Anisotropic Gaussian Filtering using Fixed Point Arithmetic

Christoph Lampert, Oliver Wirjadi

In: Proceedings of the 2006 International Conference on Image Processing (ICIP 2006). IEEE International Conference on Image Processing (ICIP) Seiten 1565-1568 ICIP 2006.


Gaussian filtering in one, two or three dimensions is among the most commonly needed tasks in signal and image pro- cessing. Finite impulse response filters in the time domain with Gaussian masks are easy to implement in either float- ing 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 IIR- filters and FFT-based methods, there are at least two alter- native methods to perform Gaussian filtering in a faster way, but so far they are only applicable when floating-point hard- ware is available. In this paper, a fixed-point implementa- tion 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.

ChlOwAnisotropicGaussianFiltering.pdf (pdf, 168 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence