Design of linear cellular neural networks for motion sensitive filtering

Recently, several researchers have proposed using spatio-temporal filters for image motion analysis. For example, the optical flow field can be calculated from the output of a set of spatio-temporal filters. Some of the most popular spatio-temporal filters are the space-time Gabor filters, obtained by convolving a time varying image with a space-time Gabor function. Based on the cellular neural network paradigm, we 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.