Classes | |
class | BackPropagationLayer |
class | BatchSupervisedTrainingFunction |
class | ChiSquareDistribution |
ChiSquareDistribution class defines the interface for a univariate Chi-Square distribution (pdfs, cdfs, etc.). More... | |
class | CompletelyConnectedWeightSet |
class | CovarianceCalculator |
Calculates the covariance matrix of the target sample data. More... | |
class | DenseFrequencyContainer |
his class is a container for frequencies of bins in an histogram. More... | |
class | DensityFunction |
DensityFunction class defines common interfaces for density functions. More... | |
class | DistanceMetric |
this class declares common interfaces for distance functions. More... | |
class | DistanceToCentroidMembershipFunction |
class represents DistanceToCentroid Density Function. More... | |
class | ErrorBackPropagationLearningFunctionBase |
class | ErrorBackPropagationLearningWithMomentum |
class | ErrorFunctionBase |
class | EuclideanDistance |
Euclidean distance function. More... | |
class | ExpectationMaximizationMixtureModelEstimator |
This class generates the parameter estimates for a mixture model using expectation maximization strategy. More... | |
class | GaussianDensityFunction |
GaussianDensityFunction class represents Gaussian Density Function. More... | |
class | GaussianDistribution |
GaussianDistribution class defines the interface for a univariate Gaussian distribution (pdfs, cdfs, etc.). More... | |
class | GaussianGoodnessOfFitComponent |
is a GoodnessOfFitComponent for Gaussian distribution. More... | |
class | GaussianMixtureModelComponent |
is a component (derived from MixtureModelComponentBase) for Gaussian class. This class is used in ExpectationMaximizationMixtureModelEstimator. More... | |
class | GaussianRadialBasisFunction |
class | GaussianTransferFunction |
struct | GetAdaptorMeasurementVectorLength |
struct | GetHistogramDimension |
class | GoodnessOfFitComponentBase |
provides component (module) type specific functionalities for GoodnessOfFitMixtureModelCostFunction. More... | |
class | GoodnessOfFitFunctionBase |
base class for classes calculates different types of goodness-of-fit statistics More... | |
class | GoodnessOfFitMixtureModelCostFunction |
calculates the goodness-of-fit statstics for multivarate mixture model More... | |
class | GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator |
This class computes texture feature coefficients from a grey level co-occurrence matrix. More... | |
class | HardLimitTransferFunction |
class | Histogram |
This class stores measurement vectors in the context of n-dimensional histogram. More... | |
class | HypersphereKernelMeanShiftModeSeeker |
Evolves the mode using a hyperspherical kernel defined by a radius (which is set by SetRadius) method. More... | |
class | IdentityTransferFunction |
class | ImageJointDomainTraits |
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 | ImageToCooccurrenceListAdaptor |
Converts pixel data into a list of pairs in order to compute a cooccurrence Histogram. More... | |
class | ImageToHistogramGenerator |
This class generates an histogram from an image. More... | |
class | ImageToListAdaptor |
This class provides ListSampleBase interfaces to ITK Image. More... | |
class | ImageToListGenerator |
The class takes an image as input and generates a list sample as output. More... | |
class | InputFunctionBase |
class | IterativeSupervisedTrainingFunction |
class | JointDomainImageToListAdaptor |
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 | KdTree |
This class provides methods for k-nearest neighbor search and related data structures for a k-d tree. More... | |
class | KdTreeBasedKmeansEstimator |
fast k-means algorithm implementation using k-d tree structure More... | |
class | KdTreeGenerator |
This class generates a KdTree object without centroid information. More... | |
class | KdTreeNode |
This class defines the interface of its derived classes. More... | |
class | KdTreeNonterminalNode |
This is a subclass of the KdTreeNode. More... | |
class | KdTreeTerminalNode |
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 | KdTreeWeightedCentroidNonterminalNode |
This is a subclass of the KdTreeNode. More... | |
class | LayerBase |
class | LearningFunctionBase |
class | ListSample |
This class is the native implementation of the ListSampleBase. More... | |
class | ListSampleBase |
This class is the base class for Samples that store measurements in a list. More... | |
class | ListSampleToHistogramFilter |
Imports data from ListSample object to Histogram object. More... | |
class | ListSampleToHistogramGenerator |
Generates a Histogram using the data from the ListSample object. More... | |
class | LogLikelihoodGoodnessOfFitFunction |
calculates loglikelihood ratio statistics More... | |
class | LogSigmoidTransferFunction |
class | MahalanobisDistanceMembershipFunction |
MahalanobisDistanceMembershipFunction class represents MahalanobisDistance Density Function. More... | |
class | MaskedScalarImageToGreyLevelCooccurrenceMatrixGenerator |
This class computes a grey-level co-occurence matrix (histogram) from a given image and mask. GLCM's are used for image texture description. More... | |
class | MeanCalculator |
calculates sample mean More... | |
class | MeanShiftModeCacheMethod |
This class stores mappings between a query point and its resulting mode point. More... | |
class | MeanShiftModeSeekerBase |
Evolves the mode. This is the base class for any mean shift mode seeking algorithm classes. More... | |
class | MeanSquaredErrorFunction |
class | MembershipFunctionBase |
MembershipFunctionBase class declares common interfaces for membership functions. More... | |
class | MembershipSample |
Container for storing the instance-identifiers of other sample with their associated class labels. More... | |
class | MembershipSampleGenerator |
MembershipSampleGenerator generates a MembershipSample object using a class mask sample. More... | |
class | MersenneTwisterRandomVariateGenerator |
MersenneTwisterRandom random variate generator. More... | |
class | MixtureModelComponentBase |
base class for distribution modules that supports analytical way to update the distribution parameters More... | |
class | MultilayerNeuralNetworkBase |
class | MultiquadricRadialBasisFunction |
class | NeighborhoodSampler |
generates a Subsample that is sampled from the input sample using a spherical kernel. More... | |
class | NeuralNetworkObject |
class | NNetDistanceMetricBase |
class | NormalVariateGenerator |
Normal random variate generator. More... | |
class | OneHiddenLayerBackPropagationNeuralNetwork |
class | PointSetToListAdaptor |
This class provides ListSampleBase interfaces to ITK PointSet. More... | |
class | ProbabilityDistribution |
ProbabilityDistribution class defines common interface for statistical distributions (pdfs, cdfs, etc.). More... | |
class | ProductInputFunction |
class | QuickPropLearningRule |
class | RadialBasisFunctionBase |
class | RandomVariateGeneratorBase |
this class defines common interfaces for random variate generators More... | |
class | RBFBackPropagationLearningFunction |
class | RBFLayer |
class | RBFNetwork |
class | Sample |
A collection of measurements for statistical analysis. More... | |
class | SampleAlgorithmBase |
This class is a base class for algorithms that operate on Sample data. The class is templated over the SampleType, which it takes as input using the SetInputSample() method. Derived classes that operate or calculate statistics on this input sample data and can access it using the GetInputSample() method. More... | |
class | SampleClassifier |
Integration point for MembershipCalculator, DecisionRule, and target sample data. More... | |
class | SampleClassifierWithMask |
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... | |
class | SampleMeanShiftBlurringFilter |
This filter blurs the input sample data using mean shift algorithm. More... | |
class | SampleMeanShiftClusteringFilter |
This filter create a cluster map from an input sample. More... | |
class | SampleSelectiveMeanShiftBlurringFilter |
This filter blurs the input sample data using mean shift algorithm selectively. More... | |
class | SampleToHistogramProjectionFilter |
projects measurement vectors on to an axis to generate an 1D histogram. More... | |
class | ScalarImageTextureCalculator |
This class computes texture descriptions from an image. More... | |
class | ScalarImageToGreyLevelCooccurrenceMatrixGenerator |
This class computes a grey-level co-occurence matrix (histogram) from a given image. GLCM's are used for image texture description. More... | |
class | ScalarImageToHistogramGenerator |
TODO. More... | |
class | ScalarImageToListAdaptor |
This class provides ListSampleBase interfaces to ITK Image. More... | |
class | SelectiveSubsampleGenerator |
SelectiveSubsampleGenerator generates a Subsample object that includes measurement vectors that belong to the classes that are specified by the SetSelectedClassLabels method. More... | |
class | SigmoidTransferFunction |
class | SignedHardLimitTransferFunction |
class | SparseFrequencyContainer |
his class is a container for an histogram. More... | |
class | SquaredDifferenceErrorFunction |
class | Subsample |
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 | SumInputFunction |
class | SymmetricSigmoidTransferFunction |
class | TanHTransferFunction |
class | TanSigmoidTransferFunction |
class | TDistribution |
TDistribution class defines the interface for a univariate Student-t distribution (pdfs, cdfs, etc.). More... | |
class | TrainingFunctionBase |
class | TransferFunctionBase |
class | TwoHiddenLayerBackPropagationNeuralNetwork |
class | VariableDimensionHistogram |
This class is similar to the Histogram class. It however allows you to specify the histogram dimension at run time. (and is therefore not templated over the size of a measurement vector). Users who know that the length of a measurement vector will be fixed, for instance joint statistics on pixel values of 2 images, (where the dimension will be 2), etc should use the Histogram class instead. More... | |
class | WeightedCentroidKdTreeGenerator |
This class generates a KdTree object with centroid information. More... | |
class | WeightedCovarianceCalculator |
Calculates the covariance matrix of the target sample data where each measurement vector has an associated weight value. More... | |
class | WeightedMeanCalculator |
calculates sample mean where each measurement vector has associated weight value More... | |
class | WeightSetBase |
Enumerations | |
enum | TextureFeatureName { Energy, Entropy, Correlation, InverseDifferenceMoment, Inertia, ClusterShade, ClusterProminence, HaralickCorrelation } |
Functions | |
template<class TSubsample> | |
void | DownHeap (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int node) |
template<class TSample> | |
void | FindSampleBound (const TSample *sample, typename TSample::ConstIterator begin, typename TSample::ConstIterator end, typename TSample::MeasurementVectorType &min, typename TSample::MeasurementVectorType &max) |
template<class TSubsample> | |
void | FindSampleBoundAndMean (const TSubsample *sample, int beginIndex, int endIndex, typename TSubsample::MeasurementVectorType &min, typename TSubsample::MeasurementVectorType &max, typename TSubsample::MeasurementVectorType &mean) |
template<class TSize> | |
TSize | FloorLog (TSize size) |
template<class TSubsample> | |
void | HeapSort (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex) |
template<class TSubsample> | |
void | InsertSort (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex) |
template<class TSubsample> | |
void | IntrospectiveSort (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int sizeThreshold) |
template<class TSubsample> | |
void | IntrospectiveSortLoop (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int depthLimit, int sizeThreshold) |
template<class TValue> | |
TValue | MedianOfThree (const TValue a, const TValue b, const TValue c) |
template<class TSubsample> | |
TSubsample::MeasurementType | NthElement (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int nth) |
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) |
template<class TSubsample> | |
TSubsample::MeasurementType | QuickSelect (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int kth, typename TSubsample::MeasurementType medianGuess) |
Texture feature types
Energy | |
Entropy | |
Correlation | |
InverseDifferenceMoment | |
Inertia | |
ClusterShade | |
ClusterProminence | |
HaralickCorrelation |
Definition at line 98 of file itkGreyLevelCooccurrenceMatrixTextureCoefficientsCalculator.h.
void itk::Statistics::DownHeap | ( | TSubsample * | sample, | |
unsigned int | activeDimension, | |||
int | beginIndex, | |||
int | endIndex, | |||
int | node | |||
) | [inline] |
void itk::Statistics::FindSampleBound | ( | const TSample * | sample, | |
typename TSample::ConstIterator | begin, | |||
typename TSample::ConstIterator | end, | |||
typename TSample::MeasurementVectorType & | min, | |||
typename TSample::MeasurementVectorType & | max | |||
) | [inline] |
void itk::Statistics::FindSampleBoundAndMean | ( | const TSubsample * | sample, | |
int | beginIndex, | |||
int | endIndex, | |||
typename TSubsample::MeasurementVectorType & | min, | |||
typename TSubsample::MeasurementVectorType & | max, | |||
typename TSubsample::MeasurementVectorType & | mean | |||
) | [inline] |
TSize itk::Statistics::FloorLog | ( | TSize | size | ) | [inline] |
void itk::Statistics::HeapSort | ( | TSubsample * | sample, | |
unsigned int | activeDimension, | |||
int | beginIndex, | |||
int | endIndex | |||
) | [inline] |
void itk::Statistics::InsertSort | ( | TSubsample * | sample, | |
unsigned int | activeDimension, | |||
int | beginIndex, | |||
int | endIndex | |||
) | [inline] |
void itk::Statistics::IntrospectiveSort | ( | TSubsample * | sample, | |
unsigned int | activeDimension, | |||
int | beginIndex, | |||
int | endIndex, | |||
int | sizeThreshold | |||
) | [inline] |
void itk::Statistics::IntrospectiveSortLoop | ( | TSubsample * | sample, | |
unsigned int | activeDimension, | |||
int | beginIndex, | |||
int | endIndex, | |||
int | depthLimit, | |||
int | sizeThreshold | |||
) | [inline] |
TValue itk::Statistics::MedianOfThree | ( | const TValue | a, | |
const TValue | b, | |||
const TValue | c | |||
) | [inline] |
TSubsample::MeasurementType itk::Statistics::NthElement | ( | TSubsample * | sample, | |
unsigned int | activeDimension, | |||
int | beginIndex, | |||
int | endIndex, | |||
int | nth | |||
) | [inline] |
NthElement is an algorithm for finding the n-th largest element of a list. In this case, only of the components of the measurement vectors is considered. This component is defined by the argument activeDimension. The search is rectricted to the range between the index begin and end, also passed as arguments. This algorithm was based on the procedure used in the STL nth_element method.
int itk::Statistics::Partition | ( | TSubsample * | sample, | |
unsigned int | activeDimension, | |||
int | beginIndex, | |||
int | endIndex, | |||
const typename TSubsample::MeasurementType | partitionValue | |||
) | [inline] |
The Partition algorithm performs partial sorting in a sample. Given a partitionValue, the algorithm moves to the beginning of the sample all MeasurementVectors whose component activeDimension is smaller than the partitionValue. In this way, the sample is partially sorted in two groups. First the group with activeDimension component smaller than the partitionValue, then the group of MeasurementVectors with activeDimension component larger than the partitionValue. The Partition algorithm takes as input a sample, and a range in that sample defined by [beginIndex,endIndex]. Only the activeDimension components of the MeasurementVectors in the sample will be considered by the algorithm. The Algorithm return an index in the range of [beginIndex,endIndex] pointing to the element with activeDimension component closest to the partitionValue.
TSubsample::MeasurementType itk::Statistics::QuickSelect | ( | TSubsample * | sample, | |
unsigned int | activeDimension, | |||
int | beginIndex, | |||
int | endIndex, | |||
int | kth | |||
) | [inline] |
QuickSelect is an algorithm for finding the k-th largest element of a list. In this case, only of the components of the measurement vectors is considered. This component is defined by the argument activeDimension. The search is rectricted to the range between the index begin and end, also passed as arguments. http://en.wikipedia.org/wiki/Selection_algorithm.
TSubsample::MeasurementType itk::Statistics::QuickSelect | ( | TSubsample * | sample, | |
unsigned int | activeDimension, | |||
int | beginIndex, | |||
int | endIndex, | |||
int | kth, | |||
typename TSubsample::MeasurementType | medianGuess | |||
) | [inline] |
QuickSelect is an algorithm for finding the k-th largest element of a list. In this case, only of the components of the measurement vectors is considered. This component is defined by the argument activeDimension. The search is rectricted to the range between the index begin and end, also passed as arguments. In this version, a guess value for the median index is provided in the argument medianGuess. The algoritm returns the value of the activeDimension component in the MeasurementVector located in the kth position. http://en.wikipedia.org/wiki/Selection_algorithm