Performance of a foveal target-detection system

Foveal active vision features imaging sensors and processing with graded acuity, coupled with context-sensitive gaze control. The wide field of view of peripheral vision reduces target search time, but its low acuity makes it susceptible to preliminary false alarms when operating in environments with structured clutter. In this paper, we present a foveal active vision technique for multiresolution cueing that detects regions of interest (ROIs) with coarse resolution and subsequently interrogates with progressively higher resolution and ROIs are disambiguated. A hierarchical foveal machine vision framework with rectilinear retinotopology is used. A two-stage detector uses multiscale shape matching to identify potential targets and a chain of neural networks to filter out false alarms. This context-sensitive, coarse-to- fine approach minimizes the number of computationally expensive high acuity interrogates required, while preserving performance. Results from our experiments using second generation forward looking infrared imagery are presented.