Gabor-type filtering in space and time with cellular neural networks

Gabor filters are preprocessing stages in image-processing and computer-vision applications. One drawback is that they are computationally intensive on a digital computer. This paper describes the design of cellular neural networks (CNNs) which compute the outputs of filters similar to Gabor filters. Analog VLSI implementations of these CNNs might eventually relieve the computational bottleneck associated with Gabor filtering image-processing algorithms. The CNNs compute both the real and imaginary parts of the filter outputs simultaneously, which is an important feature in applying them in algorithms utilizing the phase of the Gabor output.