A spectral attentional mechanism tuned to object configurations

This paper describes an attentional mechanism based on the interpretation of spectral signatures for detecting regular object configurations in areas of an image delineated using context information. The proposed global operator relies on the spectral analysis of edge structure and exploits spatial as well as frequency-domain constraints derived from known geometrical models of monitored objects. A decision theoretic method for learning acceptance detection regions is presented. Applications of this attentional mechanism are demonstrated for several aerial image interpretation tasks for attentional as well as recognition purposes. Specific examples are described for detecting vehicle formations (such as convoys), qualifying the geometry of detected formations, or monitoring the occupancy of regions of interest (such as parking areas, roads, or open areas). Experiments and sensitivity analysis results are reported.