Vergence Control in a Binocular Vision System using Weightless Neural Networks

This paper presents a methodology that we have developed and used to implement an artificial binocular vision system capable of emulating the vergence eye movements. This methodology involves using Weightless Neural Networks (WNNs) as building blocks of artificial vision architectures. Using the proposed methodology, we have designed several WNN based architectures in which images captured by virtual cameras are used for controlling the position of these cameras’ “fovea” (high resolution region of the cameras’ image). Our best architecture is able to control the foveae vergence movements with average error of only 3.6 image pixels, which is equivalent to an angular error of approximately 0.615.