net.imglib2.algorithm.gradient

Class PartialDerivative

java.lang.Object

net.imglib2.algorithm.gradient.PartialDerivative

public class PartialDerivative
extends Object

Author:

Tobias Pietzsch, Philipp Hanslovsky, Tim-Oliver Buchholz, Matthias Arzt

Constructor Summary

Constructors

Constructor and Description
PartialDerivative()

Method Summary

Modifier and Type	Method and Description
static <T extends net.imglib2.type.numeric.NumericType<T>> void	gradientBackwardDifference(net.imglib2.RandomAccessible<T> source, net.imglib2.RandomAccessibleInterval<T> result, int dimension) Compute the backward difference of source in a particular dimension: d_f( x ) = ( f( x ) - f( x - e ) ) where e is the unit vector along that dimension
static <T extends net.imglib2.type.numeric.NumericType<T>> void	gradientCentralDifference(net.imglib2.RandomAccessible<T> source, net.imglib2.RandomAccessibleInterval<T> result, int dimension) Compute the partial derivative (central difference approximation) of source in a particular dimension: d_f( x ) = ( f( x + e ) - f( x - e ) ) / 2, where e is the unit vector along that dimension.
static <T extends net.imglib2.type.numeric.NumericType<T>> void	gradientCentralDifference2(net.imglib2.RandomAccessible<T> source, net.imglib2.RandomAccessibleInterval<T> gradient, int dimension) Compute the partial derivative (central difference approximation) of source in a particular dimension: d_f( x ) = ( f( x + e ) - f( x - e ) ) / 2, where e is the unit vector along that dimension.
static <T extends net.imglib2.type.numeric.NumericType<T>> void	gradientCentralDifferenceParallel(net.imglib2.RandomAccessible<T> source, net.imglib2.RandomAccessibleInterval<T> gradient, int dimension, int nTasks, ExecutorService es) Compute the partial derivative (central difference approximation) of source in a particular dimension: d_f( x ) = ( f( x + e ) - f( x - e ) ) / 2, where e is the unit vector along that dimension.
static <T extends net.imglib2.type.numeric.NumericType<T>> void	gradientForwardDifference(net.imglib2.RandomAccessible<T> source, net.imglib2.RandomAccessibleInterval<T> result, int dimension) Compute the forward difference of source in a particular dimension: d_f( x ) = ( f( x + e ) - f( x ) ) where e is the unit vector along that dimension

Methods inherited from class java.lang.Object

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

Constructor Detail

PartialDerivative

public PartialDerivative()

Method Detail

gradientCentralDifference2

public static <T extends net.imglib2.type.numeric.NumericType<T>> void gradientCentralDifference2(net.imglib2.RandomAccessible<T> source,
                                                                                                  net.imglib2.RandomAccessibleInterval<T> gradient,
                                                                                                  int dimension)

Compute the partial derivative (central difference approximation) of source in a particular dimension: d_f( x ) = ( f( x + e ) - f( x - e ) ) / 2, where e is the unit vector along that dimension.

Parameters:

source - source image, has to provide valid data in the interval of the gradient image plus a one pixel border in dimension.

gradient - output image

dimension - along which dimension the partial derivatives are computed

gradientCentralDifferenceParallel

public static <T extends net.imglib2.type.numeric.NumericType<T>> void gradientCentralDifferenceParallel(net.imglib2.RandomAccessible<T> source,
                                                                                                         net.imglib2.RandomAccessibleInterval<T> gradient,
                                                                                                         int dimension,
                                                                                                         int nTasks,
                                                                                                         ExecutorService es)
                                                                                                  throws InterruptedException,
                                                                                                         ExecutionException

Compute the partial derivative (central difference approximation) of source in a particular dimension: d_f( x ) = ( f( x + e ) - f( x - e ) ) / 2, where e is the unit vector along that dimension.

Parameters:

source - source image, has to provide valid data in the interval of the gradient image plus a one pixel border in dimension.

gradient - output image

dimension - along which dimension the partial derivatives are computed

nTasks - Number of tasks for gradient computation.

es - ExecutorService providing workers for gradient computation. Service is managed (created, shutdown) by caller.

Throws:

InterruptedException

ExecutionException

gradientCentralDifference

public static <T extends net.imglib2.type.numeric.NumericType<T>> void gradientCentralDifference(net.imglib2.RandomAccessible<T> source,
                                                                                                 net.imglib2.RandomAccessibleInterval<T> result,
                                                                                                 int dimension)

Compute the partial derivative (central difference approximation) of source in a particular dimension: d_f( x ) = ( f( x + e ) - f( x - e ) ) / 2, where e is the unit vector along that dimension.

Parameters:

source - source image, has to provide valid data in the interval of the gradient image plus a one pixel border in dimension.

result - output image

dimension - along which dimension the partial derivatives are computed

gradientBackwardDifference

public static <T extends net.imglib2.type.numeric.NumericType<T>> void gradientBackwardDifference(net.imglib2.RandomAccessible<T> source,
                                                                                                  net.imglib2.RandomAccessibleInterval<T> result,
                                                                                                  int dimension)

Compute the backward difference of source in a particular dimension: d_f( x ) = ( f( x ) - f( x - e ) ) where e is the unit vector along that dimension

Parameters:

source - source image, has to provide valid data in the interval of the gradient image plus a one pixel border in dimension.

result - output image

dimension - along which dimension the partial derivatives are computed

gradientForwardDifference

public static <T extends net.imglib2.type.numeric.NumericType<T>> void gradientForwardDifference(net.imglib2.RandomAccessible<T> source,
                                                                                                 net.imglib2.RandomAccessibleInterval<T> result,
                                                                                                 int dimension)

Compute the forward difference of source in a particular dimension: d_f( x ) = ( f( x + e ) - f( x ) ) where e is the unit vector along that dimension

Parameters:

source - source image, has to provide valid data in the interval of the gradient image plus a one pixel border in dimension.

result - output image

dimension - along which dimension the partial derivatives are computed