Package com.openinventor.imageviz.engines.geometryandmatching.patternrecognition

Class SoGrayscaleCorrelationProcessing2d

  • All Implemented Interfaces:
    SafeDisposable
    public class SoGrayscaleCorrelationProcessing2d
extends SoImageVizEngine
    SoGrayscaleCorrelationProcessing2d engine. The SoGrayscaleCorrelationProcessing2d image filter performs a correlation between a grey level image I and a grey level kernel K returning the correlation image O.

    See Correlation for generalities.

    Notations:

    The different possibilities are presented below using a 1-D correlation between an image and kernel. In the image, the kernel appears 6 times with different contrast and luminosity. Figure 1: 1D image and kernel The 6 examples show the kernel appearing with different contrast and luminosity. Figure 2: Example of 1D correlationsMultiply correlation

    The correlation is performed depending on SoGrayscaleCorrelationProcessing2d.CorrelationMode.

    For DIRECT

    In this mode, we have detected 3 of the 6 patterns matching the kernel. Only high luminosity patterns have been detected. The best matching is obtained for the high contrast and luminosity pattern. Figure 3: Example of Multiply 1D direct correlationFor MEAN

    In this mode, we have detected 2 of the 6 patterns matching the kernel. Only high contrast patterns have been detected. The confidence rate is the same for the two 2 patterns. Figure 4: Example of Multiply 1D mean correlationFor VARIANCE

    In this mode, we have detected the 2 patterns matching the kernel with the same confidence rate. Figure 5: Example of Multiply 1D variance correlationFor MEAN_VARIANCE

    In this mode, we have detected the 6 patterns matching the kernel with the same confidence rate. Figure 6: Example of Multiply 1D mean and variance correlationDifference correlation

    The correlation is performed depending on SoGrayscaleCorrelationProcessing2d.CorrelationMode.

    For DIRECT

    In this mode, we have detected 3 of the 6 patterns matching the kernel. Only similar luminosity patterns have been detected. The best matching is obtained with the similar contrast and luminosity pattern. Figure 7: Example of Difference 1D direct correlationFor MEAN

    In this mode, we have detected 2 of the 6 patterns matching the kernel. Only similar contrast patterns have been detected. The confidence rate is the same for the 2 patterns. Figure 8: Example of Difference 1D mean correlationFor VARIANCE

    In this mode, we have detected 3 of the 6 patterns matching the kernel. Only similar luminosity patterns have been detected. The confidence rate is the same for the 3 patterns. Figure 9: Example of Difference 1D variance correlationFor MEAN_VARIANCE

    In this mode, we have detected the 6 patterns matching the kernel with the same confidence rate. Figure 10: Example of Difference 1D mean and variance correlationSign Change correlation

    The correlation is performed depending on SoGrayscaleCorrelationProcessing2d.CorrelationMode.

    is the sign change criterion performed on the difference image. It corresponds to the number of sign changes calculated on every line. Figure 11: Example of sign correlation The object in the model and the object in the image have luminosity, contrast and noise differences. After normalization depending on the correlation type, the sign change criterion is applied on the difference image. The noise is supposed to be additive and zero mean. The statistical density function of the noise is supposed to be symmetrical. The best matching will correspond to the maximum number of sign changes.

    This correlation gives very good results with big kernels. It was created for medical applications where images are often very noisy.

    For DIRECT

    For MEAN

    For VARIANCE

    For MEAN_VARIANCE

    File format/default:

    GrayscaleCorrelationProcessing2d {

      correlationCriterion MULTIPLY
      inGrayImage NULL
      inKernelImage NULL
      offsetMode OFFSET_1
      correlationMode DIRECT
    }


    Library references: dcorrel mcorrel scorrel