See: Description
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  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.

The correlation filters allow you to specify a correlation step.
The correlation filters allow the matching of rectangular or irregular patterns. Nonrectangular 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
) :
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:
SoBinaryCorrelationProcessing2d
,
SoGrayscaleCorrelationProcessing2d
with MULTIPLY
,
SoGrayscaleCorrelationProcessing2d
with SUBSTRACT
or
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.
The luminosity and contrast normalization is controlled by one of the 4 correlation types:
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:
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.
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 .
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 noncalculated points are set to 3e38. Then, the SbCorrelationDetail
contains:
matchingPositionX
, matchingPositionY
),
minComputed
, maxComputed
),
minTheoretical
, maxTheoretical
).
Generated on September 3, 2019, Copyright © Thermo Fisher Scientific. All rights reserved. http://www.openinventor.com