Robustness of CNN implementations for Gabor-type filtering

Gabor filters are preprocessing stages for many image processing and computer vision applications. Unfortunately, they are computationally intensive on a digital computer. Although an analog VLSI chip for Gabor filtering could relieve this bottleneck by computing the filter outputs with less power and in less time than required by serial digital computers, one drawback is a loss in accuracy due to the limited precision with which circuit components can be implemented. This paper describes an analysis of several different possible circuit implementations of an analog VLSI cellular neural network for Gabor filtering which shows that the effect of variations in circuit components can be minimized by proper circuit design.