Neural Systems for Automatic Target Learning and Recognition

II We have designed and implemented several computational neural systems for the automatic learning and recognition of targets in both passive visible and synthetic-aperture radar (SAR) imagery. Motivated by biological vision systems (in particular, that of the macaque monkey), our computational neural systems employ a variety of neural networks. Boundary Contour System (BCS) and Feature Contour System (FCS) networks are used for image conditioning. Shunting center-surround networks, Diffusion-Enhancement Bilayer (DEB) networks, log-polar transforms, and overlapping receptive fields are responsible for feature extraction and coding. Adaptive Resonance Theory (ART-2) networks perform aspect categorization and template learning of the targets. And Aspect networks are used to accumulate evidence!confidence over temporal sequences of imagery. In this article we present an overview of our resellrch for the past several years, highlighting our earlier work on the unsupervised learning of threedimensional (3-D) objects as applied to aircraft recognition in the passive visible domain, the recent modification of this system with application to the learning and recognition of tactical targets from SAR imagery, the further application of this system to reentry-vehicle recognition &om inverse SAR, or ISAR, imagery, and the incorporation of this recognition system on a mobile robot called the Mobile Adaptive Visual Navigator (MAVIN) at Lincoln Laboratory.

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