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- W2022262154 abstract "In this paper a novel method is proposed and demonstrated for automatic rotation angle measurement of a 2D objectusing a hybrid architecture, consisting of a 4f optical correlator with a binary phase only multiplexed matched filter and asingle layer neural network. The hybrid set-up can be considered as a two-layer perceptron-like neural network; anoptical correlator is the first layer and the standard single layer neural network is the second layer. The training schemeused to train the hybrid architecture is a combination of a Direct Binary Search algorithm, to train the optical correlator,and an Error Back Propagation algorithm, to train the neural network. The aim is to perform the major informationprocessing by the optical correlator with a small additional processing by the neural network stage. This allows thesystem to be used for real-time applications as optics has the inherent ability to process information in a parallel mannerat high speed. The neural network stage gives an extra dimension of freedom so that complicated tasks like automaticrotation angle measurement can be achieved. Results of both computer simulation and experimental set-up are presentedfor rotation angle measurement of an English alphabetic character as a 2D object. The experimental set-up consists of areal optical correlator using two spatial light modulators for both input and frequency plane representations and a PCbased model of a single layer network." @default.
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- W2022262154 date "2011-04-25" @default.
- W2022262154 modified "2023-09-23" @default.
- W2022262154 title "Automatic angle measurement of a 2D object using optical correlator-neural networks hybrid system" @default.
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- W2022262154 doi "https://doi.org/10.1117/12.883653" @default.
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