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Math operations for IP algorithms. More...
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| double | lanczos (double x, std::ptrdiff_t a) |
| Lanczos response at point xLanczos response is defined as: x == 0: 1 -a < x && x < a: 0 otherwise: normalized_sinc(x) / normalized_sinc(x / a) | |
| template<typename GradientView , typename OutputView > | |
| void | compute_hessian_entries (GradientView dx, GradientView dy, OutputView ddxx, OutputView dxdy, OutputView ddyy) |
| Compute xy gradient, and second order x and y gradientsHessian matrix is defined as a matrix of partial derivates for 2d case, it is [[ddxx, dxdy], [dxdy, ddyy]. d stands for derivative, and x or y stand for direction. For example, dx stands for derivative (gradient) in horizontal direction, and ddxx means second order derivative in horizon direction https://en.wikipedia.org/wiki/Hessian_matrix. | |
| void | generate_normalized_mean (boost::gil::gray32f_view_t dst) |
| Generate mean kernelFills supplied view with normalized mean in which all entries will be equal to. More... | |
| void | generate_unnormalized_mean (boost::gil::gray32f_view_t dst) |
| Generate kernel with all 1sFills supplied view with 1s (ones) | |
| void | generate_gaussian_kernel (boost::gil::gray32f_view_t dst, double sigma) |
| Generate Gaussian kernelFills supplied view with values taken from Gaussian distribution. See https://en.wikipedia.org/wiki/Gaussian_blur. | |
Math operations for IP algorithms.
This is mostly handful of mathemtical operations that are required by other image processing algorithms
Normalized cardinal sine
normalized_sinc(x) = sin(pi * x) / (pi * x)
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Generate mean kernelFills supplied view with normalized mean in which all entries will be equal to.
