HaralickDescriptor Class 
Namespace: Accord.Imaging
The HaralickDescriptor type exposes the following members.
Name  Description  

HaralickDescriptor 
Initializes a new instance of the HaralickDescriptor class.

Name  Description  

AngularSecondMomentum 
Gets Haralick's first textural feature, the
Angular Second Momentum, also known as Energy
or Homogeneity.
 
ClusterProminence 
Gets the Cluster Prominence textural feature.
 
ClusterShade 
Gets the Cluster Shade textural feature.
 
ColumnEntropy 
Gets H_{y}, the entropy of the
ColumnMarginal vector.
 
ColumnMarginal 
Gets the marginal probability vector
obtained by summing the columns of p(i,j),
given as p_{y}(j) = Σ_{i} p(i,j).
 
ColumnMean 
Gets μ_y, the mean value of the
ColumnMarginal vector.
 
ColumnStandardDeviation 
Gets σ_{y}, the variance of the
ColumnMarginal vector.
 
Contrast 
Gets Haralick's second textural feature,
the Contrast.
 
Correlation 
Gets Haralick's third textural feature,
the Correlation.
 
DifferenceEntropy 
Gets Haralick's eleventh textural feature,
the Difference Entropy.
 
Differences 
Gets p_{(xy)} (k), the sum of elements
whose absolute indices diferences equals to k.
 
DifferenceVariance 
Gets Haralick's tenth textural feature,
the Difference Variance.
 
Entropy 
Gets Haralick's ninth textural feature,
the Entropy.
 
F01 
Gets Haralick's first textural feature,
the Angular Second Momentum.
 
F02 
Gets Haralick's second textural feature,
the Contrast.
 
F03 
Gets Haralick's third textural feature,
the Correlation.
 
F04 
Gets Haralick's fourth textural feature,
the Sum of Squares: Variance.
 
F05 
Gets Haralick's fifth textural feature,
the Inverse Difference Moment.
 
F06 
Gets Haralick's sixth textural feature,
the Sum Average.
 
F07 
Gets Haralick's seventh textural feature,
the Sum Variance.
 
F08 
Gets Haralick's eighth textural feature,
the Sum Entropy.
 
F09 
Gets Haralick's ninth textural feature,
the Entropy.
 
F10 
Gets Haralick's tenth textural feature,
the Difference Variance.
 
F11 
Gets Haralick's eleventh textural feature,
the Difference Entropy.
 
F12 
Gets Haralick's twelfth textural feature,
the First Information Measure.
 
F13 
Gets Haralick's thirteenth textural feature,
the Second Information Measure.
 
F14 
Gets Haralick's fourteenth textural feature,
the Maximal Correlation Coefficient.
 
FirstInformationMeasure 
Gets Haralick's twelfth textural feature,
the First Information Measure.
 
GrayLevels 
Gets the number of gray levels in the
original image. This is the number of
dimensions of the cooccurrence matrix.
 
InverseDifferenceMoment 
Gets Haralick's fifth textural feature, the Inverse
Difference Moment, also known as Local Homogeneity.
Can be regarded as a complement to Contrast.
 
LaplaceContrast 
Gets a variation of Haralick's second textural feature,
the Contrast with Absolute values (instead of squares).
 
MaximalCorrelationCoefficient 
Gets Haralick's fourteenth textural feature,
the Maximal Correlation Coefficient.
 
Mean 
Gets the matrix mean μ.
 
RowEntropy 
Gets H_{x}, the entropy of the
RowMarginal vector.
 
RowMarginal 
Gets the marginal probability vector
obtained by summing the rows of p(i,j),
given as p_{x}(i) = Σ_{j} p(i,j).
 
RowMean 
Gets μ_{x}, the mean value of the
RowMarginal vector.
 
RowStandardDeviation 
Gets σ_{x}, the variance of the
RowMarginal vector.
 
SecondInformationMeasure 
Gets Haralick's thirteenth textural feature,
the Second Information Measure.
 
Sum 
Gets the matrix sum.
 
SumAverage 
Gets Haralick's sixth textural feature,
the Sum Average.
 
SumEntropy 
Gets Haralick's eighth textural feature,
the Sum Entropy.
 
SumOfSquares 
Gets Haralick's fourth textural feature,
the Sum of Squares: Variance.
 
Sums 
Gets p_{(x+y)}(k), the sum
of elements whose indices sum to k.
 
SumVariance 
Gets Haralick's seventh textural feature,
the Sum Variance.
 
TextureHomogeneity 
Gets a variation of Haralick's fifth textural feature,
the Texture Homogeneity. Can be regarded as a complement
to LaplaceContrast.

Name  Description  

Equals  Determines whether the specified object is equal to the current object. (Inherited from Object.)  
Finalize  Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)  
GetHashCode  Serves as the default hash function. (Inherited from Object.)  
GetType  Gets the Type of the current instance. (Inherited from Object.)  
GetVector 
Creates a feature vector with
the chosen feature functions.
 
MemberwiseClone  Creates a shallow copy of the current Object. (Inherited from Object.)  
ToString  Returns a string that represents the current object. (Inherited from Object.) 
Name  Description  

HasMethod 
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)  
IsEqual 
Compares two objects for equality, performing an elementwise
comparison if the elements are vectors or matrices.
(Defined by Matrix.)  
To(Type)  Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)  
ToT  Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) 
Haralick's texture features are based on measures derived from Graylevel Cooccurrence matrices (GLCM).
Whether considering the intensity or grayscale values of the image or various dimensions of color, the cooccurrence matrix can measure the texture of the image. Because cooccurrence matrices are typically large and sparse, various metrics of the matrix are often taken to get a more useful set of features. Features generated using this technique are usually called Haralick features, after R. M. Haralick, attributed to his paper Textural features for image classification (1973).
This class encompasses most of the features derived on Haralick's original paper. All features are lazyevaluated until needed; but may also be combined in a single feature vector by calling GetVector(Int32).
References:
For a complete example on how to use Haralick, please refer to the documentation of the main Haralick class.