public class PartialDerivative extends Object
| Constructor and Description |
|---|
PartialDerivative() |
| Modifier and Type | Method and Description |
|---|---|
static <T extends NumericType<T>> |
gradientBackwardDifference(RandomAccessible<T> source,
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 NumericType<T>> |
gradientCentralDifference(RandomAccessible<T> source,
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 NumericType<T>> |
gradientCentralDifference2(RandomAccessible<T> source,
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 NumericType<T>> |
gradientCentralDifferenceParallel(RandomAccessible<T> source,
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 NumericType<T>> |
gradientForwardDifference(RandomAccessible<T> source,
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 |
public static <T extends NumericType<T>> void gradientCentralDifference2(RandomAccessible<T> source, RandomAccessibleInterval<T> gradient, int dimension)
d_f( x ) = ( f( x + e ) - f( x - e ) ) / 2,
where e is the unit vector along that dimension.source - source image, has to provide valid data in the interval of the
gradient image plus a one pixel border in dimension.gradient - output imagedimension - along which dimension the partial derivatives are computedpublic static <T extends NumericType<T>> void gradientCentralDifferenceParallel(RandomAccessible<T> source, RandomAccessibleInterval<T> gradient, int dimension, int nTasks, ExecutorService es) throws InterruptedException, ExecutionException
d_f( x ) = ( f( x + e ) - f( x - e ) ) / 2,
where e is the unit vector along that dimension.source - source image, has to provide valid data in the interval of the
gradient image plus a one pixel border in dimension.gradient - output imagedimension - along which dimension the partial derivatives are computednTasks - Number of tasks for gradient computation.es - ExecutorService providing workers for gradient
computation. Service is managed (created, shutdown) by caller.InterruptedExceptionExecutionExceptionpublic static <T extends NumericType<T>> void gradientCentralDifference(RandomAccessible<T> source, RandomAccessibleInterval<T> result, int dimension)
d_f( x ) = ( f( x + e ) - f( x - e ) ) / 2,
where e is the unit vector along that dimension.source - source image, has to provide valid data in the interval of the
gradient image plus a one pixel border in dimension.result - output imagedimension - along which dimension the partial derivatives are computedpublic static <T extends NumericType<T>> void gradientBackwardDifference(RandomAccessible<T> source, RandomAccessibleInterval<T> result, int dimension)
d_f( x ) = ( f( x ) - f( x - e ) )
where e is the unit vector along that dimensionsource - source image, has to provide valid data in the interval of
the gradient image plus a one pixel border in dimension.result - output imagedimension - along which dimension the partial derivatives are computedpublic static <T extends NumericType<T>> void gradientForwardDifference(RandomAccessible<T> source, RandomAccessibleInterval<T> result, int dimension)
d_f( x ) = ( f( x + e ) - f( x ) )
where e is the unit vector along that dimensionsource - source image, has to provide valid data in the interval of
the gradient image plus a one pixel border in dimension.result - output imagedimension - along which dimension the partial derivatives are computedCopyright © 2015–2022 ImgLib2. All rights reserved.