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