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Optical Engineering

Optical correlator-neural networks hybrid system for automatic angle measurement of two-dimensional objects
Author(s): Nadarajah Manivannan; Wamadeva Balachandran; Mark A. A. Neil
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Paper Abstract

A new interpolation algorithm is proposed and demonstrated to perform automatic angle measurement of two-dimensional (2D) objects. The proposed algorithm works in conjunction with optical correlator neural network hybrid architecture (OCNN). The OCNN is trained with a combined algorithm of direct binary search and error back propagation. Input of the OCNN is presented with an image whose angle of rotation is to be measured, and output from the OCNN is fed into the proposed interpolation algorithm, which finally produces the rotation angle of the input image. Results of both computer simulation and experimental set-up are presented for an English alphabetic character as a 2D object. The experimental set-up consists of a real optical correlator using two spatial light modulators for both input and frequency plane representations and a PC based model of a single layer neural network. We obtained very low experimental mean absolute error of 3.18 deg with standard deviation of 2.9 deg.

Paper Details

Date Published: 4 May 2012
PDF: 10 pages
Opt. Eng. 51(5) 057201 doi: 10.1117/1.OE.51.5.057201
Published in: Optical Engineering Volume 51, Issue 5
Show Author Affiliations
Nadarajah Manivannan, Brunel Univ. (United Kingdom)
Wamadeva Balachandran, Brunel Univ. (United Kingdom)
Mark A. A. Neil, Imperial College London (United Kingdom)

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