|
| Kernel | Box (size_t dimensions, size_t side_lenght) |
| | Creates and returns a N-dimensional box kernel with 1 coefficients. More...
|
| |
| Kernel | Gabor (float sigma, size_t dimensions, size_t direction) |
| | Creates and returns a Gabor kernel. direction should be lower than dimensions value. More...
|
| |
| Kernel | NormalizedGabor (float sigma, size_t dimensions, size_t direction) |
| | Creates and returns a normalized Gabor kernel. Direction should be lower than dimensions value. More...
|
| |
| Kernel | Gaussian (size_t dimensions, float radius, float std_dev, float amplitude=-1.0f) |
| | Creates and returns a Gaussian kernel. More...
|
| |
| Kernel | NormalizedGaussian (size_t dimensions, float radius, float std_dev) |
| | Creates and returns a normalized Gaussian kernel. More...
|
| |
| Kernel | Circular (float radius=1.0f) |
| | Creates and returns a 2D circular kernel of radius 'radius'. More...
|
| |
| Kernel | Elliptic (float x_radius=1.0f, float y_radius=1.0f) |
| | Creates and returns a 2D elliptic kernel of radius 'radius'. More...
|
| |
| Kernel | Spheric (float radius=1.0f) |
| | Creates and returns a 3D spheric kernel of radius 'radius'. More...
|
| |
| Kernel | HyperSpheric (float radius=1.0f, size_t dims=4) |
| | Creates and returns a hiper-spheric kernel of radius 'radius' and with dim dimensions. More...
|
| |
| Kernel | Ellipsoid (float x_radius=1.0f, float y_radius=1.0f, float z_radius=1.0f) |
| | Creates and returns a 3D ellipsoid kernel of radius 'radius'. More...
|
| |
| Kernel | Sobel (size_t dimensions, size_t direction) |
| | Creates and returns a Sobel kernel. direction should be lower than dimensions value. Each of Sobel's kernel sides is 3 pixels long. It is computed from a triangle filter with smoothing (h(−1) = 1, h(0) = 2, h(1) = 1) and central differences (h'(−1) =−1, h'(0) =0, h'(1) = 1) for derivative reconstruction. E.g. h'x(x,y,z) =h'(x)h(y)h(z). More...
|
| |
| Kernel | NormalizedSobel (size_t dimensions, size_t direction) |
| | Creates and returns a normalized Sobel kernel. direction should be lower than dimensions value. Each of Sobel's kernel sides is 3 pixels long. It is computed from a triangle filter with smoothing (h(−1) = 1, h(0) = 2, h(1) = 1) and central differences (h'(−1) =−1, h'(0) =0, h'(1) = 1) for derivative reconstruction. E.g. h'x(x,y,z) =h'(x)h(y)h(z). Sum of elements is 1. More...
|
| |
| Kernel Bial::KernelType::NormalizedSobel |
( |
size_t |
dimensions, |
|
|
size_t |
direction |
|
) |
| |
Creates and returns a normalized Sobel kernel. direction should be lower than dimensions value. Each of Sobel's kernel sides is 3 pixels long. It is computed from a triangle filter with smoothing (h(−1) = 1, h(0) = 2, h(1) = 1) and central differences (h'(−1) =−1, h'(0) =0, h'(1) = 1) for derivative reconstruction. E.g. h'x(x,y,z) =h'(x)h(y)h(z). Sum of elements is 1.
- Date
- 2013/Nov/22
- Parameters
-
| dimensions | Number of kernel dimensions. |
| direction | Gradient direction to be estimated. |
- Returns
- Normalized Sobel kernel.
- Warning
- none.
| Kernel Bial::KernelType::Sobel |
( |
size_t |
dimensions, |
|
|
size_t |
direction |
|
) |
| |
Creates and returns a Sobel kernel. direction should be lower than dimensions value. Each of Sobel's kernel sides is 3 pixels long. It is computed from a triangle filter with smoothing (h(−1) = 1, h(0) = 2, h(1) = 1) and central differences (h'(−1) =−1, h'(0) =0, h'(1) = 1) for derivative reconstruction. E.g. h'x(x,y,z) =h'(x)h(y)h(z).
- Date
- 2013/Nov/22
- Parameters
-
| dimensions | Number of kernel dimensions. |
| direction | Gradient direction to be estimated. |
- Returns
- Sobel kernel.
- Warning
- none.
1.8.11 
