Title :
A set of neural lattices that use the central limit for Fourier and Gabor transforms, multiple-scale Gaussian smoothing, and edge detection
Author :
Ben-Arie, Jezekiel
Author_Institution :
Dept. of Electr. & Comput. Eng., Illinois Inst. of Technol., Chicago, IL, USA
Abstract :
A set of neural lattices based on the central limit theorem is described. Each of the described lattices generates in parallel a set of multiscale Gaussian smoothings of their input arrays. The recursive smoothing principle of the lattices can be extended to any dimension. In addition, the lattices can generate a variety of multiple-scale operators such as the edge detectors of J. Canny (1986), Laplacians of Gaussians, and multidimensional Fourier and Gabor transforms
Keywords :
Fourier transforms; Laplace transforms; image processing; neural nets; pattern recognition; signal processing; Gabor transforms; central limit theorem; edge detection; input arrays; multidimensional Fourier transforms; multilayered lattice structure; multiple-scale Gaussian smoothing; neural lattices; recursive smoothing principle; Convolution; Detectors; Fourier transforms; Gaussian processes; Image edge detection; Laplace equations; Lattices; Neural networks; Smoothing methods; Speech processing;
Conference_Titel :
Circuits and Systems, 1991., Proceedings of the 34th Midwest Symposium on
Conference_Location :
Monterey, CA
Print_ISBN :
0-7803-0620-1
DOI :
10.1109/MWSCAS.1991.252105
