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Proceedings Paper

Optoelectronic implementation of diffusion neural network for contour detection
Author(s): Jae-Chang Kim; Cheol Soo Cho; Ki Gon Nam; Tae-Hoon Yoon; Hua-Kuang Liu
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Paper Abstract

In this paper we investigate a feasibility of an opto-electronic implementation of the diffusion neural network for contour detection. The diffusion neural network performs the Gaussian operation efficiently by the diffusion process. We apply this in producing the DOG (Difference of two Gaussian) functions, which can detect the intensity changes of the different spatial frequency components in an image. In the diffusion neural network each neuron has four connections with the four nearest neighbor neurons and a self-decay loop for a 2D image, and the connection weights are fixed-valued. Therefore the diffusion neural network is simpler and more efficient than LOG masking method in hardware or optical implementation. We implement the diffusion neural network opto-electronically using the point spread function of a spatial light modulator. This system is composed of a spatial light modulator, a 2D image sensor array, and a computer. The processing time of the system is very fast. Therefore the system has a potential applicability to the system that requires a real time processing of an image.

Paper Details

Date Published: 5 August 1994
PDF: 5 pages
Proc. SPIE 2321, Second International Conference on Optoelectronic Science and Engineering '94, (5 August 1994); doi: 10.1117/12.182092
Show Author Affiliations
Jae-Chang Kim, Pusan National Univ. (South Korea)
Cheol Soo Cho, Pusan National Univ. (South Korea)
Ki Gon Nam, Pusan National Univ. (South Korea)
Tae-Hoon Yoon, Pusan National Univ. (South Korea)
Hua-Kuang Liu, Jet Propulsion Lab. (United States)

Published in SPIE Proceedings Vol. 2321:
Second International Conference on Optoelectronic Science and Engineering '94
Wang Da-Heng; Anna Consortini; James B. Breckinridge, Editor(s)

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