Gaussian filtering in one, two or three dimensions is among

the most commonly needed tasks in signal and image processing.

Finite impulse response filters in the time domain

with Gaussian masks are easy to implement in either floating

or fixed point arithmetic, because Gaussian kernels are

strictly positive and bounded. But these implementations are

slow for large images or kernels. With the recursive IIRfilters

and FFT-based methods, there are at least two alternative

methods to perform Gaussian filtering in a faster way,

but so far they are only applicable when floating-point hardware

is available. In this paper, a fixed-point implementation

of recursive Gaussian filtering is discussed and applied

to isotropic and anisotropic image filtering by making use of

a non-orthogonal separation scheme of the Gaussian filter.