Optoelectronic implementation of diffusion neural network for contour detection

Jae-Chang Kim; Cheol Soo Cho; Ki Gon Nam; Tae-Hoon Yoon; Hua-Kuang Liu

doi:10.1117/12.182092

5 August 1994 Optoelectronic implementation of diffusion neural network for contour detection

Jae-Chang Kim, Cheol Soo Cho, Ki Gon Nam, Tae-Hoon Yoon, Hua-Kuang Liu

Proceedings Volume 2321, Second International Conference on Optoelectronic Science and Engineering '94; (1994) https://doi.org/10.1117/12.182092
Event: Optoelectronic Science and Engineering '94: International Conference, 1994, Beijing, China

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.

Citation Download Citation

Jae-Chang Kim, Cheol Soo Cho, Ki Gon Nam, Tae-Hoon Yoon, and Hua-Kuang Liu "Optoelectronic implementation of diffusion neural network for contour detection", Proc. SPIE 2321, Second International Conference on Optoelectronic Science and Engineering '94, (5 August 1994); https://doi.org/10.1117/12.182092

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