图像分析、随机场和动态蒙特卡罗方法

图书标准信息

• 作者 G.Winkler 著
• 出版社 世界图书出版公司
• 出版时间 1999-03
• 版次 1
• ISBN 9787506238250
• 定价 51.00元
• 装帧 平装
• 开本 24开
• 纸张 胶版纸
• 页数 324页
• 正文语种 英语

【内容简介】

　　ThistextisconcernedwithaprobabilisticapproachtoimageanalysisasinitiatedbyU.GRENANDER，D.andS.GEMAN，B.R.HUNTandmanyothers，anddevelopedandpopularizedbyD.andS.GEMANinapaperfrom1984.ItformallyadoptstheBayesianparadigmandthereforeisreferredtoas"BayesianImageAnalysis".
　　Therehasbeenconsiderableandstillgrowinginterestinpriormodelsand，inparticular，indiscreteMarkovrandomfieldmethods.Whereasimageanalysisisrepletewithadhoctechniques，Bayesianimageanalysisprovidesageneralframeworkencompassingvariousproblemsfromimaging.Amongthosearesuch"classical"applicationslikerestoration，edgedetection，texturediscrimination，motionanalysisandtomographicreconstruction.Thesubjectisrapidlydevelopingandinthenearfutureislikelytodealwithhigh-levelapplicationslikeobjectrecognition.FascinatingexperimentsbyY.CHOW，U.GRENANDERandD.M.KEENAN(1987)，(1990)stronglysupportthisbelief.

【目录】

Introduction
PartⅠ.BayesianImageAnalysis:Introduction
1.TheBayesianParadigm
1.1TheSpaceofImages
1.2TheSpaceofObservations
1.3PriorandPosteriorDistribution
1.4BayesianDecisionRules
2.CleaningDirtyPictures
2.1DistortionofImages
2.1.1PhysicalDigitalImagingSystems
2.1.2PosteriorDistributions
2.2Smoothing
2.3PiecewiseSmoothing
2.4BoundaryExtraction
3.RandomFields
3.1MarkovRandomFields
3.2GibbsFieldsandPotentials
3.3MoreonPotentials

PartⅡ.TheGibbsSamplerandSimulatedAnnealing
4.MarkovChains:LimitTheorems
4.1Preliminaries
4.2TheContractionCoefficient
4.3HomogeneousMarkovChains
4.4InhomogeneousMarkovChains
5.SamplingandAnnealing
5.1Sampling
5.2SimulatedAnnealing
5.3Discussion
6.CoolingSchedules
6.1TheICMAlgorithm
6.2ExactMAPEVersusFastCooling
6.3FiniteTimeAnnealing
7.SamplingandAnnealingRevisited
7.1ALawofLargeNumbersforInhomogeneousMarkovChains
7.2AGeneralTheoresm
7.3SamplingandAnnealingUnderConstraints

PartⅢ.MoreonSamplingandAnnealing
8.MetropolisAlgorithms
9.AlternativeApproaches
10.ParallelAlgorithms
PartⅣ.TextureAnalysis
11.Partitioning
12.TextureModelsandClassification
PartⅤ.ParameterEstimation
13.MaximumLikelihoodEstimators
14.SpacialMLEstimation
PartⅥ.Supplement
15.AGlanceatNeuralNetworks
16.MixedAPplications

PartⅦ.Appendix
A.SimulationofRandomVariables
B.ThePerron-FrobeniusTheorem
C.ConcaveFunctions
D.AGlobalConvergenceTheoremforDescentAlgorithms
References
Index