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Proceedings Paper

Improving classification of neural networks by reducing lens aperture
Author(s): Inna Stainvas; Zeev Zalevsky; David Mendlovic; Nathan Intrator
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Paper Abstract

Image blur strongly degrades object recognition. We propose a mechanism to reduce defocus blur by reducing the aperture of the camera lens, and show that it leads to a far more robust recognition. The recognition is demonstrated via a Neural Network architecture that we have previously proposed for blurred face recognition.

Paper Details

Date Published: 6 December 2002
PDF: 10 pages
Proc. SPIE 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V, (6 December 2002); doi: 10.1117/12.453545
Show Author Affiliations
Inna Stainvas, Tel Aviv Univ. (Israel)
Zeev Zalevsky, Tel Aviv Univ. (Israel)
David Mendlovic, Tel Aviv Univ. (Israel)
Nathan Intrator, Tel Aviv Univ. (Israel)


Published in SPIE Proceedings Vol. 4787:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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