Separable Convolution refers to breaking down the convolution kernel into lower dimension kernels. Show that convolution with a 2D Gaussian kernel is a spatially separable convolution, i.e. there are two 1D kernels if applied to the image row-wise and column-wise in sequence, it is equivalent to convolving that image with the 2D Gaussian kernel. Is Sobel kernel spatially separable? Why separable convolutions are preferred?