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HaralickDescriptor Class |
Haralick's Texture Features.
Inheritance HierarchySystemObject
Accord.ImagingHaralickDescriptor
Accord.ImagingHaralickDescriptor
Namespace: Accord.Imaging
Assembly: Accord.Imaging (in Accord.Imaging.dll) Version: 3.8.0
SyntaxThe HaralickDescriptor type exposes the following members.
Constructors| Name | Description | |
|---|---|---|
 | HaralickDescriptor |
Initializes a new instance of the HaralickDescriptor class.
|
Properties| 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 Hy, the entropy of the
ColumnMarginal vector.
|
| ColumnMarginal |
Gets the marginal probability vector
obtained by summing the columns of p(i,j),
given as py(j) = Σi p(i,j).
|
| ColumnMean |
Gets μ_y, the mean value of the
ColumnMarginal vector.
|
| ColumnStandardDeviation |
Gets σy, 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(x-y) (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 co-occurrence 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 Hx, the entropy of the
RowMarginal vector.
|
| RowMarginal |
Gets the marginal probability vector
obtained by summing the rows of p(i,j),
given as px(i) = Σj p(i,j).
|
| RowMean |
Gets μx, the mean value of the
RowMarginal vector.
|
| RowStandardDeviation |
Gets σx, 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.
|
Methods| 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.) |
Extension Methods| 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.) |
RemarksHaralick's texture features are based on measures derived from Gray-level Co-occurrence matrices (GLCM).
Whether considering the intensity or grayscale values of the image or various dimensions of color, the co-occurrence matrix can measure the texture of the image. Because co-occurrence 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 lazy-evaluated until needed; but may also be combined in a single feature vector by calling GetVector(Int32).
References:
- Wikipedia Contributors, "Co-occurrence matrix". Available at http://en.wikipedia.org/wiki/Co-occurrence_matrix
- Robert M Haralick, K Shanmugam, Its'hak Dinstein; "Textural Features for Image Classification". IEEE Transactions on Systems, Man, and Cybernetics. SMC-3 (6): 610–621, 1973. Available at: http://www.makseq.com/materials/lib/Articles-Books/Filters/Texture/Co-occurrence/haralick73.pdf
ExamplesFor a complete example on how to use Haralick, please refer to the documentation of the main Haralick class.
See Also