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| Gaussian Class Template Reference |
--- | --- | ---

`#include "vigra/gaussians.hxx"`

## Public Types

typedef T | value_type
typedef T | argument_type
typedef T | result_type

## Public Methods

| Gaussian (T sigma=1.0, unsigned int derivativeOrder=0)
result_type | operator() (argument_type x) const
value_type | sigma () const
unsigned int | derivativeOrder () const
double | radius (double sigmaMultiple=3.0) const

## Detailed Description

### template<class T = double>
class vigra::Gaussian< T >

The Gaussian function and its derivatives.

Implemented as a unary functor. Since it supports the `radius()` function it can also be used as a kernel in resamplingConvolveImage().

#include "vigra/gaussians.hxx"
Namespace: vigra

## Member Typedef Documentation

typedef T argument_type

| the functor's argument type

typedef T result_type

| the functor's result type

typedef T value_type

| the value type if used as a kernel in resamplingConvolveImage().

## Constructor & Destructor Documentation

Gaussian | ( | T | sigma = 1.0,
|  | unsigned int | derivativeOrder = 0
| ) | `[inline, explicit]`

Create functor for the given standard deviation `sigma` and derivative order n. The functor then realizes the function

Precondition:

                sigma > 0.0

## Member Function Documentation

unsigned int derivativeOrder | ( |  | ) | const`[inline]`

| Get the derivative order of the Gaussian.

Gaussian< T >::result_type operator() | ( | argument_type | x | ) | const

| Function (functor) call.

double radius | ( | double | sigmaMultiple = 3.0 | ) | const`[inline]`

| Get the required filter radius for a discrete approximation of the Gaussian. The radius is given as a multiple of the Gaussian's standard deviation (default: `sigma * (3 + 1/2 * derivativeOrder()` -- the second term accounts for the fact that the derivatives of the Gaussian become wider with increasing order). The result is rounded to the next higher integer.

value_type sigma | ( |  | ) | const`[inline]`

| Get the standard deviation of the Gaussian.

The documentation for this class was generated from the following file:

- vigra/gaussians.hxx

© Ullrich Köthe (koethe@informatik.uni-hamburg.de) Cognitive Systems Group, University of Hamburg, Germany | html generated using doxygen and Python VIGRA 1.4.0 (21 Dec 2005)