1 November 1991 Linear lattice architectures that utilize the central limit for image analysis, Gaussian operators, sine, cosine, Fourier, and Gabor transforms
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Abstract
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.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jezekiel Ben-Arie, Jezekiel Ben-Arie, } "Linear lattice architectures that utilize the central limit for image analysis, Gaussian operators, sine, cosine, Fourier, and Gabor transforms", Proc. SPIE 1606, Visual Communications and Image Processing '91: Image Processing, (1 November 1991); doi: 10.1117/12.50343; https://doi.org/10.1117/12.50343
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