Scale space filtering by Fejer kernel

A multiscale description of the raw signal data must be first obtained before more abstract representations and descriptions are generated. Many unsuccessful attempts have been made to let this initial description be as compact and complete as possible. We propose a new filtering kernel called Fejer kernel to try to tackle this problem. The behaviors of signals´ features in different scales are investigated in Fejer scale space-(x, Δ) plane. Simulation results are shown to demonstrate this new multiscale description for a signal. It compares favorably with Gaussian scale space filtering in that it brings out the main features of the signal under large scales. Gaussian filtering just makes every feature smoother. Further investigation to refine this new multiscale signal description is under development