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| class | itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CandidateVector |
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| class | itk::Statistics::ChiSquareDistribution |
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| class | itk::Statistics::VectorContainerToListSampleAdaptor< TVectorContainer >::ConstIterator |
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| class | itk::Statistics::PointSetToListSampleAdaptor< TPointSet >::ConstIterator |
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| class | itk::Statistics::ListSample< TMeasurementVector >::ConstIterator |
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| class | itk::Statistics::JointDomainImageToListSampleAdaptor< TImage >::ConstIterator |
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| class | itk::Statistics::ImageToNeighborhoodSampleAdaptor< TImage, TBoundaryCondition >::ConstIterator |
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| class | itk::Statistics::ImageToListSampleAdaptor< TImage >::ConstIterator |
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| class | itk::Statistics::Histogram< TMeasurement, TFrequencyContainer >::ConstIterator |
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| class | itk::Statistics::CovarianceSampleFilter< TSample > |
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| class | itk::Statistics::DecisionRule |
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| class | itk::Statistics::DenseFrequencyContainer2 |
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| class | itk::Statistics::DistanceMetric< TVector > |
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| class | itk::Statistics::DistanceToCentroidMembershipFunction< TVector > |
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| class | itk::Statistics::EuclideanDistanceMetric< TVector > |
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| class | itk::Statistics::EuclideanSquareDistanceMetric< TVector > |
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| class | itk::Statistics::ExpectationMaximizationMixtureModelEstimator< TSample > |
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| class | itk::Statistics::ExpectationMaximizationMixtureModelEstimatorEnums |
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| class | itk::Statistics::GaussianDistribution |
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| class | itk::Statistics::GaussianMembershipFunction< TMeasurementVector > |
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| class | itk::Statistics::GaussianMixtureModelComponent< TSample > |
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| class | itk::Statistics::GaussianRandomSpatialNeighborSubsampler< TSample, TRegion > |
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| class | itk::Statistics::Histogram< TMeasurement, TFrequencyContainer > |
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| class | itk::HistogramToEntropyImageFilter< THistogram, TImage > |
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| class | itk::HistogramToImageFilter< THistogram, TImage, TFunction > |
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| class | itk::HistogramToIntensityImageFilter< THistogram, TImage > |
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| class | itk::HistogramToLogProbabilityImageFilter< THistogram, TImage > |
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| class | itk::HistogramToProbabilityImageFilter< THistogram, TImage > |
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| class | itk::Statistics::HistogramToRunLengthFeaturesFilter< THistogram > |
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| class | itk::Statistics::HistogramToRunLengthFeaturesFilterEnums |
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| class | itk::Statistics::HistogramToTextureFeaturesFilter< THistogram > |
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| class | itk::Statistics::HistogramToTextureFeaturesFilterEnums |
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| class | itk::Statistics::ImageClassifierFilter< TSample, TInputImage, TOutputImage > |
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| class | itk::Statistics::ImageJointDomainTraits< TImage > |
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| class | itk::Statistics::ImageToHistogramFilter< TImage > |
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| class | itk::Statistics::ImageToListSampleAdaptor< TImage > |
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| class | itk::Statistics::ImageToListSampleFilter< TImage, TMaskImage > |
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| class | itk::Statistics::ImageToNeighborhoodSampleAdaptor< TImage, TBoundaryCondition > |
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| class | itk::Statistics::VectorContainerToListSampleAdaptor< TVectorContainer >::Iterator |
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| class | itk::Statistics::PointSetToListSampleAdaptor< TPointSet >::Iterator |
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| class | itk::Statistics::ListSample< TMeasurementVector >::Iterator |
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| class | itk::Statistics::JointDomainImageToListSampleAdaptor< TImage >::Iterator |
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| class | itk::Statistics::ImageToNeighborhoodSampleAdaptor< TImage, TBoundaryCondition >::Iterator |
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| class | itk::Statistics::ImageToListSampleAdaptor< TImage >::Iterator |
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| class | itk::Statistics::Histogram< TMeasurement, TFrequencyContainer >::Iterator |
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| class | itk::Statistics::JointDomainImageToListSampleAdaptor< TImage > |
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| class | itk::KalmanLinearEstimator< T, VEstimatorDimension > |
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| class | itk::Statistics::KdTree< TSample > |
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| class | itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree > |
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| class | itk::Statistics::KdTreeGenerator< TSample > |
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| class | itk::Statistics::KdTreeNode< TSample > |
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| class | itk::Statistics::KdTreeNonterminalNode< TSample > |
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| class | itk::Statistics::KdTreeTerminalNode< TSample > |
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| class | itk::Statistics::KdTreeWeightedCentroidNonterminalNode< TSample > |
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| class | itk::Statistics::ListSample< TMeasurementVector > |
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| class | itk::Statistics::MahalanobisDistanceMembershipFunction< TVector > |
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| class | itk::Statistics::MahalanobisDistanceMetric< TVector > |
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| class | itk::Statistics::ManhattanDistanceMetric< TVector > |
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| class | itk::Statistics::MaskedImageToHistogramFilter< TImage, TMaskImage > |
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| class | itk::Statistics::MaximumDecisionRule |
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| class | itk::Statistics::MaximumRatioDecisionRule |
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| class | itk::Statistics::MeanSampleFilter< TSample > |
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| class | itk::Statistics::MeasurementVectorTraits |
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| class | itk::Statistics::MeasurementVectorTraitsTypes< TMeasurementVector > |
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| class | itk::Statistics::MembershipFunctionBase< TVector > |
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| class | itk::Statistics::MembershipSample< TSample > |
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| class | itk::Statistics::MinimumDecisionRule |
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| class | itk::Statistics::MixtureModelComponentBase< TSample > |
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| class | itk::Statistics::KdTree< TSample >::NearestNeighbors |
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| class | itk::Statistics::NeighborhoodSampler< TSample > |
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| class | itk::Statistics::NormalVariateGenerator |
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| class | itk::Statistics::PointSetToListSampleAdaptor< TPointSet > |
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| class | itk::Statistics::ProbabilityDistribution |
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| class | itk::Statistics::RegionConstrainedSubsampler< TSample, TRegion > |
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| class | RunLengthFeature |
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| class | itk::Statistics::Sample< TMeasurementVector > |
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| class | itk::Statistics::SampleClassifierFilter< TSample > |
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| class | itk::Statistics::SampleToHistogramFilter< TSample, THistogram > |
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| class | itk::Statistics::SampleToSubsampleFilter< TSample > |
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| class | itk::Statistics::ScalarImageToCooccurrenceListSampleFilter< TImage > |
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| class | itk::Statistics::ScalarImageToCooccurrenceMatrixFilter< TImageType, THistogramFrequencyContainer > |
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| class | itk::Statistics::ScalarImageToHistogramGenerator< TImageType > |
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| class | itk::Statistics::ScalarImageToRunLengthFeaturesFilter< TImageType, THistogramFrequencyContainer > |
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| class | itk::Statistics::ScalarImageToRunLengthMatrixFilter< TImageType, THistogramFrequencyContainer > |
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| class | itk::Statistics::ScalarImageToTextureFeaturesFilter< TImageType, THistogramFrequencyContainer > |
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| class | itk::Statistics::SparseFrequencyContainer2 |
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| class | itk::Statistics::SpatialNeighborSubsampler< TSample, TRegion > |
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| class | itk::Statistics::StandardDeviationPerComponentSampleFilter< TSample > |
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| class | itk::Statistics::Subsample< TSample > |
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| class | itk::Statistics::SubsamplerBase< TSample > |
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| class | itk::Statistics::TDistribution |
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| class | TERMINATION_CODE |
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| class | TextureFeature |
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| class | itk::Statistics::UniformRandomSpatialNeighborSubsampler< TSample, TRegion > |
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| class | itk::Statistics::VectorContainerToListSampleAdaptor< TVectorContainer > |
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| class | itk::Statistics::WeightedCentroidKdTreeGenerator< TSample > |
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| class | itk::Statistics::WeightedCovarianceSampleFilter< TSample > |
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| class | itk::Statistics::WeightedMeanSampleFilter< TSample > |
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The Statistics module contains basic data structures, statistical algorithms, and a classification for general statistical analysis and classification problems. This includes, for examples, classes for calculating histograms, calculating sample statistics, creating decision rules, or for performing statistical pattern classification. Statistics are calculated on a Sample, which contains MeasurementVector's.
Collaboration diagram for Module ITKStatistics:
1.8.16
