Linear lattice architectures that utilize the central limit for image analysis, Gaussian operators, sine, cosine, Fourier, and Gabor transforms
Jezekiel Ben-Arie · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
A set of neural lattices that are based on the central limit theorem is described. These lattices, generate in parallel, a set of multiple scale Gaussian smoothing of their input arrays. As the number of layers is increased, the generated kernels converge to ideal Gaussians with infinitely small error. In addition, the lattices can generate in parallel, a variety of multiple scale image operators such as: Canny's edge detectors, Laplacians of Gaussians, and Sine, Cosine, Fourier and Gabor transforms. It is also proved that any bounded signal, including sinusoidal kernels, can be approximated by a finite number of Gaussians with arbitrarily small error.