This paper describes a modular method for classification of 1-D signals which utilizes the shift-invariant MultiScale WAvelet Representation (MSWAR). The classification employs three modules. Representation module that uses the generalization of the multiresolution wavelet representation. Measurement module that uses local and global measures to establish measures of similarity between the reference and observed signals. And finally, classification module that employs a set of decision rules. These rules are derived based on theoretical and experimental considerations, and under specified conditions, guarantee the correct classification of observed signals with five types of deformities.

Date Published: 20 August 1992
PDF: 11 pages
Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); doi: 10.1117/12.139935

Published in SPIE Proceedings Vol. 1706:
Adaptive and Learning Systems
Firooz A. Sadjadi, Editor(s)