mln::linear Namespace Reference

Namespace of linear image processing routines. More...

Namespaces
namespace	gaussian
namespace	impl
	Namespace of linear image processing routines implementation details.
namespace	internal
namespace	local
	Specializations of local linear routines.
namespace	my
Classes
struct	ch_convolve
Functions
template<typename I >
mln::trait::concrete< I >::ret	gaussian (const Image< I > &input, float sigma)
	Gaussian filter of an image input.
template<typename I >
mln::trait::concrete< I >::ret	gaussian (const Image< I > &input, float sigma, int dir)
template<typename I >
mln::trait::concrete< I >::ret	gaussian_1d (const Image< I > &input, double sigma, const typename I::value &bdr)
template<typename I >
mln::trait::concrete< I >::ret	gaussian_1st_derivative (const Image< I > &input, float sigma)
template<typename I >
mln::trait::concrete< I >::ret	gaussian_1st_derivative (const Image< I > &input, float sigma, int dir)
template<typename I >
mln::trait::concrete< I >::ret	gaussian_2nd_derivative (const Image< I > &input, float sigma)
template<typename I >
mln::trait::concrete< I >::ret	gaussian_2nd_derivative (const Image< I > &input, float sigma, int dir)
template<typename I >
mln::trait::concrete< I >::ret	gaussian_directional_2d (const Image< I > &input, unsigned dir, double sigma, const typename I::value &bdr)
template<typename I , typename W , unsigned Sh, unsigned Sv>
	mln_ch_convolve (I, W) convolve_2x1d(const Image< I > &input
template<typename I , typename W >
	mln_ch_convolve (I, W) convolve(const Image< I > &input
template<typename I , typename W , unsigned S>
	mln_ch_convolve (I, W) convolve_directional(const Image< I > &input
template<typename I >
	mln_ch_convolve_grad (I, int) sobel_2d(const Image< I > &input)
	Compute the vertical component of the 2D Sobel gradient.
template<typename I , typename C >
void	recursivefilter_directional_fastest (I &ima, const C &c, const typename I::psite &start, const typename I::psite &finish, int len, const typename I::psite::delta &d, const typename I::value &bdr)
template<typename I , typename C >
void	recursivefilter_directional_generic (I &ima, const C &c, const typename I::psite &start, const typename I::psite &finish, int len, const typename I::psite::delta &d)
template<typename I >
	mln_ch_convolve (I, int) sobel_2d_h(const Image< I > &input)
	Sobel_2d gradient components.
Variables
unsigned	dir
W(&	horizontal_weights )[Sh]
W(W(&	vertical_weights )[Sv]
const Weighted_Window< W > &	w_win
unsigned W(&	weights )[S]

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Detailed Description

Namespace of linear image processing routines.

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Function Documentation

template<typename I >

mln::trait::concrete< I >::ret mln::linear::gaussian	(	const Image< I > &	input,
		float	sigma
	)			[inline]

Gaussian filter of an image input.

Precondition:

output.domain = input.domain

Apply an approximated gaussian filter of sigma on input. This filter is applied in all the input image direction.

Precondition:

input.is_valid

References mln::exact(), mln::linear::impl::gaussian_norm_coef_(), mln::linear::impl::generic_filter_common_(), and mln::initialize().

Referenced by mln::subsampling::gaussian_subsampling().

template<typename I >

mln::trait::concrete< I >::ret mln::linear::gaussian	(	const Image< I > &	input,
		float	sigma,
		int	dir
	)			[inline]

Apply an approximated gaussian filter of sigma on input. on a specific direction dir if dir = 0, the filter is applied on the first image dimension. if dir = 1, the filter is applied on the second image dimension. And so on...

Precondition:

input.is_valid

dir < dimension(input)

References mln::exact(), mln::linear::impl::gaussian_norm_coef_(), mln::linear::impl::generic_filter_common_(), and mln::initialize().

template<typename I >

mln::trait::concrete< I >::ret mln::linear::gaussian_1d	(	const Image< I > &	input,
		double	sigma,
		const typename I::value &	bdr
	)			[inline]

References mln::extension::adjust_fill(), mln::linear::my::recursivefilter_coef_::DericheGaussian, mln::duplicate(), mln::trace::entering(), mln::exact(), mln::trace::exiting(), mln::geom::ninds(), and recursivefilter_directional_fastest().

template<typename I >

mln::trait::concrete< I >::ret mln::linear::gaussian_1st_derivative	(	const Image< I > &	input,
		float	sigma
	)			[inline]

Apply an approximated first derivative gaussian filter of sigma on input This filter is applied in all the input image direction.

Precondition:

input.is_valid

References mln::exact(), mln::linear::impl::gaussian_1st_deriv_coef_norm_(), mln::linear::impl::generic_filter_common_(), and mln::initialize().

template<typename I >

mln::trait::concrete< I >::ret mln::linear::gaussian_1st_derivative	(	const Image< I > &	input,
		float	sigma,
		int	dir
	)			[inline]

Apply an approximated first derivative gaussian filter of sigma on input. on a specific direction dir if dir = 0, the filter is applied on the first image dimension. if dir = 1, the filter is applied on the second image dimension. And so on...

Precondition:

input.is_valid

dir < dimension(input)

References mln::exact(), mln::linear::impl::gaussian_1st_deriv_coef_norm_(), mln::linear::impl::generic_filter_common_(), and mln::initialize().

template<typename I >

mln::trait::concrete< I >::ret mln::linear::gaussian_2nd_derivative	(	const Image< I > &	input,
		float	sigma
	)			[inline]

Apply an approximated second derivative gaussian filter of sigma on input This filter is applied in all the input image direction.

Precondition:

input.is_valid

References mln::exact(), mln::linear::impl::gaussian_2nd_deriv_coef_norm_(), mln::linear::impl::generic_filter_common_(), and mln::initialize().

template<typename I >

mln::trait::concrete< I >::ret mln::linear::gaussian_2nd_derivative	(	const Image< I > &	input,
		float	sigma,
		int	dir
	)			[inline]

Apply an approximated second derivative gaussian filter of sigma on input. on a specific direction dir if dir = 0, the filter is applied on the first image dimension. if dir = 1, the filter is applied on the second image dimension. And so on...

Precondition:

input.is_valid

dir < dimension(input)

References mln::exact(), mln::linear::impl::gaussian_2nd_deriv_coef_norm_(), mln::linear::impl::generic_filter_common_(), and mln::initialize().

template<typename I >

mln::trait::concrete< I >::ret mln::linear::gaussian_directional_2d	(	const Image< I > &	input,
		unsigned	dir,
		double	sigma,
		const typename I::value &	bdr
	)			[inline]

References mln::extension::adjust_fill(), mln::linear::my::recursivefilter_coef_::DericheGaussian, mln::duplicate(), mln::trace::entering(), mln::exact(), mln::trace::exiting(), mln::geom::ncols(), mln::geom::nrows(), and recursivefilter_directional_fastest().

template<typename I >

mln::linear::mln_ch_convolve	(	I	,
		int
	)			const [inline]

Sobel_2d gradient components.

Compute the L1 norm of the 2D Sobel gradient.

Compute the vertical component of the 2D Sobel gradient.

Compute the horizontal component of the 2D Sobel gradient.

References mln::trace::entering(), mln::exact(), mln::trace::exiting(), mln_ch_convolve(), mln_ch_convolve_grad(), and mln::data::transform().

template<typename I , typename W , unsigned Sh, unsigned Sv>

mln::linear::mln_ch_convolve	(	I	,
		W
	)			const

Convolution of an image input by two weighted line-shapes windows.

Warning:

The weighted window is used as-is, considering that its symmetrization is handled by the client.

Precondition:

input.is_valid

template<typename I , typename W >

mln::linear::mln_ch_convolve	(	I	,
		W
	)			const

Convolution of an image input by the weighted window w_win.

Warning:

Computation of output(p) is performed with the value type of output.

The weighted window is used as-is, considering that its symmetrization is handled by the client.

Precondition:

input.is_valid

Referenced by mln_ch_convolve(), and mln_ch_convolve_grad().

template<typename I , typename W , unsigned S>

mln::linear::mln_ch_convolve	(	I	,
		W
	)			const [inline]

Convolution of an image input by a line-shaped (directional) weighted window defined by the array of weights.

Warning:

Computation of output(p) is performed with the value type of output.

The weighted window is used as-is, considering that its symmetrization is handled by the client.

Precondition:

input.is_valid

template<typename I >

mln::linear::mln_ch_convolve_grad	(	I	,
		int
	)			const

Compute the vertical component of the 2D Sobel gradient.

References mln::trace::entering(), mln::exact(), mln::trace::exiting(), mln_ch_convolve(), and mln::data::transform().

Referenced by mln_ch_convolve().

template<typename I , typename C >

void mln::linear::recursivefilter_directional_fastest	(	I &	ima,
		const C &	c,
		const typename I::psite &	start,
		const typename I::psite &	finish,
		int	len,
		const typename I::psite::delta &	d,
		const typename I::value &	bdr
	)			[inline]

Referenced by gaussian_1d(), and gaussian_directional_2d().

template<typename I , typename C >

void mln::linear::recursivefilter_directional_generic	(	I &	ima,
		const C &	c,
		const typename I::psite &	start,
		const typename I::psite &	finish,
		int	len,
		const typename I::psite::delta &	d
	)			[inline]

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Variable Documentation

unsigned mln::linear::dir

W(& mln::linear::horizontal_weights

W( W(& mln::linear::vertical_weights)[Sv]

const Weighted_Window<W>& mln::linear::w_win

unsigned W(& mln::linear::weights)[S]