Optical dual-scale architecture for neural image recognition

Mark A. Neifeld

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


A novel neural-network architecture that combines image data reduction with focus of attention to achieve reduced training cost, improved noise tolerance, and better generalization performance than comparable conventional networks for image-recognition tasks is presented. The dual-scale architecture is amenable to optical implementation, and an example optical system is demonstrated. For one example problem, the best-case improvements of the dual-scale network over its conventional counterpart were found through simulation to be a factor of 6.7 in training cost, 67.3% in noise tolerance, and 61.6% in generalization to distortions. The dual-scale network is also applied to one instance of a human face recognition problem.

Original languageEnglish (US)
Pages (from-to)5920-5927
Number of pages8
JournalApplied optics
Issue number26
StatePublished - Sep 1995

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering


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