process 35 x 35 kernel using convolution method

Dear开发者_Go百科 all, I would like to do a convolution using a 35 x 35 kernel. Any suggestion? or any method already in opencv i can use? Because now the cvfilter2d can only support until 10 x 10 kernel.

If you just need quick-and-dirty solution due to OpenCV's size limitation, then you can divide the 35x35 kernel into a 5x5 set of 7x7 "kernel tiles", apply each "kernel tile" to the image to get an output, then shift the result and combine them to get the final sum.

General suggestions for convolution with large 2D kernels:

1. Try to use kernels that are separable, i.e. a kernel that is the outer product of a column vector and a row vector. In other words, the matrix that represents the kernel is rank-1.
2. Try use the FFT method. Convolution in the spatial domain is the same as elementwise conjugate multiplication in the frequency domain.
3. If the kernel is full-rank and for the application's purpose it cannot be modified, then consider using SVD to decompose the kernel into a set of 35 rank-1 matrices (each of which can be expressed as the outer product of a column vector and a row vector), and perform convolution only with the matrices associated with the largest singular values. This introduces errors into the results, but the error can be estimated based on the singular values. (a.k.a. the MATLAB method)

Other special cases:

1. Kernels that can be expressed as sum of overlapping rectangular blocks can be computed using the integral image (the method used in Viola-Jones face detection).
2. Kernels that are smooth and modal (with a small number of peaks) can be approximated by sum of 2D Gaussians.