boofcv.alg.filter.derivative

Class GradientSobel

java.lang.Object

boofcv.alg.filter.derivative.GradientSobel

public class GradientSobel
extends Object

Computes the image's first derivative along the x and y axises using the Sobel operator.

The Sobel kernel weights the inner most pixels more than ones farther away. This tends to produce better results, but not as good as a gaussian kernel with larger kernel. However, it can be optimized so that it is much faster than a Gaussian.

For integer images, the derivatives in the x and y direction are computed by convolving the following kernels:

y-axis

-0.25	-0.5	-0.25
0	0	0
0.25	0.5	0.25

Author:

Peter Abeles

Field Summary

-1	0	1
-2	0	2
-1	0	1

Fields

Modifier and Type	Field and Description
static Kernel2D_F32	kernelDerivX_F32
static Kernel2D_I32	kernelDerivX_I32
static Kernel2D_F32	kernelDerivY_F32
static Kernel2D_I32	kernelDerivY_I32

Constructor Summary

Constructors

Constructor and Description
GradientSobel()

Method Summary

Methods

Modifier and Type	Method and Description
static Kernel2D	getKernelX(boolean isInteger) Returns the kernel for computing the derivative along the x-axis.
static void	process(ImageFloat32 orig, ImageFloat32 derivX, ImageFloat32 derivY, ImageBorder_F32 border) Computes the derivative in the X and Y direction using an integer Sobel edge detector.
static void	process(ImageSInt16 orig, ImageSInt16 derivX, ImageSInt16 derivY, ImageBorder_I32<ImageSInt16> border) Computes the derivative in the X and Y direction using an integer Sobel edge detector.
static void	process(ImageUInt8 orig, ImageSInt16 derivX, ImageSInt16 derivY, ImageBorder_I32<ImageUInt8> border) Computes the derivative in the X and Y direction using an integer Sobel edge detector.

Methods inherited from class java.lang.Object

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

Field Detail

kernelDerivX_I32

public static Kernel2D_I32 kernelDerivX_I32

kernelDerivY_I32

public static Kernel2D_I32 kernelDerivY_I32

kernelDerivX_F32

public static Kernel2D_F32 kernelDerivX_F32

kernelDerivY_F32

public static Kernel2D_F32 kernelDerivY_F32

Constructor Detail

GradientSobel

public GradientSobel()

Method Detail

getKernelX

public static Kernel2D getKernelX(boolean isInteger)

Returns the kernel for computing the derivative along the x-axis.

process

public static void process(ImageUInt8 orig,
           ImageSInt16 derivX,
           ImageSInt16 derivY,
           ImageBorder_I32<ImageUInt8> border)

Computes the derivative in the X and Y direction using an integer Sobel edge detector.

Parameters:

orig - Input image. Not modified.

derivX - Storage for image derivative along the x-axis. Modified.

derivY - Storage for image derivative along the y-axis. Modified.

border - Specifies how the image border is handled. If null the border is not processed.

process

public static void process(ImageSInt16 orig,
           ImageSInt16 derivX,
           ImageSInt16 derivY,
           ImageBorder_I32<ImageSInt16> border)

Computes the derivative in the X and Y direction using an integer Sobel edge detector.

Parameters:

orig - Input image. Not modified.

derivX - Storage for image derivative along the x-axis. Modified.

derivY - Storage for image derivative along the y-axis. Modified.

border - Specifies how the image border is handled. If null the border is not processed.

process

public static void process(ImageFloat32 orig,
           ImageFloat32 derivX,
           ImageFloat32 derivY,
           ImageBorder_F32 border)

Computes the derivative in the X and Y direction using an integer Sobel edge detector.

Parameters:

orig - Input image. Not modified.

derivX - Storage for image derivative along the x-axis. Modified.

derivY - Storage for image derivative along the y-axis. Modified.

border - Specifies how the image border is handled. If null the border is not processed.