Glenn S. Himes and Rafael M. Iñigo,
Automatic Target Recognition Using a Neocognitron,
IEEE Transactions on Knowledge and Data Engineering,
4, 2, pp. 167-172, April 1992.
Glenn S. Himes and Rafael M. Iñigo,
Automatic Target Recognition Using a Neocognitron,
IEEE Transactions on Knowledge and Data Engineering,
4, 2, pp. 167-172, April 1992.This paper describes the use of a neocognitron in an automatic target recognition system. An image is acquired, edge detected, segmented, and centred on a log-spiral grid using subsystems not discussed in this paper. A conformal transformation is used to map the log-spiral grid to a computation plane in which rotations and scalings are transformed to displacements along the vertical and horizontal axes, respectively. Since the neocognitron can recognize shifted objects, the use of log-spiral images by the neocognitron enables the system to recognize scaled, rotated, and translated objects. Two modifications to prior neocognitron implementations are described. A new weight reinforcement method is introduced which solves a significant training problem for the neocognitron. A method of reducing training time is also introduced which specifies the initial weights in the network. All subsequent layers are trained using unsupervised learning. Simulation results using 32 times 32 and 64 times 64 ICBM images are presented.