A brief review is presented of neural network tools for Automatic Target Recognition (ATR) . These tools include collective computation for implementing a variety of computational-vision techniques learning and adaptation for pattern recognition knowledge integration for expert-system capabilities and beyondsupercomputer- level hardware. As a specific example neural networks for stereo vision are introduced as a potentially fruitful approach to ATR. Preliminary results are presented which show substantial performance improvements over previous stereo algorithms for producing accurate dense displacement maps. These maps can be used in turn to derive accurate geometrical shape information that can result in improved recognition performance. 1.
Lin-Chen WangSandor Z. DerNasser M. NasrabadiSyed A. Rizvi
Steven K. RogersJohn M. ColombiCurtis E. MartinJames C. GaineyKenneth H. FieldingThomas J. BurnsDennis W. RuckMatthew KabriskyMark E. Oxley
Steven K. RogersDennis W. RuckMatthew KabriskyGregory L. Tarr