DIPlib Documentation - ©1995-2009 Quantitative Imaging Group, Delft University of Technology.

# FiniteDifferenceEx

A linear gradient filter

## SYNOPSIS

`#include "dip_linear.h"`

dip_Error dip_FiniteDifferenceEx ( in, out, boundary, process, parOrder, smoothflag )

## DATA TYPES

binary, integer, float, complex

## FUNCTION

The FiniteDifferenceEx filter implements several basic one dimensional FIR convolution filters. The difference between this function and `FiniteDifference` is that this one has an interface more similar to `Gauss` and `Derivative`: it can process different derivatives along different dimensions at the same time. The first derivative is a convolution with (1 0 -1)/2, and the second derivative is a convolution with (1 -2 1). When `parOrder` is 0 for a dimension, either the triangular smoothing filter (1 2 1)/4 is applied (`smoothflag` set to `DIP_TRUE`), or the dimension is not processed at all (`smoothflag` set to `DIP_FALSE`).

Setting all `process` to `DIP_TRUE`, all `parOrder` to 0 except 1 dimension to 1, and `smoothflag` to `DIP_TRUE` yields the `SobelGradient`.

## ARGUMENTS

Data type | Name | Description
`dip_Image` | `in` | Input image
`dip_Image` | `out` | Output image
`dip_BoundaryArray` | `boundary` | Boundary conditions
`dip_BooleanArray` | `process` | Dimensions to process
`dip_IntegerArray` | `parOrder` | Order of Derivative along each dimension
`dip_Boolean` | `smoothflag` | Wether or not to smooth in the non-derivative directions

## SEE ALSO

General information about convolution

`FiniteDifference`, `SobelGradient`, `Uniform`, `Gauss`, `Derivative`

DIPlib on-line documentation | Function reference | Global function index