A new type of nonlinear filter, called theE-filter, is introduced that involves a transformation of the independent variable of the input function. It is shown how anE-filter can be designed to filter out superimposed "noise" on a signal, leaving the large peaks of the signal unattenuated. Unlike a Iow-pass linear filter, the low-passE-filter is almost frequency independent and so does not affect the amplitudes of large sharp peaks of the signal. It is shown that theE-filter can be realized in real time and that a wide class ofE-filters have a filtering action which is independent of the dc level of the input signal.

One aspect of a new theory of feature perception is considered. An algorithm is presented which can perceive and locate various features of a pattern by analyzing a statistic of the "chords" of the pattern. The procedure is illustrated by applying the algorithm to a pattern containing the Muller-Lyer figures. In measuring the length of the figures it is found that the algorithm has a visual illusion. A machine capable of executing the algorithm is described.