The harmogram is calculated from the power spectral density and an estimate of background noise. It is a computationally effective means of analyzing a signal for constituent periodic signals. For one dimensional signals it can 1) provide a means to determine the likelihood that a periodic component is present and 2) determine its principal frequency and related harmonics. The mathematical foundations of the harmogram have recently been extended to two dimensional signals where it provides interesting insight into images (e.g., texture and composition). In either case, the harmogram produces a much reduced feature set making it a useful preprocessing step to classification. This presentation details the harmogram process and illustrates its application with several examples.

(Joint work with O. Thomas Holland)