Shape identification using a charge simulation retina model

Investigations were conducted to explore the feasibility of a prototype charge simulation retina machine vision system to identify shape and size, when different three-dimensional objects were arbitrarily located in the vision field of the retina. The system consisted of a light source, light beam conditioner, artificial retina installed with photo sensors, data transfer unit, and a computer installed with analogue to digital converter peripherals. The retina was used to acquire image features for regular prisms. The features were transferred to a charge simulation retina model that was identical to the prototype retina and were compressed using a charge simulation method (CSM) algorithm by computing output signals at work cells located in the retina model. With these signals, neural networks were trained to classify each image sample, to identify shape and size. The results showed that object displacement, especially for locations beyond a circle with a radius one-tenth that of the retina and measured from the centre of the base of the retina, significantly affected shape and size classification performance. Despite this, overall shape and size classification rates of 75% and above were obtained when the retina discriminated between different prisms. The results indicate that it is feasible for the charge simulation retina based on the CSM algorithm to identify three-dimensional shapes.