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

This group provides a package of pattern recognition functions based on standard correlation.

See: Description

Class Summary

Class	Description
SoBinaryCorrelationProcessing2d	SoBinaryCorrelationProcessing2d engine SoBinaryCorrelationProcessing2d performs the logical correlation between a binary image and a binary kernel.
SoBinaryCorrelationProcessing2d.SbCorrelationDetail	Results details of image correlation.
SoGrayscaleCorrelationProcessing2d	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.
SoGrayscaleCorrelationProcessing2d.SbCorrelationDetail	Results details of image correlation.

Enum Summary

Enum	Description
SoBinaryCorrelationProcessing2d.OffsetModes	See Correlation.
SoGrayscaleCorrelationProcessing2d.CorrelationCriterions	See Correlation.
SoGrayscaleCorrelationProcessing2d.CorrelationModes	See Correlation and for each SoGrayscaleCorrelationProcessing2d.CorrelationCriterion.
SoGrayscaleCorrelationProcessing2d.OffsetModes	This field is ignored in the multiply correlation mode.

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

This group provides a package of pattern recognition functions based on standard correlation. Some tools to ease the exploitation of results are also included.

Correlation

The correlation filters allow you to specify a correlation step.

The correlation filters allow the matching of rectangular or irregular patterns. Non-rectangular patterns are implemented with mask AOIs.

Use SoGrayscaleCorrelationProcessing2d for grayscale image correlation and SoBinaryCorrelationProcessing2d for binary image correlation.

SoGrayscaleCorrelationProcessing2d allows for local luminosity and / or contrast normalization. There are 4 different correlation types (see SoGrayscaleCorrelationProcessing2d.CorrelationMode) :

• DIRECT: Direct correlation (no normalization).
• MEAN: Mean normalized correlation (luminosity).
• VARIANCE: Variance normalized correlation (contrast).
• MEAN_VARIANCE: Mean and variance normalized correlation (luminosity and contrast).

We perform the correlation between a input image, , and a kernel, . The output image is an floating point image, . The correlation coefficient at location is given by a local calculation between the model and a pattern extraction from the input image, . The pattern location is and its dimension is . The actual calculation depends on image type:

• logical for SoBinaryCorrelationProcessing2d,
• multiplication for SoGrayscaleCorrelationProcessing2d with MULTIPLY ,
• difference SoGrayscaleCorrelationProcessing2d with SUBSTRACT or
• sign change criteria SoGrayscaleCorrelationProcessing2d with SIGNCHANGE .

It also depends on the correlation normalization TYPE as shown below. When a part of the pattern lies beyond the edge of the image the correlation is not performed on the image border. SCorrelation filters provide a step parameter (see SoGrayscaleCorrelationProcessing2d.OffsetMode and SoBinaryCorrelationProcessing2d.OffsetMode) which speeds up the operation by calculating 1 value out of each step, as shown in Figure 1.

Luminosity and Contrast normalization

The luminosity and contrast normalization is controlled by one of the 4 correlation types:

• DIRECT: Direct correlation (no normalization).
• MEAN: Mean normalized correlation (luminosity).
• VARIANCE: Variance normalized correlation (contrast).
• MEAN_VARIANCE: Mean and variance normalized correlation (luminosity and contrast).

  Output Image Normalization

  During the correlation the minimum and the maximum values are calculated . At the end of the filter processus, the correlation image is normalized between -1 and 1. The normalization depends on the following algorithm:

  Position of the Correlation Coefficients

  If the dimensions are odd, the position of the correlation coefficient is centerd in the pattern and corresponds to a pixel position. If the dimensions are even, the position of the correlation coefficient is the closest pixel position to the top and the left. Correlation with a Non-Rectangular Pattern

  If a binary image is attached as a mask to the kernel image , the correlation is made locally between and . The mean and variance calculation are made on and on . Output Data

  The correlation filters return a floating point correlation image. At the end of the processus, this correlation image is converted between -1 and 1 (worst and best matching detected). The non-calculated points are set to -3e38. Then, the SbCorrelationDetail contains:

• the position of the best matching (matchingPositionX, matchingPositionY),
• the minimum and the maximum values of the correlation image before the conversion (minComputed, maxComputed),
• the theoretical minimum and maximum correlation values before the conversion (minTheoretical, maxTheoretical).