Lateral interaction in accumulative computation: a model for motion detection

Abstract

Some of the major computer vision techniques make use of neural nets. In this paper we present a novel model based on neural networks denominated lateral interaction in accumulative computation (LIAC). This model is based on a series of neuronal models in one layer, namely the local accumulative computation model, the double time scale model and the recurrent lateral interaction model. The LIAC model usefulness in the general task of motion detection may be appreciated by means of some significant examples of object detection in indefinite sequences of synthetic and real images.