Discrete implementation of biologically inspired image processing for target detection

In nature, systems which visually process the world around them, in computationally efficient manners, have evolved over millions of years. The brain of an insect, which is smaller than a grain of rice, and with less than a million neurons, can effectively engage in computationally challenging tasks. For example, visually detecting and discriminating small moving objects, which are embedded within a complex optical flow pattern (induced by ego-motion). This task has yet to be perfected by current image processing techniques, though recent research is taking inspiration from nature to do so, in creating biologically inspired models of insect vision. This paper presents the progress made on our previous computational model based on electrophysiological data of a class of cells called Small Target Motion Detection neurons (STMDs). This model was based in the continuous temporal domain with constraints imposed on the inputs to the model. Modifications to the model include re-implementation in the discrete domain, the addition of a more physiologically accurate log-normal filter, the inclusion of a Reichardt Correlator and the creation of the highly controllable virtual world as a front end to the model. Model outputs show that the target detecting characteristics of the previous continuous model are maintained, though in a form which is directly applicable to hardware implementation.