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PCTSignatures Class
http://www.emgu.com
Class implementing PCT (position-color-texture) signature extraction as described in: Martin Krulis, Jakub Lokoc, and Tomas Skopal. Efficient extraction of clustering-based feature signatures using GPU architectures. Multimedia Tools Appl., 75(13):8071–8103, 2016. The algorithm is divided to a feature sampler and a clusterizer. Feature sampler produces samples at given set of coordinates. Clusterizer then produces clusters of these samples using k-means algorithm. Resulting set of clusters is the signature of the input image. A signature is an array of SIGNATURE_DIMENSION-dimensional points.Used dimensions are: weight, x, y position; lab color, contrast, entropy.
Inheritance Hierarchy

Namespace: Emgu.CV.XFeatures2D
Assembly: Emgu.CV.World (in Emgu.CV.World.dll) Version: 3.2.0.2682 (3.2.0.2682)
Syntax
public class PCTSignatures : UnmanagedObject

The PCTSignatures type exposes the following members.

Constructors
  NameDescription
Public methodPCTSignatures(VectorOfPointF, VectorOfInt)
Creates PCTSignatures algorithm using pre-generated sampling points and clusterization seeds indexes.
Public methodPCTSignatures(VectorOfPointF, Int32)
Creates PCTSignatures algorithm using pre-generated sampling points and number of clusterization seeds. It uses the provided sampling points and generates its own clusterization seed indexes.
Public methodPCTSignatures(Int32, Int32, PCTSignaturesPointDistributionType)
Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.
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Properties
  NameDescription
Public propertyClusterMinSize
This parameter multiplied by the index of iteration gives lower limit for cluster size. Clusters containing fewer points than specified by the limit have their centroid dismissed and points are reassigned.
Public propertyDistanceFunction
Distance function selector used for measuring distance between two points in k-means.
Public propertyDropThreshold
Remove centroids in k-means whose weight is lesser or equal to given threshold.
Public propertyGrayscaleBits
Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used). The greyscale bitmap is used for computing contrast and entropy values.
Public propertyIterationCount
Number of iterations of the k-means clustering. We use fixed number of iterations, since the modified clustering is pruning clusters (not iteratively refining k clusters).
Public propertyJoiningDistance
Threshold euclidean distance between two centroids. If two cluster centers are closer than this distance, one of the centroid is dismissed and points are reassigned.
Public propertyMaxClustersCount
Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.
Public propertyPtr
Pointer to the unmanaged object
(Inherited from UnmanagedObject.)
Public propertyWeightA
Weights (multiplicative constants) that linearly stretch individual axes of the feature space. (x,y = position. L,a,b = color in CIE Lab space. c = contrast. e = entropy)
Public propertyWeightB
Weights (multiplicative constants) that linearly stretch individual axes of the feature space. (x,y = position. L,a,b = color in CIE Lab space. c = contrast. e = entropy)
Public propertyWeightEntropy
Weights (multiplicative constants) that linearly stretch individual axes of the feature space. (x,y = position. L,a,b = color in CIE Lab space. c = contrast. e = entropy)
Public propertyWeightL
Weights (multiplicative constants) that linearly stretch individual axes of the feature space. (x,y = position. L,a,b = color in CIE Lab space. c = contrast. e = entropy)
Public propertyWeightX
Weights (multiplicative constants) that linearly stretch individual axes of the feature space. (x,y = position. L,a,b = color in CIE Lab space. c = contrast. e = entropy)
Public propertyWeightY
Weights (multiplicative constants) that linearly stretch individual axes of the feature space. (x,y = position. L,a,b = color in CIE Lab space. c = contrast. e = entropy)
Public propertyWindowRadius
Size of the texture sampling window used to compute contrast and entropy. (center of the window is always in the pixel selected by x,y coordinates of the corresponding feature sample).
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Methods
  NameDescription
Public methodComputeSignature
Computes signature of given image.
Public methodDispose
The dispose function that implements IDisposable interface
(Inherited from DisposableObject.)
Protected methodDisposeObject
Release the unmanaged memory associated with this PCTSignatures object
(Overrides DisposableObjectDisposeObject.)
Public methodStatic memberDrawSignature
Draws signature in the source image and outputs the result. Signatures are visualized as a circle with radius based on signature weight and color based on signature color. Contrast and entropy are not visualized.
Public methodEquals (Inherited from Object.)
Protected methodFinalize
Destructor
(Inherited from DisposableObject.)
Public methodGetHashCode (Inherited from Object.)
Public methodGetType (Inherited from Object.)
Protected methodMemberwiseClone (Inherited from Object.)
Protected methodReleaseManagedResources
Release the managed resources. This function will be called during the disposal of the current object. override ride this function if you need to call the Dispose() function on any managed IDisposable object created by the current object
(Inherited from DisposableObject.)
Public methodToString (Inherited from Object.)
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Fields
  NameDescription
Protected field_ptr
A pointer to the unmanaged object
(Inherited from UnmanagedObject.)
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See Also