GaussianDerivative2d

Approximates the convolution of a two-dimensional image with a Gaussian kernel or its derivative.

Access to parameter description

This algorithm independently applies, on each axis, an approximation of:

a Gaussian filter
the derivative of a Gaussian filter
the second derivative of a Gaussian filter

The Gaussian filter is based on the recursive filtering method proposed by R.Deriche which is defined for one dimension by:

$f(x)=b(\alpha|x|+1)e^{-\alpha|x|}~~where~~b=\frac{\alpha}{4}$ Note that this filter has an infinite impulse response and takes advantage of the separability of the Gaussian kernel.
Using this mode, the computation time is independent of the standard deviation.

Reference
R.Deriche. "Fast algorithms for low-level vision". IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, no 1, pp. 78-87, Jan. 1990.

See also

Function Syntax

This function returns outputImage.

// Function prototype
std::shared_ptr< iolink::ImageView >
gaussianDerivative2d( std::shared_ptr< iolink::ImageView > inputImage,
                      iolink::Vector2i32 orderDerivative,
                      iolink::Vector2d standardDeviation,
                      std::shared_ptr< iolink::ImageView > outputImage = NULL );

This function returns outputImage.

// Function prototype.
gaussian_derivative_2d( input_image,
                        order_derivative = [0, 0],
                        standard_deviation = [1, 1],
                        output_image = None )

This function returns outputImage.

// Function prototype.
public static IOLink.ImageView
GaussianDerivative2d( IOLink.ImageView inputImage,
                      int[] orderDerivative = null,
                      double[] standardDeviation = null,
                      IOLink.ImageView outputImage = null );

Class Syntax

// Command constructor.
GaussianDerivative2d();

/// Gets the inputImage parameter.
/// The input image.
std::shared_ptr< iolink::ImageView > inputImage() const;
/// Sets the inputImage parameter.
/// The input image.
void setInputImage( std::shared_ptr< iolink::ImageView > inputImage );

/// Gets the orderDerivative parameter.
/// The derivation order for each direction X and Y. Each value must be 0, 1 or 2.
iolink::Vector2i32 orderDerivative() const;
/// Sets the orderDerivative parameter.
/// The derivation order for each direction X and Y. Each value must be 0, 1 or 2.
void setOrderDerivative( const iolink::Vector2i32& orderDerivative );

/// Gets the standardDeviation parameter.
/// The sigma value of the Gaussian filter for each direction X and Y. Each value must be greater than or equal to 0.1.
iolink::Vector2d standardDeviation() const;
/// Sets the standardDeviation parameter.
/// The sigma value of the Gaussian filter for each direction X and Y. Each value must be greater than or equal to 0.1.
void setStandardDeviation( const iolink::Vector2d& standardDeviation );

/// Gets the outputImage parameter.
/// The output image. Its dimensions, calibration and interpretation are forced to the same values as the input image. Its type is forced to float.
std::shared_ptr< iolink::ImageView > outputImage() const;
/// Sets the outputImage parameter.
/// The output image. Its dimensions, calibration and interpretation are forced to the same values as the input image. Its type is forced to float.
void setOutputImage( std::shared_ptr< iolink::ImageView > outputImage );


// Method to launch the command.
void execute();

# Property of the inputImage parameter.
GaussianDerivative2d.input_image
# Property of the orderDerivative parameter.
GaussianDerivative2d.order_derivative
# Property of the standardDeviation parameter.
GaussianDerivative2d.standard_deviation
# Property of the outputImage parameter.
GaussianDerivative2d.output_image

// Method to launch the command.
execute()

// Command constructor.
GaussianDerivative2d()

// Property of the inputImage parameter.
GaussianDerivative2d.inputImage
// Property of the orderDerivative parameter.
GaussianDerivative2d.orderDerivative
// Property of the standardDeviation parameter.
GaussianDerivative2d.standardDeviation
// Property of the outputImage parameter.
GaussianDerivative2d.outputImage

// Method to launch the command.
Execute()

Parameters

	Description	Type	Supported Values	Default Value
inputImage	The input image.	Image	Binary, Label, Grayscale or Multispectral	nullptr
standardDeviation	The sigma value of the Gaussian filter for each direction X and Y. Each value must be greater than or equal to 0.1.	Vector2d	>=0.1	{1.f, 1.f}
orderDerivative	The derivation order for each direction X and Y. Each value must be 0, 1 or 2.	Vector2i32	[0, 2]	{0, 0}
outputImage	The output image. Its dimensions, calibration and interpretation are forced to the same values as the input image. Its type is forced to float.	Image		nullptr

Description	Type	Supported Values	Default Value
input_image	The input image.	image	Binary, Label, Grayscale or Multispectral	None
standard_deviation	The sigma value of the Gaussian filter for each direction X and Y. Each value must be greater than or equal to 0.1.	vector2d	>=0.1	[1, 1]
order_derivative	The derivation order for each direction X and Y. Each value must be 0, 1 or 2.	vector2i32	[0, 2]	[0, 0]
output_image	The output image. Its dimensions, calibration and interpretation are forced to the same values as the input image. Its type is forced to float.	image		None

Description	Type	Supported Values	Default Value
inputImage	The input image.	Image	Binary, Label, Grayscale or Multispectral	null
standardDeviation	The sigma value of the Gaussian filter for each direction X and Y. Each value must be greater than or equal to 0.1.	Vector2d	>=0.1	{1f, 1f}
orderDerivative	The derivation order for each direction X and Y. Each value must be 0, 1 or 2.	Vector2i32	[0, 2]	{0, 0}
outputImage	The output image. Its dimensions, calibration and interpretation are forced to the same values as the input image. Its type is forced to float.	Image		null

Object Examples

std::shared_ptr< iolink::ImageView > polystyrene = ioformat::readImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene.tif" );

GaussianDerivative2d gaussianDerivative2dAlgo;
gaussianDerivative2dAlgo.setInputImage( polystyrene );
gaussianDerivative2dAlgo.setOrderDerivative( {0, 0} );
gaussianDerivative2dAlgo.setStandardDeviation( {1.0, 1.0} );
gaussianDerivative2dAlgo.execute();

std::cout << "outputImage:" << gaussianDerivative2dAlgo.outputImage()->toString();

polystyrene = ioformat.read_image(imagedev_data.get_image_path("polystyrene.tif"))

gaussian_derivative_2d_algo = imagedev.GaussianDerivative2d()
gaussian_derivative_2d_algo.input_image = polystyrene
gaussian_derivative_2d_algo.order_derivative = [0, 0]
gaussian_derivative_2d_algo.standard_deviation = [1.0, 1.0]
gaussian_derivative_2d_algo.execute()

print( "output_image:", str( gaussian_derivative_2d_algo.output_image ) )

ImageView polystyrene = ViewIO.ReadImage( @"Data/images/polystyrene.tif" );

GaussianDerivative2d gaussianDerivative2dAlgo = new GaussianDerivative2d
{
    inputImage = polystyrene,
    orderDerivative = new int[]{0, 0},
    standardDeviation = new double[]{1.0, 1.0}
};
gaussianDerivative2dAlgo.Execute();

Console.WriteLine( "outputImage:" + gaussianDerivative2dAlgo.outputImage.ToString() );

Function Examples

std::shared_ptr< iolink::ImageView > polystyrene = ioformat::readImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene.tif" );

auto result = gaussianDerivative2d( polystyrene, {0, 0}, {1.0, 1.0} );

std::cout << "outputImage:" << result->toString();

polystyrene = ioformat.read_image(imagedev_data.get_image_path("polystyrene.tif"))

result = imagedev.gaussian_derivative_2d( polystyrene, [0, 0], [1.0, 1.0] )

print( "output_image:", str( result ) )

ImageView polystyrene = ViewIO.ReadImage( @"Data/images/polystyrene.tif" );

IOLink.ImageView result = Processing.GaussianDerivative2d( polystyrene, new int[]{0, 0}, new double[]{1.0, 1.0} );

Console.WriteLine( "outputImage:" + result.ToString() );

GaussianDerivative2d

- Function Syntax

+ Class Syntax

Parameters

Object Examples

Function Examples

Function Syntax

Class Syntax