[Statistics]

SoCooccurrenceQuantification2d engine provides texture indicators based on the co-occurrence matrix computation. More...

`#include <ImageViz/Engines/ImageAnalysis/Statistics/SoCooccurrenceQuantification2d.h>`

Inheritance diagram for SoCooccurrenceQuantification2d:

## Classes | |

class | SbCoocurrrenceDetail |

Results details of cooccurrence global measure. More... | |

## Public Member Functions | |

SoCooccurrenceQuantification2d () | |

## Public Attributes | |

SoSFImageDataAdapter | inImage |

SoSFImageDataAdapter | inMaskImage |

SoSFInt32 | offsetX |

SoSFInt32 | offsetY |

SoImageVizEngineAnalysisOutput < SbCoocurrrenceDetail > | outResult |

The SoCooccurrenceQuantification2d engine provides some information concerning the texture thanks to the computation of a co-occurrence matrix. This command allow s to classify, given a direction , pairs of pixels by their gray level. The co-occurrence matrix components are given by :

Where is the image graylevel for coordinates.

This formulation means that for a given pair , contains the number of pixels verifying and .

This matrix is made symmetric and normalized such as :

These operations allow to be independent to the image size and to hold properties on a direction and its symmetric. Thirteen indicators are computed from this matrix :

- an angular second moment indicator (ASM), also called uniformity. This indicator takes high values when image pixels present strong local uniformity.
- a contrast indicator (Con). This indicator takes high values for great gray level variations.
- a correlation indicator (Cor) which measures the dependency between gray levels and those of neighbouring pixels.
- a variance indicator (SSV) also called sum of squares. This indicator measures the dispersion of the combination of gray levels and their neighbour pixels around the mean.
- an inverse difference moment (IDM) also called homogeneity.
- a sum average indicator (SAv).
- a sum variance indicator (SVa).
- a sum entropy indicator (SEn).
- an entropy indicator (Ent).
- a difference variance indicator (DVa).
- a difference entropy indicator (DEn).
- two information measures of correlation (IC1 and IC2).

In addition this engine returns one more information in the result object which is the number of image pixels used for computation.

- Haralick R., Shanmugam K. & Dinstein I. "Textural features for image classification."

- CooccurrenceQuantification2d {

inImage | NULL |

inMaskImage | NULL |

offsetX | 1 |

offsetY | 0 |

Library references: cooccurrence

SoCooccurrenceQuantification2d::SoCooccurrenceQuantification2d | ( | ) |

Constructor.

The input image.

Default value is NULL. Supported types include: grayscale binary label color image.

The binary image for the mask or empty (command looks for only inside).

This parameter is optional. Default value is NULL. Supported types include: binary color image.

The X Offset.

Default value is 1.

The Y Offset.

Default value is 0.

The output measure result.

Default value is NULL.

The documentation for this class was generated from the following file:

- ImageViz/Engines/ImageAnalysis/Statistics/SoCooccurrenceQuantification2d.h

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