Visual attention: detecting abrupt onsets within the selective tuning model

Abstract

The paper focuses on one dimension of a model of visual attention, namely the detection and quantification of abrupt onsets and offsets. The overall model is based on the concept of selective tuning. The goal of the research is to develop a model of visual attention that has both biological plausibility as well as computational utility. Abrupt onsets are well known attention capture cues and play a large role not only in signaling salient events in everyday life, but also figure prominently in most psychophysical experimental paradigms. The solution is simple, easily parallelized, yields excellent performance, and provides useful robot head control cues for onset foveation. The model is described in some detail and several performance examples are shown. A description of the implementation is also included.