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Proceedings Paper

Large-capacity neural nets for scene analysis
Author(s): David P. Casasent
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Paper Abstract

We consider the classification of multiple objects in a scene with distortion and clutter present. Our opinions on the role for neural nets (NNs) in this application and the different properties that NNs must have to address this problem are advanced. A hierarchical/inference approach is suggested using correlation NNs for low-level operations and new classifier NNs with higher-order decision surfaces for the final decision NNs. Our concern is NN capacity and performance (in noise). Our capacity guidelines advanced concern the number of neurons, use of analog neurons, Ho-Kashyap (HK) NNs, and two new NNs with higher-order decision surfaces. Our noise performance guidelines advanced concern the number of neuron layers, hidden-layer neuron encoding, and robust HK NNs.

Paper Details

Date Published: 16 September 1992
PDF: 15 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140007
Show Author Affiliations
David P. Casasent, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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