de.lmu.ifi.dbs.jfeaturelib.edgeDetector

Class Canny

java.lang.Object

de.lmu.ifi.dbs.jfeaturelib.edgeDetector.AbstractDescriptor

de.lmu.ifi.dbs.jfeaturelib.edgeDetector.Canny

All Implemented Interfaces:

Descriptor

public class Canny
extends AbstractDescriptor

A configurable implementation of the Canny edge detection algorithm. This classic algorithm has a number of shortcomings, but remains an effective tool in many scenarios. The original source code was provided by Tom Gibara who released the code to the public domain.

Author:

Tom Gibara

Nested Class Summary

Nested classes/interfaces inherited from interface de.lmu.ifi.dbs.jfeaturelib.Descriptor

Descriptor.Supports

Field Summary

Fields inherited from class de.lmu.ifi.dbs.jfeaturelib.edgeDetector.AbstractDescriptor

pcs

Fields inherited from interface de.lmu.ifi.dbs.jfeaturelib.Descriptor

DOES_ALL

Constructor Summary

Constructors

Constructor and Description
Canny() Constructs a new detector with default parameters.

Method Summary

Methods

Modifier and Type	Method and Description
float	getGaussianKernelRadius() The radius of the Gaussian convolution kernel used to smooth the source image prior to gradient calculation.
int	getGaussianKernelWidth() The number of pixels across which the Gaussian kernel is applied.
float	getHighThreshold() Return the high threshold for hysteresis.
float	getLowThreshold() Get the low threshold for hysteresis.
boolean	isContrastNormalized() Whether the luminance data extracted from the source image is normalized by linearizing its histogram prior to edge extraction.
void	run(ij.process.ImageProcessor ip) Starts the canny edge detection.
void	sContrastNormalized(boolean contrastNormalized) Sets whether the contrast should be normalized
void	setGaussianKernelRadius(float gaussianKernelRadius) Sets the radius of the Gaussian convolution kernel used to smooth the source image prior to gradient calculation.
void	setGaussianKernelWidth(int gaussianKernelWidth) The number of pixels across which the Gaussian kernel is applied.
void	setHighThreshold(float threshold) Sets the high threshold for hysteresis.
void	setLowThreshold(float threshold) Sets the low threshold for hysteresis.
void	setProperties(LibProperties properties)
EnumSet<Descriptor.Supports>	supports() Defines the capability of the algorithm.

Methods inherited from class de.lmu.ifi.dbs.jfeaturelib.edgeDetector.AbstractDescriptor

addPropertyChangeListener, endProgress, startProgress

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Canny

public Canny()

Constructs a new detector with default parameters.

Method Detail

setProperties

public void setProperties(LibProperties properties)
                   throws IOException

Overrides:

setProperties in class AbstractDescriptor

Throws:

IOException

supports

public EnumSet<Descriptor.Supports> supports()

Defines the capability of the algorithm.

Returns:

the set of supported capabilities.

See Also:

PlugInFilter, supports()

run

public void run(ij.process.ImageProcessor ip)

Starts the canny edge detection.

Parameters:

ip - ImageProcessor of the source image

getLowThreshold

public float getLowThreshold()

Get the low threshold for hysteresis.

Returns:

the low hysteresis threshold

setLowThreshold

public void setLowThreshold(float threshold)

Sets the low threshold for hysteresis. Suitable values for this parameter must be determined experimentally for each application. It is nonsensical (though not prohibited) for this value to exceed the high threshold value.

Parameters:

threshold - a low hysteresis threshold

getHighThreshold

public float getHighThreshold()

Return the high threshold for hysteresis.

Returns:

the high hysteresis threshold

setHighThreshold

public void setHighThreshold(float threshold)

Sets the high threshold for hysteresis. Suitable values for this parameter must be determined experimentally for each application. It is nonsensical (though not prohibited) for this value to be less than the low threshold value.

Parameters:

threshold - a high hysteresis threshold

getGaussianKernelWidth

public int getGaussianKernelWidth()

The number of pixels across which the Gaussian kernel is applied.

Returns:

the radius of the convolution operation in pixels

setGaussianKernelWidth

public void setGaussianKernelWidth(int gaussianKernelWidth)

The number of pixels across which the Gaussian kernel is applied. This implementation will reduce the radius if the contribution of pixel values is deemed negligable, so this is actually a maximum radius.

Parameters:

gaussianKernelWidth - a radius for the convolution operation in pixels, at least 2.

getGaussianKernelRadius

public float getGaussianKernelRadius()

The radius of the Gaussian convolution kernel used to smooth the source image prior to gradient calculation. The default value is 16.

Returns:

the Gaussian kernel radius in pixels

setGaussianKernelRadius

public void setGaussianKernelRadius(float gaussianKernelRadius)

Sets the radius of the Gaussian convolution kernel used to smooth the source image prior to gradient calculation.

isContrastNormalized

public boolean isContrastNormalized()

Whether the luminance data extracted from the source image is normalized by linearizing its histogram prior to edge extraction. The default value is false.

Returns:

whether the contrast is normalized

sContrastNormalized

public void sContrastNormalized(boolean contrastNormalized)

Sets whether the contrast should be normalized

Parameters:

contrastNormalized - true if the contrast should be normalized, false otherwise