DocumentCode :
541065
Title :
Some separable linear filtering tasks using CNNs
Author :
Matei, R.P.
Volume :
1
fYear :
0
fDate :
0-0 0
Firstpage :
193
Abstract :
In this paper, we propose some efficient realizations of separable 2-D spatial filters implemented on Cellular Neural Networks (CNNs), based on the Gaussian distribution function, which is approximated by both FIR and IIR filters. We also present a method of iterative filtering, which allows a selective Gaussian function to be implemented by repeating a simple filtering task several times. Some examples of selective low-pass and band-pass separable filters are given to illustrate the design methods.
Keywords :
FIR filters; Gaussian distribution; IIR filters; band-pass filters; cellular neural nets; low-pass filters; 2-D spatial filters; CNN; FIR filter; Gaussian distribution function; IIR filters; band-pass separable filters; cellular neural networks; iterative filtering; linear filtering; low-pass separable filter;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signals, Circuits and Systems, 2003. SCS 2003. International Symposium on
Print_ISBN :
0-7803-7979-9
Type :
conf
DOI :
10.1109/SCS.2003.1226981
Filename :
5731253
Link To Document :
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