Issue No. 02 - April (1992 vol. 4)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/69.134254
<p>The use of a neocognitron in an automatic target recognition (ATR) system is described. An image is acquired, edge detected, segmented, and centered on a log-spiral grid using subsystems not discussed in the 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 layer of weights in the network. All subsequent layers are trained using unsupervised learning. Simulation results using 32*32 and 64*64 intercontinental ballistic missile (ICBM) images are presented.</p>
neocognitron; automatic target recognition; conformal transformation; log-spiral grid; computation plane; weight reinforcement method; training problem; unsupervised learning; intercontinental ballistic missile; computerised pattern recognition; computerised picture processing; learning systems; military systems; neural nets
R. Iñigo and G. Himes, "Automatic Target Recognition Using a Neocognitron," in IEEE Transactions on Knowledge & Data Engineering, vol. 4, no. , pp. 167-172, 1992.