High speed edge detection by sampling a time series with an orthogonal neural network

We introduce a high speed edge detection method where a time series is sampled using an orthogonal neural network, ONN, that is operating in an autoassociative testing mode. The training is done in near real-time. The testing is very fast since there are no calculations involving Gaussian functions, Laplacian operators or convolution. The speed of edge detection is further improved by combining a very simple rule-based expert system with our ONN. A Fourier analysis of an autoassociative ONN is presented. It is also shown that the ONN can improve its performance while on the job using a monitor/teacher system.