Locally linear neural networks for optical correlators

摘要

Locally linear neural networks that were developed to process image data using optical correlator outputs are described. These networks extend well-known nearest neighbor techniques and have the desirable properties of coordinate invariance, data interpolation, linear representation, and data bootstrapping. Simplified locally linear networks are successfully used to estimate the rotation of objects in images for objects located by simulated optical correlation.