Package com.openinventor.imageviz.engines.imageanalysis.statistics

Class SoCooccurrenceQuantification2d

  • All Implemented Interfaces:
    SafeDisposable
    public class SoCooccurrenceQuantification2d
extends SoImageVizEngine
    SoCooccurrenceQuantification2d engine provides texture indicators based on the co-occurrence matrix computation. 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. where :
    • 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). where HX and HY are entropies of px and py such as : and

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

    References

    Haralick R., Shanmugam K. & Dinstein I. "Textural features for image classification." IEEE Transactions on Systems, Man, and Cybernetics. Vol 3, No 6, pp 610-621, October 1973.

    File format/default:

    CooccurrenceQuantification2d {

      inImage NULL
      inMaskImage NULL
      offsetX 1
      offsetY 0
    }


    Library references: cooccurrence

    • Constructor Detail

      • SoCooccurrenceQuantification2d

        public SoCooccurrenceQuantification2d()
        Constructor.