图象分析、随机场和动态蒙特卡罗方法（影印版）

　　This text is concerned with a probabilistic approach to image analysis as initiated by U. GRENANDER， D. and S. GEMAN， B.R. HUNT and many others， and developed and popularized by D. and S. GEMAN in a paper from 1984. It formally adopts the Bayesian paradigm and therefore is referred to as "Bayesian Image Analysis". There has been considerable and still growing interest in prior models and， in particular， in discrete Markov random field methods. Whereas image analysis is replete with ad hoc techniques， Bayesian image analysis provides a general framework encompassing various problems from imaging. Among those are such "classical" applications like restoration， edge detection， texture discrimination， motion analysis and tomographic reconstruction. The subject is rapidly developing and in the near future is likely to deal with high-level applications like object recognition. Fascinating experiments by Y. CHOW，U. GRENANDER and D.M. KEENAN（1987）， （1990） strongly support this belief.本书为英文版。

暂缺《图象分析、随机场和动态蒙特卡罗方法（影印版）》作者简介

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.
7.1.1TheLawofLargeNumbers
7.1.2ACounterexample
7.2AGeneralTheorem
7.3SamplingandAnnealingunderConstraints
7.3.1SimulatedAnnealing
7.3.2SimulatedAnnealingunderConstraints
7.3.3SamplingwithandwithoutConstraints
PartIII.MoreonSamplingandAnnealing
8.MetropolisAlgorithms
8.1TheMetropolisSampler
8.2ConvergenceTheorems
8.3BestConstants,
8.4AboutVisitingSchemes
8.4.1SystematicSweepStrategies
8.4.2TheInfluenceofProposalMatrices
8.5TheMetropolisAlgorithminConfi)inatorialOptimization
8.6GeneralizationsandModifications
8.6.1Metropolis-HastingsAlgorithms
8.6.2ThresholdRandomSearch
9.AlternativeApproaches
9.1SecondLargestEigenvalues
9.1.1ConvergenceReproved
9.1.2SamplingandSecondLargestEigenvalues
9.1.3ContinuousTimeandSpace
10.ParallelAlgorithms
10.1PartiallyParallelAlgorithms
10.1.1SynchroneousUpdatingonIndependentSets
10.1.2TheSwendson-WangAlgorithm
10.2SynchroneousAlgorithms
10.2.1Introduction
10.2.2InvariantDistributionsandConvergence
10.2.3SupportoftheLimitDistribution
10.3SynchroneousAlgorithmsandReversibility
10.3.1Preliminaries
10.3.2InvarianceandReversibility
10.3.3FinalRemarks
PartIV.TextureAnalysis
11.Partitioning
11.1Introduction
11.2HowtoTellTexturesApart
11.3Features
11.4BayesianTextureSegmentation
11.4.1TheFeatures
11.4.2TheKohnogorov-SmirnovDistance
11.4.3APartitionModel
11.4.4Optimization
11.4.5ABoundaryModel
11.5Julesz'sConjecture
11.5.1Introduction
11.5.2PointProcesses
12.TextureModelsandClassification
12.1Introduction
12.2TextureModels
12.2.1The-Model
12.2.2TheAntohinomialModel
12.2.3Automodels
12.3TextureSynthesis
12.4TextureClassification
12.4.1GeneralRemarks
12.4.2ContextualClassification
12.4.3MPMMethods
PartV.ParameterEstimation
13.MaximumLikelihoodEstimators
13.1Introduction
13.2TheLikelihoodFunction
13.3ObjectiveFunctions
13.4AsymptoticConsistency
14.SpacialMLEstimation
14.1Introduction
14.2IncreasingObservationWindows
14.3ThePseudolikelihoodMethod
14.4TheMaximumLikelihoodMethod
14.5ComputationofMLEstimators
14.6PartiallyObservedData
PartVI.Supplement
15.AGlanceatNeuralNetworks
15.1Introduction
15.2BoltzmannMachines
15.3ALearningRule
16.MixedApplications
16.1Motion
16.2TomographicImageReconstruction
16.3BiologicalShape
PartVII.Appendix
A.SimulationofRandomVariables
A.1Pseudo-randomNumbers
A.2DiscreteRandomVariables
A.3LocalGibbsSamplers
A.4FurtherDistributions
A.4.1BinomialVariables
A.4.2PoissonVariables
A.4.3GaussianVariables
A.4.4TheRejectionMethod
A.4.5ThePolarMethod
B.ThePerron-FrobeniusTheorem.
C.ConcaveFunctions
D.AGlobalConvergenceTheoremforDescentAlgorithms
References
Index