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Classes |
| class | itk::Statistics::CovarianceCalculator< TSample > |
| | Calculates the covariance matrix of the target sample data. More...
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| class | itk::Statistics::DenseFrequencyContainer< TFrequencyValue > |
| | his class is a container for frequencies of bins in an histogram. More...
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| class | itk::Statistics::DensityFunction< TMeasurementVector > |
| | DensityFunction class defines common interfaces for density functions. More...
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| class | itk::Statistics::DistanceMetric< TVector > |
| | this class declares common interfaces for distance functions. More...
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| class | itk::Statistics::DistanceToCentroidMembershipFunction< TVector > |
| | DistanceToCentroidMembershipFunction class represents DistanceToCentroid Density Function. More...
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| class | itk::Statistics::EuclideanDistance< TVector > |
| | Euclidean distance function. More...
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| class | itk::Statistics::ExpectationMaximizationMixtureModelEstimator< TSample > |
| | This class generates the parameter estimates for a mixture model using expectation maximization strategy. More...
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| class | itk::Statistics::GaussianDensityFunction< TMeasurementVector > |
| | GaussianDensityFunction class represents Gaussian Density Function. More...
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| class | itk::Statistics::GaussianGoodnessOfFitComponent< TInputSample > |
| | is a GoodnessOfFitComponent for Gaussian distribution. More...
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| class | itk::Statistics::GaussianMixtureModelComponent< TSample > |
| | is a component (derived from MixtureModelComponentBase) for Gaussian class. This class is used in ExpectationMaximizationMixtureModelEstimator. More...
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| class | itk::Statistics::GoodnessOfFitComponentBase< TInputSample > |
| | provides component (module) type specific functionalities for GoodnessOfFitMixtureModelCostFunction. More...
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| class | itk::Statistics::GoodnessOfFitFunctionBase< TInputHistogram > |
| | base class for classes calculates different types of goodness-of-fit statistics More...
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| class | itk::Statistics::GoodnessOfFitMixtureModelCostFunction< TInputSample > |
| | calculates the goodness-of-fit statstics for multivarate mixture model More...
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| class | itk::Statistics::Histogram< TMeasurement, VMeasurementVectorSize, TFrequencyContainer > |
| | This class stores measurement vectors in the context of n-dimensional histogram. More...
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| class | itk::Statistics::Histogram< TMeasurement, VMeasurementVectorSize, TFrequencyContainer >::Iterator |
| class | itk::Statistics::HypersphereKernelMeanShiftModeSeeker< TSample > |
| | Evolves the mode using a hyperspherical kernel defined by a radius (which is set by SetRadius) method. More...
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| class | itk::Statistics::ImageToListAdaptor< TImage, TMeasurementVector > |
| | This class provides ListSampleBase interfaces to ITK Image. More...
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| class | itk::Statistics::ImageToListAdaptor< TImage, TMeasurementVector >::Iterator |
| struct | itk::Statistics::ImageJointDomainTraits< TImage > |
| | This class provides the type defintion for the measurement vector in the joint domain (range domain -- pixel values + spatial domain -- pixel's physical coordinates). More...
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| class | itk::Statistics::JointDomainImageToListAdaptor< TImage > |
| | This adaptor returns measurement vectors composed of an image pixel's range domain value (pixel value) and spatial domain value (pixel's physical coordiantes). More...
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| struct | itk::Statistics::KdTreeNode< TSample > |
| | This class defines the interface of its derived classes. More...
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| struct | itk::Statistics::KdTreeNonterminalNode< TSample > |
| | This is a subclass of the KdTreeNode. More...
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| struct | itk::Statistics::KdTreeWeightedCentroidNonterminalNode< TSample > |
| | This is a subclass of the KdTreeNode. More...
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| struct | itk::Statistics::KdTreeTerminalNode< TSample > |
| | This class is the node that doesn't have any child node. The IsTerminal method returns true for this class. This class stores the instance identifiers belonging to this node, while the nonterminal nodes do not store them. The AddInstanceIdentifier and GetInstanceIdentifier are storing and retrieving the instance identifiers belonging to this node. More...
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| class | itk::Statistics::KdTree< TSample > |
| | This class provides methods for k-nearest neighbor search and related data structures for a k-d tree. More...
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| class | itk::Statistics::KdTree< TSample >::NearestNeighbors |
| | data structure for storing k-nearest neighbor search result (k number of Neighbors) More...
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| class | itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree > |
| | fast k-means algorithm implementation using k-d tree structure More...
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| class | itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CandidateVector |
| struct | itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CandidateVector::Candidate |
| class | itk::Statistics::KdTreeGenerator< TSample > |
| | This class generates a KdTree object without centroid information. More...
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| class | itk::Statistics::ListSample< TMeasurementVector > |
| | This class is the native implementation of the ListSampleBase. More...
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| class | itk::Statistics::ListSample< TMeasurementVector >::Iterator |
| class | itk::Statistics::ListSampleBase< TMeasurementVector > |
| | This class is the base class for containers that have a list of measurement vectors. More...
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| class | itk::Statistics::ListSampleToHistogramFilter< TListSample, THistogram > |
| | Imports data from ListSample object to Histogram object. More...
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| class | itk::Statistics::ListSampleToHistogramGenerator< TListSample, THistogramMeasurement, TFrequencyContainer > |
| | Generates a Histogram using the data from the ListSample object. More...
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| class | itk::Statistics::LogLikelihoodGoodnessOfFitFunction< TInputHistogram > |
| | calculates loglikelihood ratio statistics More...
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| class | itk::Statistics::MahalanobisDistanceMembershipFunction< TVector > |
| | MahalanobisDistanceMembershipFunction class represents MahalanobisDistance Density Function. More...
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| class | itk::Statistics::MeanCalculator< TSample > |
| | calculates sample mean More...
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| class | itk::Statistics::MeanShiftModeCacheMethod< TMeasurementVector > |
| | This class stores mappings between a query point and its resulting mode point. More...
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| struct | itk::Statistics::MeanShiftModeCacheMethod< TMeasurementVector >::LessMeasurementVector |
| class | itk::Statistics::MeanShiftModeSeekerBase< TSample > |
| | Evolves the mode. This is the base class for any mean shift mode seeking algorithm classes. More...
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| class | itk::Statistics::MembershipFunctionBase< TVector > |
| | MembershipFunctionBase class declares common interfaces for membership functions. More...
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| class | itk::Statistics::MembershipSample< TSample > |
| | Container for storing the instance-identifiers of other sample with their associated class labels. More...
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| class | itk::Statistics::MembershipSample< TSample >::Iterator |
| class | itk::Statistics::MembershipSampleGenerator< TInputSample, TClassMaskSample > |
| | MembershipSampleGenerator generates a MembershipSample object using a class mask sample. More...
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| class | itk::Statistics::MixtureModelComponentBase< TSample > |
| | base class for distribution modules that supports analytical way to update the distribution parameters More...
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| class | itk::Statistics::NeighborhoodSampler< TSample > |
| | generates a Subsample that is sampled from the input sample using a spherical kernel. More...
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| class | itk::Statistics::NormalVariateGenerator |
| | Normal random variate generator. More...
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| class | itk::Statistics::PointSetToListAdaptor< TPointSet > |
| | This class provides ListSampleBase interfaces to ITK PointSet. More...
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| class | itk::Statistics::PointSetToListAdaptor< TPointSet >::Iterator |
| class | itk::Statistics::RandomVariateGeneratorBase |
| | this class defines common interfaces for random variate generators More...
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| class | itk::Statistics::Sample< TMeasurementVector > |
| | Sample defines common iterfaces for each subclasses. More...
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| class | itk::Statistics::SampleAlgorithmBase< TInputSample > |
| | calculates sample mean More...
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| class | itk::Statistics::SampleClassifier< TSample > |
| | Integration point for MembershipCalculator, DecisionRule, and target sample data. More...
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| class | itk::Statistics::SampleClassifierWithMask< TSample, TMaskSample > |
| | Integration point for MembershipCalculator, DecisionRule, and target sample data. This class is functionally identical to the SampleClassifier, except that users can perform only part of the input sample that belongs to the subset of classes. More...
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| class | itk::Statistics::SampleMeanShiftBlurringFilter< TSample > |
| | This filter blurs the input sample data using mean shift algorithm. More...
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| class | itk::Statistics::SampleMeanShiftClusteringFilter< TSample > |
| | This filter create a cluster map from an input sample. More...
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| class | itk::Statistics::SampleSelectiveMeanShiftBlurringFilter< TSample > |
| | This filter blurs the input sample data using mean shift algorithm selectively. More...
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| class | itk::Statistics::SampleToHistogramProjectionFilter< TInputSample, THistogramMeasurement > |
| | projects measurement vectors on to an axis to generate an 1D histogram. More...
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| class | itk::Statistics::ScalarImageToListAdaptor< TImage > |
| | This class provides ListSampleBase interfaces to ITK Image. More...
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| class | itk::Statistics::SelectiveSubsampleGenerator< TInputSample, TClassMaskSample > |
| | SelectiveSubsampleGenerator generates a Subsample object that includes measurement vectors that belong to the classes that are specified by the SetSelectedClassLabels method. More...
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| class | itk::Statistics::SparseFrequencyContainer< TFrequencyValue > |
| | his class is a container for an histogram. More...
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| class | itk::Statistics::Subsample< TSample > |
| class | itk::Statistics::Subsample< TSample >::Iterator |
| class | itk::Statistics::WeightedCentroidKdTreeGenerator< TSample > |
| | This class generates a KdTree object with centroid information. More...
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| class | itk::Statistics::WeightedCovarianceCalculator< TSample > |
| | Calculates the covariance matrix of the target sample data where each measurement vector has an associated weight value. More...
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| class | itk::Statistics::WeightedMeanCalculator< TSample > |
| | calculates sample mean where each measurement vector has associated weight value More...
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Functions |
| template<class TSize> TSize | FloorLog (TSize size) |
| template<class TValue> TValue | MedianOfThree (const TValue a, const TValue b, const TValue c) |
| template<class TSample> void | FindSampleBound (TSample *sample, typename TSample::Iterator begin, typename TSample::Iterator end, typename TSample::MeasurementVectorType &min, typename TSample::MeasurementVectorType &max) |
| template<class TSubsample> void | FindSampleBoundAndMean (TSubsample *sample, int beginIndex, int endIndex, typename TSubsample::MeasurementVectorType &min, typename TSubsample::MeasurementVectorType &max, typename TSubsample::MeasurementVectorType &mean) |
| template<class TSubsample> int | Partition (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, const typename TSubsample::MeasurementType partitionValue) |
| template<class TSubsample> TSubsample::MeasurementType | QuickSelect (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int kth, typename TSubsample::MeasurementType medianGuess) |
| template<class TSubsample> TSubsample::MeasurementType | QuickSelect (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int kth) |
| template<class TSubsample> void | InsertSort (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex) |
| template<class TSubsample> void | DownHeap (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int node) |
| template<class TSubsample> void | HeapSort (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex) |
| template<class TSubsample> void | IntrospectiveSortLoop (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int depthLimit, int sizeThreshold) |
| template<class TSubsample> void | IntrospectiveSort (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int sizeThreshold) |
