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Proceedings Paper

Intelligent edge enhancement using multilayer neural network based on multi-valued neurons
Author(s): Igor Aizenberg; Shane Alexander; Jacob Jackson; Thomas Neal; Jeffrey Wilson; Kristi Kendrick
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Paper Abstract

In this paper, we solve the edge enhancement problem using an intelligent approach. We use a multilayer neural network based on multi-valued neurons (MLMVN) as an intelligent edge enhancer. The problem of neural edge enhancement using a classical multilayer feedforward neural network (MLF) was already considered by some authors. Since MLMVN significantly outperforms MLF in terms of learning speed, number of parameters employed, and generalization capability, it is very attractive to apply it for solving the edge enhancement problem. The main result which is presented in the paper, is the proven ability of MLMVN to enhance edges corresponding to a certain edge detection operator. Moreover, it is possible to enhance edges on noisy images ignoring a noisy texture. It is shown that to learn any edge detection operator using MLMVN, only a single image is required for learning purposes. The most important conclusion is that a neural network can learn different edge detection operators from a single example and then process those images that did not participate in the learning process detecting edges specifically corresponding to the learned operator with a high accuracy.

Paper Details

Date Published: 3 February 2011
PDF: 11 pages
Proc. SPIE 7870, Image Processing: Algorithms and Systems IX, 787004 (3 February 2011); doi: 10.1117/12.872263
Show Author Affiliations
Igor Aizenberg, Texas A&M Univ. (United States)
Shane Alexander, Texas A&M Univ. (United States)
Jacob Jackson, Texas A&M Univ. (United States)
Thomas Neal, Texas A&M Univ. (United States)
Jeffrey Wilson, Texas A&M Univ. (United States)
Kristi Kendrick, Texas A&M Univ. (United States)

Published in SPIE Proceedings Vol. 7870:
Image Processing: Algorithms and Systems IX
Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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