Two-dimensional convolution with X-Y separable kernels. im is the input matric. im is reflected on all sides before convolution. xkernels and ykernels are both row vectors. If ykernels is not given, then use xkernels as ykernels. If xkernels and ykernels are matrices, each row is taken as one convolution kernel and convolved with the image, and the sum of the results is returned. This function is useful when a 2-D filter can be represented by a linear combination of several separable filters. If the number of separable filters that form the 2-D filter is not too large, this can be substantially faster than conv2. From (put citation here) Xuemei Zhang 3/1/96