Uses of Class
FeatureExtractor.Clustering.ClusteringFeatures

Packages that use ClusteringFeatures

Document.Windsurf	Contains the definition of classes modelling images and regions for Windsurf.
FeatureExtractor.Clustering	Contains the abstract classes for the segmentation of images into regions.
FeatureExtractor.Clustering.Windsurf	Contains classes specific for the Windsurf clustering algorithm.
FeatureExtractor.Filter	Contains the abstract classes for the filtering of images, to be performed in order to prepare each image to segmentation.

Uses of ClusteringFeatures in Document.Windsurf

Constructors in Document.Windsurf with parameters of type ClusteringFeatures

WindsurfBand(ClusteringFeatures c, double[][] m)
Creates a new band given a centroid and a covariance matrix.

Uses of ClusteringFeatures in FeatureExtractor.Clustering

Methods in FeatureExtractor.Clustering that return ClusteringFeatures

abstract ClusteringFeatures	ClusteringFeatures.clone() Creates a new ClusteringFeatures object as a copy of this object.
static ClusteringFeatures	ClusteringFeatures.computeCentroid(java.util.Vector<ClusteringFeatures> v) Computes the centroid of a vector of ClusteringFeatures.
abstract ClusteringFeatures	ClusteringFeatures.diff(ClusteringFeatures p) Subtracts the features of a given object to those of this object.
ClusteringFeatures	Pixel.getFeatures() Returns the features of this pixel.
ClusteringFeatures	Cluster.getRepresentativePoint() Returns the cluster representative point.
abstract ClusteringFeatures	ClusteringFeatures.normalize(double n) Normalizes the features of this object.
abstract ClusteringFeatures	ClusteringFeatures.sum(ClusteringFeatures p) Adds the features of this object to those of a given object.

Methods in FeatureExtractor.Clustering with parameters of type ClusteringFeatures

abstract ClusteringFeatures	ClusteringFeatures.diff(ClusteringFeatures p) Subtracts the features of a given object to those of this object.
abstract double	ClusteringDistance.distance(ClusteringFeatures p1, ClusteringFeatures p2) Computes the clustering distance between two points.
void	Cluster.setRepresentativePoint(ClusteringFeatures c) Modifies the cluster representative point.
abstract ClusteringFeatures	ClusteringFeatures.sum(ClusteringFeatures p) Adds the features of this object to those of a given object.

Method parameters in FeatureExtractor.Clustering with type arguments of type ClusteringFeatures

static ClusteringFeatures

ClusteringFeatures.computeCentroid(java.util.Vector<ClusteringFeatures> v) Computes the centroid of a vector of ClusteringFeatures.

Constructors in FeatureExtractor.Clustering with parameters of type ClusteringFeatures

Pixel(int x, int y, ClusteringFeatures features)
Basic constructor.

Uses of ClusteringFeatures in FeatureExtractor.Clustering.Windsurf

Subclasses of ClusteringFeatures in FeatureExtractor.Clustering.Windsurf

class

WindsurfClusteringFeatures Class representing the clustering features for Windsurf.

Methods in FeatureExtractor.Clustering.Windsurf that return ClusteringFeatures

ClusteringFeatures	WindsurfClusteringFeatures.clone() Creates a new WindsurfClusteringFeatures object as a copy of this object.
ClusteringFeatures	WindsurfClusteringFeatures.diff(ClusteringFeatures p) Subtracts the features of a given object to those of this object.
static ClusteringFeatures[][]	WindsurfKMeansClustering.getSubMatrix(ClusteringFeatures[][] matrix, boolean width, boolean height) Extracts a submatrix from a bi-dimensional ClusteringFeatures array.
ClusteringFeatures	WindsurfClusteringFeatures.normalize(double n) Normalizes the features of this object.
ClusteringFeatures	WindsurfClusteringFeatures.sum(ClusteringFeatures p) Adds the features of this object to those of a given object.

Methods in FeatureExtractor.Clustering.Windsurf with parameters of type ClusteringFeatures

static HSVImage	WindsurfClusteringFeatures.createHSV(ClusteringFeatures[][] data) Extracts the HSV features from a matrix of WindsurfClusteringFeatures.
ClusteringFeatures	WindsurfClusteringFeatures.diff(ClusteringFeatures p) Subtracts the features of a given object to those of this object.
double	Mahalanobis.distance(ClusteringFeatures p1, ClusteringFeatures p2) Computes the Mahalanobis distance between two points.
static ClusteringFeatures[][]	WindsurfKMeansClustering.getSubMatrix(ClusteringFeatures[][] matrix, boolean width, boolean height) Extracts a submatrix from a bi-dimensional ClusteringFeatures array.
ClusteringFeatures	WindsurfClusteringFeatures.sum(ClusteringFeatures p) Adds the features of this object to those of a given object.

Constructors in FeatureExtractor.Clustering.Windsurf with parameters of type ClusteringFeatures

Mahalanobis(ClusteringFeatures[][] data)
Basic constructor.

Uses of ClusteringFeatures in FeatureExtractor.Filter

Fields in FeatureExtractor.Filter declared as ClusteringFeatures

protected ClusteringFeatures[][]

GenericFilterOutput.datapoints Pixels of the image to be filtered

Methods in FeatureExtractor.Filter that return ClusteringFeatures

ClusteringFeatures[][]

GenericFilterOutput.getDataPoints() Returns the matrix of points obtained from the filter.