SoGrayscaleCorrelationProcessing2d Class Reference
[Pattern Recognition]

ImageViz SoGrayscaleCorrelationProcessing2d engine More...

#include <ImageViz/Engines/GeometryAndMatching/PatternRecognition/SoGrayscaleCorrelationProcessing2d.h>

Inheritance diagram for SoGrayscaleCorrelationProcessing2d:
SoImageVizEngine SoEngine SoFieldContainer SoBase SoRefCounter SoTypedObject

List of all members.

Classes

class  SbCorrelationDetail
 Results details of image correlation. More...

Public Types

enum  CorrelationCriterion {
  SIGNCHANGE = 0,
  SUBSTRACT = 1,
  MULTIPLY = 2
}
enum  OffsetMode {
  OFFSET_1 = 0,
  OFFSET_2 = 1,
  OFFSET_4 = 2,
  OFFSET_8 = 3
}
enum  CorrelationMode {
  DIRECT = 0,
  MEAN = 1,
  VARIANCE = 2,
  MEAN_VARIANCE = 3
}

Public Member Functions

 SoGrayscaleCorrelationProcessing2d ()

Public Attributes

SoSFEnum correlationCriterion
SoSFImageDataAdapter inGrayImage
SoSFImageDataAdapter inKernelImage
SoSFEnum offsetMode
SoSFEnum correlationMode
SoImageVizEngineOutput
< SoSFImageDataAdapter,
SoImageDataAdapter * > 
outMatchingImage
SoImageVizEngineAnalysisOutput
< SbCorrelationDetail
outResult

Detailed Description

ImageViz 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:

\[Kmean=\frac{\sum\limits_{i=1}^{kx} \sum\limits_{j=1}^{ky} K(i,j)}{kx\times ky}\]

\[Imean(n,m)=\frac{\sum\limits_{i=1}^{kx} \sum\limits_{j=1}^{ky} I(n+i,m+j)}{kx\times ky}\]

\[Kvar=\sum_{i=1}^{kx} \sum_{j=1}^{ky} \left(K(i,j)-Kmean\right)^{2}\]

\[Ivar(n,m)=\sum_{i=1}^{kx} \sum_{j=1}^{ky} \left( I(n+i-\frac{kx}{2},m+j-\frac{ky}{2} ) - Imean(n,m)\right)^{2}\]

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.

SoCorrelationProcessing2d01.png

Figure 1: 1D image and kernel

The 6 examples show the kernel appearing with different contrast and luminosity.

SoCorrelationProcessing2d02.png

Figure 2: Example of 1D correlations

Multiply correlation

For DIRECT

For MEAN

For VARIANCE

For MEAN_VARIANCE

Difference correlation

For DIRECT

For MEAN

For VARIANCE

For MEAN_VARIANCE

Sign Change correlation

For DIRECT

For MEAN

For VARIANCE

For MEAN_VARIANCE

FILE FORMAT/DEFAULT


Library references: dcorrel mcorrel scorrel


Member Enumeration Documentation

See Correlation.

Enumerator:
SIGNCHANGE 

See Sign Change correlation.

SUBSTRACT 

See Difference correlation.

MULTIPLY 

See Multiply correlation.

See Correlation and for each SoGrayscaleCorrelationProcessing2d::CorrelationCriterion.

Enumerator:
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).

This field is ignored in the multiply correlation mode.

See Correlation

Enumerator:
OFFSET_1 

step of 1

OFFSET_2 

step of 2

OFFSET_4 

step of 4

OFFSET_8 

Constructor & Destructor Documentation

SoGrayscaleCorrelationProcessing2d::SoGrayscaleCorrelationProcessing2d (  ) 

Constructor.


Member Data Documentation

Select the correlation operator.

Use enum CorrelationCriterion. Default is MULTIPLY

Select the normalization mode for correlation.

Use enum CorrelationMode. Default is DIRECT

The input grayscale image.

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

The correlation kernel.

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

Select the calculation offset (number of pixels).

Use enum OffsetMode. Default is OFFSET_1

The output correlation image.

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

The correlation matching results.

Default value is NULL.


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

Open Inventor Toolkit reference manual, generated on 28 Oct 2019
Copyright © Thermo Fisher Scientific All rights reserved.
http://www.openinventor.com/