Utilizing the central limit theorem for parallel multiple-scale image processing with neural architectures

Jezekiel Ben-Arie

doi:10.1117/12.48381

1 October 1991 Utilizing the central limit theorem for parallel multiple-scale image processing with neural architectures

Jezekiel Ben-Arie

Author Affiliations +

Proceedings Volume 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision; (1991) https://doi.org/10.1117/12.48381
Event: San Diego, '91, 1991, San Diego, CA, United States

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.

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Jezekiel Ben-Arie "Utilizing the central limit theorem for parallel multiple-scale image processing with neural architectures", Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); https://doi.org/10.1117/12.48381

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