RadialAutocorrelationProfile
Computes the radial autocorrelation function of a grayscale image from its center.
Access to parameter description
This algorithm calculates the radial autocorrelation function (two-point autocorrelation function) of the tomographic input image. The algorithm first computes the standard autocorrelation (that is, correlation for each offset in every direction). Then, mean and standard deviation are computed along circles in 2D (but over spherical shells in 3D) with center at 0 offset.
The output is a 3-column spreadsheet indicating, for each radius, the corresponding average intensity value and standard deviation.
See also
Access to parameter description
This algorithm calculates the radial autocorrelation function (two-point autocorrelation function) of the tomographic input image. The algorithm first computes the standard autocorrelation (that is, correlation for each offset in every direction). Then, mean and standard deviation are computed along circles in 2D (but over spherical shells in 3D) with center at 0 offset.
The output is a 3-column spreadsheet indicating, for each radius, the corresponding average intensity value and standard deviation.
See also
Function Syntax
This function returns outputMeasurement.
// Function prototype
RadialIntensityProfileMsr::Ptr radialAutocorrelationProfile( std::shared_ptr< iolink::ImageView > inputImage, RadialIntensityProfileMsr::Ptr outputMeasurement = NULL );
This function returns outputMeasurement.
// Function prototype. radial_autocorrelation_profile( input_image, output_measurement = None )
This function returns outputMeasurement.
// Function prototype. public static RadialIntensityProfileMsr RadialAutocorrelationProfile( IOLink.ImageView inputImage, RadialIntensityProfileMsr outputMeasurement = null );
Class Syntax
Parameters
| Class Name | RadialAutocorrelationProfile |
|---|
| Parameter Name | Description | Type | Supported Values | Default Value | |
|---|---|---|---|---|---|
 |
inputImage |
The input image. | Image | Binary or Grayscale | nullptr |
 |
outputMeasurement |
The output radial autocorrelation and its standard deviation result according to the distance from the image center. | RadialIntensityProfileMsr | nullptr | |
Object Examples
auto foam = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam.vip" ); RadialAutocorrelationProfile radialAutocorrelationProfileAlgo; radialAutocorrelationProfileAlgo.setInputImage( foam ); radialAutocorrelationProfileAlgo.execute(); std::cout << "mean: " << radialAutocorrelationProfileAlgo.outputMeasurement()->mean( 0 , 0 , 0 ) ;
foam = imagedev.read_vip_image(imagedev_data.get_image_path("foam.vip"))
radial_autocorrelation_profile_algo = imagedev.RadialAutocorrelationProfile()
radial_autocorrelation_profile_algo.input_image = foam
radial_autocorrelation_profile_algo.execute()
print( "mean: ", str( radial_autocorrelation_profile_algo.output_measurement.mean( 0 , 0 , 0 ) ) )
ImageView foam = Data.ReadVipImage( @"Data/images/foam.vip" );
RadialAutocorrelationProfile radialAutocorrelationProfileAlgo = new RadialAutocorrelationProfile
{
inputImage = foam
};
radialAutocorrelationProfileAlgo.Execute();
Console.WriteLine( "mean: " + radialAutocorrelationProfileAlgo.outputMeasurement.mean( 0 , 0 , 0 ) );
Function Examples
auto foam = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam.vip" ); auto result = radialAutocorrelationProfile( foam ); std::cout << "mean: " << result->mean( 0 , 0 , 0 ) ;
foam = imagedev.read_vip_image(imagedev_data.get_image_path("foam.vip"))
result = imagedev.radial_autocorrelation_profile( foam )
print( "mean: ", str( result.mean( 0 , 0 , 0 ) ) )
ImageView foam = Data.ReadVipImage( @"Data/images/foam.vip" ); RadialIntensityProfileMsr result = Processing.RadialAutocorrelationProfile( foam ); Console.WriteLine( "mean: " + result.mean( 0 , 0 , 0 ) );