CannyEdgeDetector3d

Performs Canny's computational approach to edge detection on a three-dimensional grayscale image.

Access to parameter description

This algorithm extracts the local maxima of a smoothed gradient image by applying the following steps:

Gaussian filtering
Gradient computation
Non maximum suppression in the gradient direction

The output image is a grayscale image representing the detected contours with one pixel thickness.

To complete the Canny edge detector method, a thresholding by hysteresis must be applied on this output image afterward.
A second output provides an estimation of the two threshold values necessary to perform this step.

Reference: J.F.Canny. "A computational approach to edge detection." IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.8, No 6, pp. 679-698, Nov. 1986.

See also

Function Syntax

This function returns a CannyEdgeDetector3dOutput structure containing the outputImage and outputMeasurement output parameters.

// Output structure.
struct CannyEdgeDetector3dOutput
{
    std::shared_ptr< iolink::ImageView > outputImage;
    CannyEdgeDetectorMsr::Ptr outputMeasurement;
};

// Function prototype.
CannyEdgeDetector3dOutput
cannyEdgeDetector3d( std::shared_ptr< iolink::ImageView > inputImage,
                     iolink::Vector3d standardDeviation,
                     std::shared_ptr< iolink::ImageView > outputImage = NULL,
                     CannyEdgeDetectorMsr::Ptr outputMeasurement = NULL );

This function returns a tuple containing the output_image and output_measurement output parameters.

// Function prototype.
canny_edge_detector_3d( input_image,
                        standard_deviation = [1, 1, 1],
                        output_image = None,
                        output_measurement = None )

This function returns a CannyEdgeDetector3dOutput structure containing the outputImage and outputMeasurement output parameters.

/// Output structure of the CannyEdgeDetector3d function.
public struct CannyEdgeDetector3dOutput
{
    public IOLink.ImageView outputImage;
    public CannyEdgeDetectorMsr outputMeasurement;
};

// Function prototype.
public static CannyEdgeDetector3dOutput
CannyEdgeDetector3d( IOLink.ImageView inputImage,
                     double[] standardDeviation = null,
                     IOLink.ImageView outputImage = null,
                     CannyEdgeDetectorMsr outputMeasurement = null );

Class Syntax

Parameters

Class Name	CannyEdgeDetector3d

	Description	Type	Supported Values	Default Value
inputImage	The input grayscale image.	Image	Binary, Label, Grayscale or Multispectral	nullptr
standardDeviation	The gaussian filter standard deviation value.	Vector3d	>=0.1	{1.f, 1.f, 1.f}
outputImage	The output grayscale image representing the detected edges. Its dimensions are forced to the same values as the input. Its data type is forced to floating point.	Image		nullptr
outputMeasurement	The output measurement provides the estimated parameters of the threshold values used by the hysteresis.	CannyEdgeDetectorMsr		nullptr

Object Examples

auto foam = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam.vip" );

CannyEdgeDetector3d cannyEdgeDetector3dAlgo;
cannyEdgeDetector3dAlgo.setInputImage( foam );
cannyEdgeDetector3dAlgo.setStandardDeviation( {1, 1, 1} );
cannyEdgeDetector3dAlgo.execute();

std::cout << "outputImage:" << cannyEdgeDetector3dAlgo.outputImage()->toString();
std::cout << "thresholdHigh: " << cannyEdgeDetector3dAlgo.outputMeasurement()->thresholdHigh( 0 ) ;

foam = imagedev.read_vip_image(imagedev_data.get_image_path("foam.vip"))

canny_edge_detector_3d_algo = imagedev.CannyEdgeDetector3d()
canny_edge_detector_3d_algo.input_image = foam
canny_edge_detector_3d_algo.standard_deviation = [1, 1, 1]
canny_edge_detector_3d_algo.execute()

print( "output_image:", str( canny_edge_detector_3d_algo.output_image ) )
print( "thresholdHigh: ", str( canny_edge_detector_3d_algo.output_measurement.threshold_high( 0 ) ) )

ImageView foam = Data.ReadVipImage( @"Data/images/foam.vip" );

CannyEdgeDetector3d cannyEdgeDetector3dAlgo = new CannyEdgeDetector3d
{
    inputImage = foam,
    standardDeviation = new double[]{1, 1, 1}
};
cannyEdgeDetector3dAlgo.Execute();

Console.WriteLine( "outputImage:" + cannyEdgeDetector3dAlgo.outputImage.ToString() );
Console.WriteLine( "thresholdHigh: " + cannyEdgeDetector3dAlgo.outputMeasurement.thresholdHigh( 0 ) );

Function Examples

auto foam = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam.vip" );

auto result = cannyEdgeDetector3d( foam, {1, 1, 1} );

std::cout << "outputImage:" << result.outputImage->toString();
std::cout << "thresholdHigh: " << result.outputMeasurement->thresholdHigh( 0 ) ;

foam = imagedev.read_vip_image(imagedev_data.get_image_path("foam.vip"))

result_output_image, result_output_measurement = imagedev.canny_edge_detector_3d( foam, [1, 1, 1] )

print( "output_image:", str( result_output_image ) )
print( "thresholdHigh: ", str( result_output_measurement.threshold_high( 0 ) )  )

ImageView foam = Data.ReadVipImage( @"Data/images/foam.vip" );

Processing.CannyEdgeDetector3dOutput result = Processing.CannyEdgeDetector3d( foam, new double[]{1, 1, 1} );

Console.WriteLine( "outputImage:" + result.outputImage.ToString() );
Console.WriteLine(  "thresholdHigh: " + result.outputMeasurement.thresholdHigh( 0 )  );