boofcv.factory.feature.detect.intensity

Class FactoryIntensityPoint

java.lang.Object

boofcv.factory.feature.detect.intensity.FactoryIntensityPoint

public class FactoryIntensityPoint
extends java.lang.Object

Provides intensity feature intensity algorithms which conform to the GeneralFeatureIntensity interface.

See Also:

FactoryIntensityPointAlg, FactoryFeatureExtractor

Constructor Summary

Constructors

Constructor and Description
FactoryIntensityPoint()

Method Summary

Modifier and Type	Method and Description
static <I extends ImageGray,D extends ImageGray> GeneralFeatureIntensity<I,D>	fast(int pixelTol, int minCont, java.lang.Class<I> imageType) Feature intensity for Fast corner detector.
static <I extends ImageGray,D extends ImageGray> GeneralFeatureIntensity<I,D>	harris(int windowRadius, float kappa, boolean weighted, java.lang.Class<D> derivType) Feature intensity for Harris corner detector.
static <I extends ImageGray,D extends ImageGray> GeneralFeatureIntensity<I,D>	hessian(HessianBlobIntensity.Type type, java.lang.Class<D> derivType) Blob detector which uses the image's second order derivatives directly.
static <I extends ImageGray,D extends ImageGray> GeneralFeatureIntensity<I,D>	kitros(java.lang.Class<D> derivType) Feature intensity for Kitchen and Rosenfeld corner detector.
static <I extends ImageGray> GeneralFeatureIntensity<I,?>	laplacian() Blob detector which uses a 3x3 kernel to approximate the second order derivatives and compute a Laplacian blob.
static <I extends ImageGray,D extends ImageGray> GeneralFeatureIntensity<I,D>	median(int radius, java.lang.Class<I> imageType) Feature intensity for median corner detector.
static <I extends ImageGray,D extends ImageGray> GeneralFeatureIntensity<I,D>	shiTomasi(int windowRadius, boolean weighted, java.lang.Class<D> derivType) Feature intensity for KLT corner detector.

Methods inherited from class java.lang.Object

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

Constructor Detail

FactoryIntensityPoint

public FactoryIntensityPoint()

Method Detail

fast

public static <I extends ImageGray,D extends ImageGray> GeneralFeatureIntensity<I,D> fast(int pixelTol,
                                                                                          int minCont,
                                                                                          java.lang.Class<I> imageType)

Feature intensity for Fast corner detector. See FastCornerIntensity for more details.

Type Parameters:

I - Input image type.

D - Derivative type.

Parameters:

pixelTol - How different pixels need to be to be considered part of a corner. Image dependent. Try 20 to start.

minCont - Minimum number of continue pixels in a circle for it ot be a corner. Can be 9,10,11 or 12.

imageType - Type of input image it is computed form.

Returns:

Fast feature intensity

harris

public static <I extends ImageGray,D extends ImageGray> GeneralFeatureIntensity<I,D> harris(int windowRadius,
                                                                                            float kappa,
                                                                                            boolean weighted,
                                                                                            java.lang.Class<D> derivType)

Feature intensity for Harris corner detector. See HarrisCornerIntensity for more details.

Type Parameters:

I - Input image type.

D - Derivative type.

Parameters:

windowRadius - Size of the feature it is detects, Try 2.

kappa - Tuning parameter, typically a small number around 0.04

weighted - Is the gradient weighted using a Gaussian distribution? Weighted is much slower than unweighted.

derivType - Image derivative type it is computed from. @return Harris corner

Returns:

Harris feature intensity

shiTomasi

public static <I extends ImageGray,D extends ImageGray> GeneralFeatureIntensity<I,D> shiTomasi(int windowRadius,
                                                                                               boolean weighted,
                                                                                               java.lang.Class<D> derivType)

Feature intensity for KLT corner detector. See ShiTomasiCornerIntensity for more details.

Type Parameters:

I - Input image type.

D - Derivative type.

Parameters:

windowRadius - Size of the feature it detects, Try 2.

weighted - Should the it be weighted by a Gaussian kernel? Unweighted is much faster.

derivType - Image derivative type it is computed from. * @param derivType Image derivative type it is computed from. @return Harris corner

Returns:

KLT feature intensity

kitros

public static <I extends ImageGray,D extends ImageGray> GeneralFeatureIntensity<I,D> kitros(java.lang.Class<D> derivType)

Feature intensity for Kitchen and Rosenfeld corner detector. See KitRosCornerIntensity for more details.

Type Parameters:

I - Input image type.

D - Derivative type.

Parameters:

derivType - Image derivative type it is computed from.

Returns:

Kitchen and Rosenfeld feature intensity

median

public static <I extends ImageGray,D extends ImageGray> GeneralFeatureIntensity<I,D> median(int radius,
                                                                                            java.lang.Class<I> imageType)

Feature intensity for median corner detector.

Type Parameters:

I - Input image type.

Parameters:

radius - Size of the feature it detects,

Returns:

Median feature intensity

hessian

public static <I extends ImageGray,D extends ImageGray> GeneralFeatureIntensity<I,D> hessian(HessianBlobIntensity.Type type,
                                                                                             java.lang.Class<D> derivType)

Blob detector which uses the image's second order derivatives directly.

Type Parameters:

I - Input image type.

D - Derivative type.

Parameters:

type - Type of Hessian

Returns:

Hessian based blob intensity

See Also:

HessianBlobIntensity

laplacian

public static <I extends ImageGray> GeneralFeatureIntensity<I,?> laplacian()

Blob detector which uses a 3x3 kernel to approximate the second order derivatives and compute a Laplacian blob.