DocumentCode :
928422
Title :
Design of linear cellular neural networks for motion sensitive filtering
Author :
Shi, Bertram E. ; Roska, Tamás ; Chua, Leon O.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA
Volume :
40
Issue :
5
fYear :
1993
fDate :
5/1/1993 12:00:00 AM
Firstpage :
320
Lastpage :
331
Abstract :
On the basis of the cellular neural network (CNN) paradigm, the authors propose a new architecture for spatio-temporal filtering called a CNN filter array and demonstrate the design of CNN filter arrays for motion sensitive filtering. One advantage of this approach to motion sensitive filtering is that a global convolution in space and time can be performed by using only spatially local interconnections and exploiting the continuous time dynamics of the CNN filter array. No storage of any past image frames is required
Keywords :
filtering and prediction theory; motion estimation; neural nets; CNN filter array; continuous time dynamics; global convolution; linear cellular neural networks; motion sensitive filtering; spatially local interconnections; spatio-temporal filtering; Cellular neural networks; Convolution; Filtering; Frequency; Gabor filters; Image motion analysis; Nonlinear filters; Optical computing; Optical filters; Optical sensors;
fLanguage :
English
Journal_Title :
Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7130
Type :
jour
DOI :
10.1109/82.227372
Filename :
227372
Link To Document :
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