ITK  5.1.0
Insight Toolkit
Classes
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::SampleToHistogramFilter< TSample, THistogram >
 
class  itk::Statistics::SampleToSubsampleFilter< TSample >
 
class  itk::Statistics::ScalarImageToCooccurrenceListSampleFilter< TImage >
 
class  itk::Statistics::ScalarImageToCooccurrenceMatrixFilter< TImageType, THistogramFrequencyContainer >
 
class  itk::Statistics::ScalarImageToHistogramGenerator< TImageType >
 
class  itk::Statistics::ScalarImageToRunLengthFeaturesFilter< TImageType, THistogramFrequencyContainer >
 
class  itk::Statistics::ScalarImageToRunLengthMatrixFilter< TImageType, THistogramFrequencyContainer >
 
class  itk::Statistics::ScalarImageToTextureFeaturesFilter< TImageType, THistogramFrequencyContainer >
 
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 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.

Dependencies: