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Functions | |
| template<typename I , typename W > | |
| mln::linear::ch_convolve< I, W > ::ret | mln::linear::convolve (const Image< I > &input, const Weighted_Window< W > &w_win) |
| template<typename I , typename P , typename W , typename R > | |
| void | mln::linear::local::convolve (const Image< I > &input, const Site< P > &p, const Weighted_Window< W > &w_win, R &result) |
| template<typename P , typename W , typename R > | |
| void | mln::linear::local::convolve (const Generalized_Pixel< P > &p, const Weighted_Window< W > &w_win, R &result) |
| template<typename I , typename W , unsigned Sh, unsigned Sv> | |
| mln::linear::ch_convolve< I, W > ::ret | mln::linear::convolve_2x1d (const Image< I > &input, W(&horizontal_weights)[Sh], W(&vertical_weights)[Sv]) |
| template<typename I , typename W , unsigned S> | |
| mln::linear::ch_convolve< I, W > ::ret | mln::linear::convolve_directional (const Image< I > &input, unsigned dir, W(&weights)[S]) |
| template<typename I > | |
| mln::trait::concrete< I >::ret | mln::linear::gaussian (const Image< I > &input, float sigma) |
| template<typename I > | |
| mln::trait::concrete< I >::ret | mln::linear::gaussian (const Image< I > &input, float sigma, int dir) |
| template<typename I > | |
| mln::trait::concrete< I >::ret | mln::linear::gaussian_1d (const Image< I > &input, double sigma, const typename I::value &bdr) |
| 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) |
| template<typename I > | |
| mln::linear::ch_convolve< I, int >::ret | mln::linear::LoG_13x13 (const Image< I > &input) |
| template<typename I > | |
| mln::linear::ch_convolve< I, int >::ret | mln::linear::LoG_17x17 (const Image< I > &input) |
| template<typename I > | |
| mln::linear::ch_convolve< I, int >::ret | mln::linear::LoG_5x5 (const Image< I > &input) |
| template<typename I > | |
| mln::linear::ch_convolve< I, int >::ret | mln::linear::LoG_7x7 (const Image< I > &input) |
| template<typename I > | |
| mln::linear::ch_convolve< I, int >::ret | mln::linear::lap_4 (const Image< I > &input) |
| template<typename I > | |
| mln::linear::ch_convolve< I, int >::ret | mln::linear::lap_8 (const Image< I > &input) |
| template<typename I > | |
| mln::linear::ch_convolve< I, int >::ret | mln::linear::lap_x (const Image< I > &input) |
| template<typename I > | |
| mln::linear::ch_convolve< I, int >::ret | mln::linear::lap_o (const Image< I > &input) |
| template<typename I > | |
| mln::linear::ch_convolve< I, int >::ret | mln::linear::sobel_2d_h (const Image< I > &input) |
| template<typename I > | |
| mln::linear::ch_convolve< I, int >::ret | mln::linear::sobel_2d_v (const Image< I > &input) |
| template<typename I > | |
| mln::trait::ch_value< I, algebra::vec< I::site::dim, typename mln::linear::ch_convolve< I, int >::ret::value > >::ret | mln::linear::sobel_2d (const Image< I > &input) |
| template<typename I > | |
| mln::linear::ch_convolve< I, int >::ret | mln::linear::sobel_2d_l1_norm (const Image< I > &input) |
| template<unsigned n, typename C > | |
| mln::trait::value_< typename mln::trait::op::times< C, C > ::ret >::sum | mln::norm::l1 (const C(&vec)[n]) |
| template<unsigned n, typename C > | |
| mln::trait::value_< typename mln::trait::op::times< C, C > ::ret >::sum | mln::norm::l1 (const algebra::vec< n, C > &vec) |
| template<unsigned n, typename C > | |
| mln::trait::value_< typename mln::trait::op::times< C, C > ::ret >::sum | mln::norm::l1_distance (const C(&vec1)[n], const C(&vec2)[n]) |
| template<unsigned n, typename C > | |
| mln::trait::value_< typename mln::trait::op::times< C, C > ::ret >::sum | mln::norm::l1_distance (const algebra::vec< n, C > &vec1, const algebra::vec< n, C > &vec2) |
| template<unsigned n, typename C > | |
| mln::trait::value_< typename mln::trait::op::times< C, C > ::ret >::sum | mln::norm::l2 (const C(&vec)[n]) |
| template<unsigned n, typename C > | |
| mln::trait::value_< typename mln::trait::op::times< C, C > ::ret >::sum | mln::norm::l2 (const algebra::vec< n, C > &vec) |
| template<unsigned n, typename C > | |
| mln::trait::value_< typename mln::trait::op::times< C, C > ::ret >::sum | mln::norm::sqr_l2 (const C(&vec)[n]) |
| template<unsigned n, typename C > | |
| mln::trait::value_< typename mln::trait::op::times< C, C > ::ret >::sum | mln::norm::sqr_l2 (const algebra::vec< n, C > &vec) |
| template<unsigned n, typename C > | |
| mln::trait::value_< typename mln::trait::op::times< C, C > ::ret >::sum | mln::norm::l2_distance (const C(&vec1)[n], const C(&vec2)[n]) |
| template<unsigned n, typename C > | |
| mln::trait::value_< typename mln::trait::op::times< C, C > ::ret >::sum | mln::norm::l2_distance (const algebra::vec< n, C > &vec1, const algebra::vec< n, C > &vec2) |
| template<unsigned n, typename C > | |
| C | mln::norm::linfty (const C(&vec)[n]) |
| template<unsigned n, typename C > | |
| C | mln::norm::linfty (const algebra::vec< n, C > &vec) |
| template<unsigned n, typename C > | |
| C | mln::norm::linfty_distance (const C(&vec1)[n], const C(&vec2)[n]) |
| template<unsigned n, typename C > | |
| C | mln::norm::linfty_distance (const algebra::vec< n, C > &vec1, const algebra::vec< n, C > &vec2) |
All linear algebra algorithms.
| mln::linear::ch_convolve< I , W >::ret mln::linear::convolve | ( | const Image< I > & | input, |
| const Weighted_Window< W > & | w_win | ||
| ) |
Convolution of an image input by the weighted window w_win.
output(p) is performed with the value type of output.| void mln::linear::local::convolve | ( | const Image< I > & | input, |
| const Site< P > & | p, | ||
| const Weighted_Window< W > & | w_win, | ||
| R & | result | ||
| ) |
Local convolution of image input at point p by the weighted window w_win.
result is performed with the type R.| void mln::linear::local::convolve | ( | const Generalized_Pixel< P > & | p, |
| const Weighted_Window< W > & | w_win, | ||
| R & | result | ||
| ) |
Local convolution around (generalized) pixel by the weighted window w_win.
result is performed with the type R.| mln::linear::ch_convolve< I , W >::ret mln::linear::convolve_2x1d | ( | const Image< I > & | input, |
| W(&) | horizontal_weights[Sh], | ||
| W(&) | vertical_weights[Sv] | ||
| ) |
Convolution of an image input by two weighted line-shapes windows.
| mln::linear::ch_convolve< I , W >::ret mln::linear::convolve_directional | ( | const Image< I > & | input, |
| unsigned | dir, | ||
| W(&) | weights[S] | ||
| ) |
Convolution of an image input by a line-shaped (directional) weighted window defined by the array of weights.
output(p) is performed with the value type of output.| mln::trait::concrete< I >::ret mln::linear::gaussian | ( | const Image< I > & | input, |
| float | sigma | ||
| ) |
Gaussian filter of an image input.
| mln::trait::concrete< I >::ret mln::linear::gaussian | ( | const Image< I > & | input, |
| float | sigma, | ||
| int | dir | ||
| ) |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
| mln::trait::concrete< I >::ret mln::linear::gaussian_1d | ( | const Image< I > & | input, |
| double | sigma, | ||
| const typename I::value & | bdr | ||
| ) |
Gaussian filter for fastest 1D images.
| mln::trait::concrete< I >::ret mln::linear::gaussian_directional_2d | ( | const Image< I > & | input, |
| unsigned | dir, | ||
| double | sigma, | ||
| const typename I::value & | bdr | ||
| ) |
Directional Gaussian filter for 2D images.
| mln::trait::value_< typename mln::trait::op::times< C , C >::ret >::sum mln::norm::l1 | ( | const C(&) | vec[n] | ) |
L1-norm of a vector vec.
| mln::trait::value_< typename mln::trait::op::times< C , C >::ret >::sum mln::norm::l1 | ( | const algebra::vec< n, C > & | vec | ) |
| mln::trait::value_< typename mln::trait::op::times< C , C >::ret >::sum mln::norm::l1_distance | ( | const C(&) | vec1[n], |
| const C(&) | vec2[n] | ||
| ) |
L1-norm distance between vectors vec1 and vec2.
| mln::trait::value_< typename mln::trait::op::times< C , C >::ret >::sum mln::norm::l1_distance | ( | const algebra::vec< n, C > & | vec1, |
| const algebra::vec< n, C > & | vec2 | ||
| ) |
| mln::trait::value_< typename mln::trait::op::times< C , C >::ret >::sum mln::norm::l2 | ( | const C(&) | vec[n] | ) |
L2-norm of a vector vec.
| mln::trait::value_< typename mln::trait::op::times< C, C >::ret >::sum mln::norm::l2 | ( | const algebra::vec< n, C > & | vec | ) |
| mln::trait::value_< typename mln::trait::op::times< C , C >::ret >::sum mln::norm::l2_distance | ( | const C(&) | vec1[n], |
| const C(&) | vec2[n] | ||
| ) |
L2-norm distance between vectors vec1 and vec2.
| mln::trait::value_< typename mln::trait::op::times< C , C >::ret >::sum mln::norm::l2_distance | ( | const algebra::vec< n, C > & | vec1, |
| const algebra::vec< n, C > & | vec2 | ||
| ) |
| mln::linear::ch_convolve< I , int >::ret mln::linear::lap_4 | ( | const Image< I > & | input | ) |
Laplacian.
| mln::linear::ch_convolve< I , int >::ret mln::linear::lap_8 | ( | const Image< I > & | input | ) |
| mln::linear::ch_convolve< I , int >::ret mln::linear::lap_o | ( | const Image< I > & | input | ) |
| mln::linear::ch_convolve< I , int >::ret mln::linear::lap_x | ( | const Image< I > & | input | ) |
| C mln::norm::linfty | ( | const C(&) | vec[n] | ) |
L-infinity-norm of a vector vec.
| C mln::norm::linfty | ( | const algebra::vec< n, C > & | vec | ) |
| C mln::norm::linfty_distance | ( | const C(&) | vec1[n], |
| const C(&) | vec2[n] | ||
| ) |
L-infinity-norm distance between vectors vec1 and vec2.
| C mln::norm::linfty_distance | ( | const algebra::vec< n, C > & | vec1, |
| const algebra::vec< n, C > & | vec2 | ||
| ) |
| mln::linear::ch_convolve< I , int >::ret mln::linear::LoG_13x13 | ( | const Image< I > & | input | ) |
Laplacian of Gaussian.
LoG 13x13 (Cf. Russ, p. 250).
| mln::linear::ch_convolve< I , int >::ret mln::linear::LoG_17x17 | ( | const Image< I > & | input | ) |
Laplacian of Gaussian.
LoG 17x17 (Cf. Sonka et al., pages 85-86).
| mln::linear::ch_convolve< I , int >::ret mln::linear::LoG_5x5 | ( | const Image< I > & | input | ) |
Laplacian of Gaussian.
LoG_5x5 (Cf. Sonka et al., pages 85-86). This is also a "mexican hat".
| mln::linear::ch_convolve< I , int >::ret mln::linear::LoG_7x7 | ( | const Image< I > & | input | ) |
Laplacian of Gaussian.
LoG 7x7 (Cf. Russ, p. 250).
| mln::trait::ch_value< I , algebra::vec< I ::site::dim, typename mln::linear::ch_convolve< I , int >::ret::value > >::ret mln::linear::sobel_2d | ( | const Image< I > & | input | ) |
Compute the vertical component of the 2D Sobel gradient.
| mln::linear::ch_convolve< I , int >::ret mln::linear::sobel_2d_h | ( | const Image< I > & | input | ) |
Sobel_2d gradient components.
Compute the horizontal component of the 2D Sobel gradient.
| mln::linear::ch_convolve< I , int >::ret mln::linear::sobel_2d_l1_norm | ( | const Image< I > & | input | ) |
Compute the L1 norm of the 2D Sobel gradient.
| mln::linear::ch_convolve< I , int >::ret mln::linear::sobel_2d_v | ( | const Image< I > & | input | ) |
Compute the vertical component of the 2D Sobel gradient.
| mln::trait::value_< typename mln::trait::op::times< C , C >::ret >::sum mln::norm::sqr_l2 | ( | const C(&) | vec[n] | ) |
Squared L2-norm of a vector vec.
| mln::trait::value_< typename mln::trait::op::times< C , C >::ret >::sum mln::norm::sqr_l2 | ( | const algebra::vec< n, C > & | vec | ) |