 |
ITK
4.1.0
Insight Segmentation and Registration Toolkit
|
|
ITK
4.1.0
Insight Segmentation and Registration Toolkit
|
Collaboration diagram for Module ITKStatistics:Classes | |
| class | itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CandidateVector |
| Candidate Vector. More... | |
| class | itk::Statistics::ChiSquareDistribution |
| ChiSquareDistribution class defines the interface for a univariate Chi-Square distribution (pdfs, cdfs, etc.). More... | |
| class | itk::Statistics::Histogram< TMeasurement, TFrequencyContainer >::ConstIterator |
| class that walks through the elements of the histogram. More... | |
| class | itk::Statistics::CovarianceSampleFilter< TSample > |
| Calculates the covariance matrix of the target sample data. More... | |
| class | itk::Statistics::DecisionRule |
| Base class for decision rules that return a class label based on a set of discriminant scores. More... | |
| class | itk::Statistics::DenseFrequencyContainer2 |
| This class is a container for frequencies of bins in an histogram. More... | |
| class | itk::Statistics::DistanceMetric< TVector > |
| this class declares common interfaces for distance functions. More... | |
| class | itk::Statistics::DistanceToCentroidMembershipFunction< TVector > |
| DistanceToCentroidMembershipFunction models class membership using a distance metric. More... | |
| class | itk::Statistics::EuclideanDistanceMetric< TVector > |
| Euclidean distance function. More... | |
| class | itk::Statistics::EuclideanSquareDistanceMetric< TVector > |
| Computes Euclidean distance between origin and given measurement vector. More... | |
| class | itk::Statistics::ExpectationMaximizationMixtureModelEstimator< TSample > |
| This class generates the parameter estimates for a mixture model using expectation maximization strategy. More... | |
| class | itk::Statistics::GaussianDistribution |
| GaussianDistribution class defines the interface for a univariate Gaussian distribution (pdfs, cdfs, etc.). More... | |
| class | itk::Statistics::GaussianMembershipFunction< TMeasurementVector > |
| GaussianMembershipFunction models class membership through a multivariate Gaussian function. More... | |
| class | itk::Statistics::GaussianMixtureModelComponent< TSample > |
| is a component (derived from MixtureModelComponentBase) for Gaussian class. This class is used in ExpectationMaximizationMixtureModelEstimator. More... | |
| class | itk::Statistics::Histogram< TMeasurement, TFrequencyContainer > |
| This class stores measurement vectors in the context of n-dimensional histogram. More... | |
| class | itk::HistogramToEntropyImageFilter< THistogram, TImage > |
| The class takes a histogram as an input and gives the entropy image as the output. A pixel, at position I, in the output image is given by. More... | |
| class | itk::HistogramToImageFilter< THistogram, TImage, TFunction > |
| This class takes a histogram as an input and returns an image of type specified by the functor. More... | |
| class | itk::HistogramToIntensityImageFilter< THistogram, TImage > |
| The class takes a histogram as an input and produces an image as the output. A pixel, at position I, in the output image is given by. More... | |
| class | itk::HistogramToLogProbabilityImageFilter< THistogram, TImage > |
| The class takes a histogram as an input and gives the log probability image as the output. A pixel, at position I, in the output image is given by. More... | |
| class | itk::HistogramToProbabilityImageFilter< THistogram, TImage > |
| The class takes a histogram as an input and gives the probability image as the output. A pixel, at position I, in the output image is given by. More... | |
| class | itk::Statistics::HistogramToRunLengthFeaturesFilter< THistogram > |
| This class computes texture feature coefficients from a grey level run-length matrix. More... | |
| class | itk::Statistics::HistogramToTextureFeaturesFilter< THistogram > |
| This class computes texture feature coefficients from a grey level co-occurrence matrix. More... | |
| class | itk::Statistics::ImageClassifierFilter< TSample, TInputImage, TOutputImage > |
| Image classification class. More... | |
| class | 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... | |
| class | itk::Statistics::ImageToHistogramFilter< TImage > |
| This class generates an histogram from an image. More... | |
| class | itk::Statistics::ImageToListSampleAdaptor< TImage > |
| This class provides ListSample interface to ITK Image. More... | |
| class | itk::Statistics::ImageToListSampleFilter< TImage, TMaskImage > |
| The class takes an image as input and generates a list sample as output. More... | |
| class | itk::Statistics::Histogram< TMeasurement, TFrequencyContainer >::Iterator |
| class that walks through the elements of the histogram. More... | |
| class | itk::Statistics::JointDomainImageToListSampleAdaptor< 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... | |
| class | itk::KalmanLinearEstimator< T, VEstimatorDimension > |
| Implement a linear recursive estimator. More... | |
| class | itk::Statistics::KdTree< TSample > |
| This class provides methods for k-nearest neighbor search and related data structures for a k-d tree. More... | |
| class | itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree > |
| fast k-means algorithm implementation using k-d tree structure More... | |
| class | itk::Statistics::KdTreeGenerator< TSample > |
| This class generates a KdTree object without centroid information. More... | |
| class | itk::Statistics::KdTreeNode< TSample > |
| This class defines the interface of its derived classes. More... | |
| class | itk::Statistics::KdTreeNonterminalNode< TSample > |
| This is a subclass of the KdTreeNode. More... | |
| class | 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... | |
| class | itk::Statistics::KdTreeWeightedCentroidNonterminalNode< TSample > |
| This is a subclass of the KdTreeNode. More... | |
| class | itk::Statistics::ListSample< TMeasurementVector > |
| This class is the native implementation of the a Sample with an STL container. More... | |
| class | itk::Statistics::MahalanobisDistanceMembershipFunction< TVector > |
| MahalanobisDistanceMembershipFunction models class membership using Mahalanobis distance. More... | |
| class | itk::Statistics::MahalanobisDistanceMetric< TVector > |
| MahalanobisDistanceMetric class computes a Mahalanobis distance given a mean and covariance. More... | |
| class | itk::Statistics::ManhattanDistanceMetric< TVector > |
| Euclidean distance function. More... | |
| class | itk::Statistics::MaskedImageToHistogramFilter< TImage, TMaskImage > |
| This class generates an histogram from an image. More... | |
| class | itk::Statistics::MaximumDecisionRule |
| A decision rule that returns the class label with the largest discriminant score. More... | |
| class | itk::Statistics::MaximumRatioDecisionRule |
| A decision rule that operates as a frequentist's approximation to Bayes rule. More... | |
| class | itk::Statistics::MeanSampleFilter< TSample > |
| Given a sample, this filter computes the sample mean. More... | |
| class | itk::Statistics::MeasurementVectorTraits |
| class | itk::Statistics::MeasurementVectorTraitsTypes< TMeasurementVector > |
| class | itk::Statistics::MembershipFunctionBase< TVector > |
| MembershipFunctionBase defines common interfaces for membership functions. More... | |
| class | itk::Statistics::MembershipSample< TSample > |
| Container for storing the instance-identifiers of other sample with their associated class labels. More... | |
| class | itk::Statistics::MinimumDecisionRule |
| A decision rule that returns the class label with the smallest discriminant score. More... | |
| class | itk::Statistics::MixtureModelComponentBase< TSample > |
| base class for distribution modules that supports analytical way to update the distribution parameters More... | |
| class | itk::Statistics::KdTree< TSample >::NearestNeighbors |
| data structure for storing k-nearest neighbor search result (k number of Neighbors) More... | |
| class | itk::Statistics::NeighborhoodSampler< TSample > |
| Generates a Subsample out of a Sample, based on a user-provided distance to a MeasurementVector. More... | |
| class | itk::Statistics::NormalVariateGenerator |
| Normal random variate generator. More... | |
| class | itk::Statistics::PointSetToListSampleAdaptor< TPointSet > |
| This class provides ListSample interface to ITK PointSet. More... | |
| class | itk::Statistics::ProbabilityDistribution |
| ProbabilityDistribution class defines common interface for statistical distributions (pdfs, cdfs, etc.). More... | |
| class | itk::Statistics::Sample< TMeasurementVector > |
| A collection of measurements for statistical analysis. More... | |
| class | itk::Statistics::SampleClassifierFilter< TSample > |
| Sample classification class. More... | |
| class | itk::Statistics::SampleToHistogramFilter< TSample, THistogram > |
| Computes the Histogram corresponding to a Sample. More... | |
| class | itk::Statistics::SampleToSubsampleFilter< TSample > |
| Base class of filters intended to select subsamples from samples. More... | |
| class | itk::Statistics::ScalarImageToCooccurrenceListSampleFilter< TImage > |
| Converts pixel data into a list of pairs in order to compute a cooccurrence Histogram. More... | |
| class | itk::Statistics::ScalarImageToCooccurrenceMatrixFilter< TImageType, THistogramFrequencyContainer > |
| This class computes a co-occurence matrix (histogram) from a given image and a mask image if provided. Coocurrence matrces are used for image texture description. More... | |
| class | itk::Statistics::ScalarImageToHistogramGenerator< TImageType > |
| TODO. More... | |
| class | itk::Statistics::ScalarImageToRunLengthFeaturesFilter< TImageType, THistogramFrequencyContainer > |
| This class computes run length descriptions from an image. More... | |
| class | itk::Statistics::ScalarImageToRunLengthMatrixFilter< TImageType, THistogramFrequencyContainer > |
| This class computes a run length matrix (histogram) from a given image and a mask image if provided. Run length matrces are used for image texture description. More... | |
| class | itk::Statistics::ScalarImageToTextureFeaturesFilter< TImageType, THistogramFrequencyContainer > |
| This class computes texture descriptions from an image. More... | |
| class | itk::Statistics::SparseFrequencyContainer2 |
| his class is a container for an histogram. More... | |
| class | itk::Statistics::StandardDeviationPerComponentSampleFilter< TSample > |
| Calculates the covariance matrix of the target sample data. More... | |
| class | itk::Statistics::Subsample< TSample > |
| This class stores a subset of instance identifiers from another sample object. You can create a subsample out of another sample object or another subsample object. The class is useful when storing or extracting a portion of a sample object. Note that when the elements of a subsample are sorted, the instance identifiers of the subsample are sorted without changing the original source sample. Most Statistics algorithms (that derive from StatisticsAlgorithmBase accept Subsample objects as inputs). More... | |
| class | itk::Statistics::TDistribution |
| TDistribution class defines the interface for a univariate Student-t distribution (pdfs, cdfs, etc.). More... | |
| class | itk::Statistics::VectorContainerToListSampleAdaptor< TVectorContainer > |
| This class provides ListSample interface to ITK VectorContainer. More... | |
| class | itk::Statistics::WeightedCentroidKdTreeGenerator< TSample > |
| This class generates a KdTree object with centroid information. More... | |
| class | itk::Statistics::WeightedCovarianceSampleFilter< TSample > |
| Calculates the covariance matrix of the target sample data. where each measurement vector has an associated weight value. More... | |
| class | itk::Statistics::WeightedMeanSampleFilter< TSample > |
| Given a sample where each measurement vector has associated weight value, this filter computes the sample mean. More... | |
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.
1.7.6.1
