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Proceedings Paper

Utilizing the central limit theorem for parallel multiple-scale image processing with neural architectures
Author(s): Jezekiel Ben-Arie
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Paper Abstract

A set of neural lattices that are based on the central limit theorem is described. Each of the described lattices, generates in parallel a set of multiple scale Gaussian smoothing of their input arrays. The recursive smoothing principle of the lattices can be extended to any dimension. In addition, the lattices can generate in real time a variety of multiple scale operators such as Canny's edge detectors, Laplacians of Gaussians, and multidimensional sine, cosine, Fourier, and Gabor transforms.

Paper Details

Date Published: 1 October 1991
PDF: 12 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48381
Show Author Affiliations
Jezekiel Ben-Arie, Illinois Institute of Technology (United States)


Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)

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