Multistage foveal target detection system

The premise of foveal vision is that surveying a large area with low resolution to detect regions of interest, followed by their verification with localized high resolution, is a more efficient use of computational and communications throughput than resolving the area uniformly at high resolution. This paper presents target/clutter discrimination techniques that support the foveal multistage detection and verification of infrared-sensed ground targets in cluttered environments. The first technique uses a back-propagation neural network to classify narrow field-of-view high acuity image chips using their projection onto a set of principal components as input features. The second technique applies linear discriminant analysis on the same input features. Both techniques include refinements that address generalization and detected region of interest position errors. Experimental results using second generation forward looking infrared imagery are presented.