Improving optical Fourier pattern recognition by accommodating the missing information

Authors:
H. John Caulfield;Angelo Karavolos;Jacques E. Ludman
Affiliations:
Alabama A&M University Research Institute, P.O. Box 313, Normal, AL;Alabama A&M University Research Institute, P.O. Box 313, Normal, AL;Northeast Photosciences, Inc., 18 Flugg Road, NH
Venue:
Information Sciences: an International Journal - Special issue: Optics and information sciences
Year:
2004

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Abstract

Information is vital to pattern recognition, yet we seldom have enough of it. In fact, some ignorance (absence of knowledge) is inevitable when we try to learn how to classify any real objects or events by learning from a finite set of exemplars. Overcoming that ignorance requires special strategies that are outlined here. The net result is that optical Fourier pattern recognition is converted from a very weak discriminator to the most powerful of all in terms of its generalization ability. The analysis is done on a very simple problem, so the logic can be understood visually as well as mathematically.