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Applied Bionics and Biomechanics
Volume 1, Issue 1, Pages 21-31

Using Weightless Neural Networks for Vergence Control in an Artificial Vision System

Karin S. Komati and Alberto F. De Souza

Departamento de Informática, Universidade Federal do Espírito Santo, Vitória, ES, Brazil

Copyright © 2003 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a methodology we have developed and used to implement an artificial binocular vision system capable of emulating the vergence of eye movements. This methodology involves using weightless neural networks (WNNs) as building blocks of artificial vision systems. Using the proposed methodology, we have designed several architectures of WNN-based artificial vision systems, in which images captured by virtual cameras are used for controlling the position of the ‘foveae’ of these cameras (high-resolution region of the images captured). Our best architecture is able to control the foveae vergence movements with average error of only 3.58 image pixels, which is equivalent to an angular error of approximately 0.629°.