Directing attention to onset and offset of image events for eye-head movement control

Abstract

This paper proposes a model that investigates a new avenue for attention control based on dynamic scenes. We have derived a computational model to detect abrupt changes and have examined how the most prominent change can be determined. With such a model, we explore the possibility of an attentional mechanism, in part guided by abrupt changes, for gaze control. The computational model is derived from the difference of Gaussian (DOG) model and it examines the change in the response of the DOG operator over time to determine if changes have occurred. On and off-DOG operators are used to detect "on" and "off" events respectively. The response of these operators is examined over various temporal window sizes so that changes at different rates can be found. The most salient "on" and "off" events are determined from the corresponding winner-take-all (WTA) network. The model has been tested with image sequences which have changes caused by brightness or motion and the results are satisfactory.