Module ITKStatistics

Collaboration diagram for Module ITKStatistics:

Classes
class	itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CandidateVector
class	itk::Statistics::ChiSquareDistribution
class	itk::Statistics::VectorContainerToListSampleAdaptor< TVectorContainer >::ConstIterator
class	itk::Statistics::PointSetToListSampleAdaptor< TPointSet >::ConstIterator
class	itk::Statistics::ListSample< TMeasurementVector >::ConstIterator
class	itk::Statistics::JointDomainImageToListSampleAdaptor< TImage >::ConstIterator
class	itk::Statistics::ImageToNeighborhoodSampleAdaptor< TImage, TBoundaryCondition >::ConstIterator
class	itk::Statistics::ImageToListSampleAdaptor< TImage >::ConstIterator
class	itk::Statistics::Histogram< TMeasurement, TFrequencyContainer >::ConstIterator
class	itk::Statistics::CovarianceSampleFilter< TSample >
class	itk::Statistics::DecisionRule
class	itk::Statistics::DenseFrequencyContainer2
class	itk::Statistics::DistanceMetric< TVector >
class	itk::Statistics::DistanceToCentroidMembershipFunction< TVector >
class	itk::Statistics::EuclideanDistanceMetric< TVector >
class	itk::Statistics::EuclideanSquareDistanceMetric< TVector >
class	itk::Statistics::ExpectationMaximizationMixtureModelEstimator< TSample >
class	itk::Statistics::ExpectationMaximizationMixtureModelEstimatorEnums
class	itk::Statistics::GaussianDistribution
class	itk::Statistics::GaussianMembershipFunction< TMeasurementVector >
class	itk::Statistics::GaussianMixtureModelComponent< TSample >
class	itk::Statistics::GaussianRandomSpatialNeighborSubsampler< TSample, TRegion >
class	itk::Statistics::Histogram< TMeasurement, TFrequencyContainer >
class	itk::HistogramToEntropyImageFilter< THistogram, TImage >
class	itk::HistogramToImageFilter< THistogram, TImage, TFunction >
class	itk::HistogramToIntensityImageFilter< THistogram, TImage >
class	itk::HistogramToLogProbabilityImageFilter< THistogram, TImage >
class	itk::HistogramToProbabilityImageFilter< THistogram, TImage >
class	itk::Statistics::HistogramToRunLengthFeaturesFilter< THistogram >
class	itk::Statistics::HistogramToRunLengthFeaturesFilterEnums
class	itk::Statistics::HistogramToTextureFeaturesFilter< THistogram >
class	itk::Statistics::HistogramToTextureFeaturesFilterEnums
class	itk::Statistics::ImageClassifierFilter< TSample, TInputImage, TOutputImage >
class	itk::Statistics::ImageJointDomainTraits< TImage >
class	itk::Statistics::ImageToHistogramFilter< TImage >
class	itk::Statistics::ImageToListSampleAdaptor< TImage >
class	itk::Statistics::ImageToListSampleFilter< TImage, TMaskImage >
class	itk::Statistics::ImageToNeighborhoodSampleAdaptor< TImage, TBoundaryCondition >
class	itk::Statistics::VectorContainerToListSampleAdaptor< TVectorContainer >::Iterator
class	itk::Statistics::PointSetToListSampleAdaptor< TPointSet >::Iterator
class	itk::Statistics::ListSample< TMeasurementVector >::Iterator
class	itk::Statistics::JointDomainImageToListSampleAdaptor< TImage >::Iterator
class	itk::Statistics::ImageToNeighborhoodSampleAdaptor< TImage, TBoundaryCondition >::Iterator
class	itk::Statistics::ImageToListSampleAdaptor< TImage >::Iterator
class	itk::Statistics::Histogram< TMeasurement, TFrequencyContainer >::Iterator
class	itk::Statistics::JointDomainImageToListSampleAdaptor< TImage >
class	itk::KalmanLinearEstimator< T, VEstimatorDimension >
class	itk::Statistics::KdTree< TSample >
class	itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >
class	itk::Statistics::KdTreeGenerator< TSample >
class	itk::Statistics::KdTreeNode< TSample >
class	itk::Statistics::KdTreeNonterminalNode< TSample >
class	itk::Statistics::KdTreeTerminalNode< TSample >
class	itk::Statistics::KdTreeWeightedCentroidNonterminalNode< TSample >
class	itk::Statistics::ListSample< TMeasurementVector >
class	itk::Statistics::MahalanobisDistanceMembershipFunction< TVector >
class	itk::Statistics::MahalanobisDistanceMetric< TVector >
class	itk::Statistics::ManhattanDistanceMetric< TVector >
class	itk::Statistics::MaskedImageToHistogramFilter< TImage, TMaskImage >
class	itk::Statistics::MaximumDecisionRule
class	itk::Statistics::MaximumRatioDecisionRule
class	itk::Statistics::MeanSampleFilter< TSample >
class	itk::Statistics::MeasurementVectorTraits
class	itk::Statistics::MeasurementVectorTraitsTypes< TMeasurementVector >
class	itk::Statistics::MembershipFunctionBase< TVector >
class	itk::Statistics::MembershipSample< TSample >
class	itk::Statistics::MinimumDecisionRule
class	itk::Statistics::MixtureModelComponentBase< TSample >
class	itk::Statistics::KdTree< TSample >::NearestNeighbors
class	itk::Statistics::NeighborhoodSampler< TSample >
class	itk::Statistics::NormalVariateGenerator
class	itk::Statistics::PointSetToListSampleAdaptor< TPointSet >
class	itk::Statistics::ProbabilityDistribution
class	itk::Statistics::RegionConstrainedSubsampler< TSample, TRegion >
class	RunLengthFeature
class	itk::Statistics::Sample< TMeasurementVector >
class	itk::Statistics::SampleClassifierFilter< TSample >
class	itk::Statistics::SampleToSubsampleFilter< TSample >
class	itk::Statistics::ScalarImageToCooccurrenceListSampleFilter< TImage >
class	itk::Statistics::ScalarImageToCooccurrenceMatrixFilter< TImageType, THistogramFrequencyContainer, TMaskImageType >
class	itk::Statistics::ScalarImageToHistogramGenerator< TImageType >
class	itk::Statistics::ScalarImageToRunLengthFeaturesFilter< TImageType, THistogramFrequencyContainer >
class	itk::Statistics::ScalarImageToRunLengthMatrixFilter< TImageType, THistogramFrequencyContainer >
class	itk::Statistics::ScalarImageToTextureFeaturesFilter< TImageType, THistogramFrequencyContainer, TMaskImageType >
class	itk::Statistics::SparseFrequencyContainer2
class	itk::Statistics::SpatialNeighborSubsampler< TSample, TRegion >
class	itk::Statistics::StandardDeviationPerComponentSampleFilter< TSample >
class	itk::Statistics::Subsample< TSample >
class	itk::Statistics::SubsamplerBase< TSample >
class	itk::Statistics::TDistribution
class	TERMINATION_CODE
class	TextureFeature
class	itk::Statistics::UniformRandomSpatialNeighborSubsampler< TSample, TRegion >
class	itk::Statistics::VectorContainerToListSampleAdaptor< TVectorContainer >
class	itk::Statistics::WeightedCentroidKdTreeGenerator< TSample >
class	itk::Statistics::WeightedCovarianceSampleFilter< TSample >
class	itk::Statistics::WeightedMeanSampleFilter< TSample >

Detailed Description

The Statistics module contains basic data structures, statistical algorithms, and a classification for general statistical analysis and classification problems. This includes, for example, classes for calculating histograms, calculating sample statistics, creating decision rules, or for performing statistical pattern classification. Statistics are calculated on an itk::Sample, which contains measurement vectors.

Dependencies:

• Module ITKCommon
• Module ITKNetlib