Detecting periodic structure
We present a method for the detection of periodic structure in images. Finding a global threshold to detect a significant frequency component, and then associating it with responsible features, is fragile. The method extracts features from the original signal, and uses their autocorrelation as the basis for an adaptive filter, with which they are convolved. This convolution represents the conformity of a local neighbourhood to the dominant spectral frequencies: combined with the original feature signal, it effectively incorporates the property of periodicity into a local feature strength. A recursive implementation is suggested. The method avoids the fragility associated with global thresholds, and is shown to work on real data.