Elaborated Reichardt correlators for velocity estimation tasks

Abstract

The study of insect vision is believed to provide a key solution to many different aspects of motion detection and velocity estimation. The main reason for this is that motion detection in the fly is extremely fast, with computations requiring only a few milliseconds. So the insect visual system serves as the basis for many models of motion detection. The earliest and the most prominent model is the Reichardt correlator model. But it is found that in the absence of additional system components, the response of a simple Reichardt correlator model is dependent on contrast and spatial frequency. Dror has demonstrated in his work that the addition of spatial and temporal filtering, saturation, integration and adaptation in a correlator based system can make it act as a reliable velocity estimator. In this paper, we try to further investigate and expand his model to improve the correlator performance. Our recent neurobiological experiments suggest that adaptive mechanisms decrease EMD (elementary motion detector) dependence on pattern contrast and improve reliability. So appropriate modelling of an adaptive feedback mechanism is done to normalise contrast of input signals.