAPUResearchandDevelopmentGroupAquilaTechnologiesPrivateLimitedBangalore-560068.Indiakopparapu@bigfoot.comUDAYB.DESAISPANNLab.Departme
2、ntofElectricalEngineerinIndianInstituteofTechnology-BombayPowai,Mumbai400076.Indiaubdesai@ee.iitb.ernet.inKluwerAcademicPublishersNewYork/Boston/Dordrecht/London/MoscoweBookISBN:0-306-46996-0PrintISBN:0-792-37372-3©2002KluwerAcademicPublishersNewYork,Boston,Dordrecht,London,Mosc
3、owPrint©2000KluwerAcademic/PlenumPublishersNewYorkAllrightsreservedNopartofthiseBookmaybereproducedortransmittedinanyformorbyanymeans,electronic,mechanical,recording,orotherwise,withoutwrittenconsentfromthePublisherCreatedintheUnitedStatesofAmericaVisitKluwerOnlineat:http://kluw
4、eronline.comandKluwer'seBookstoreat:http://ebooks.kluweronline.comContentsListofFiguresixListofTablesxiPrefacexiiiAcknowledgmentsxv1.OVERVIEW11.Introduction11.1.ScopeofComputerVision22.ImageInterpretation23.LiteratureReview54.Approaches75.LayoutoftheMonograph92.BACKGROUND111.Int
5、roduction112.MarkovRandomFieldModels132.1.MRFModelandGibbsDistribution132.2.MaximumEntropyInterpretation172.3.Application:EstimatingImageAttribute182.4.PosteriorDistribution192.5.DetectingDiscontinuities:LineFieldModel222.6.Minimizingposteriorienergy233.Multiresolution243.1.Gaus
6、sianandLaplacianPyramid263.2.WaveletPyramid29viBAYESIANAPPROACHTOIMAGEINTERPRETATION3.MRFFRAMEWORKFORIMAGEINTERPRETATION351.MRFonaGraph351.1.MRFbasedFormulationforInterpretation371.2.SelectionofCliquesFunctions394.BAYESIANNETAPPROACHTOINTERPRETATION431.Introduction432.MRFmodelle
7、adingtoBayesianNetworkFormulation443.BayesianNetworksandProbabilisticInference453.1.Introduction463.2.DefinitionofaBayesianNetwork473.3.JointProbabilityDistribution473.4.PropertiesofBayesianNetworks484.ProbabilityUpdatinginBayesianNetworks494.1.TheDependenceSemanticsofBayesianNe
8、tworks494.2.DifferentSchemesforUpdatingProbabil
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