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Diffusion neural network model for image-preprocessing

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5 Author(s)
Yool Kwon ; Dept. of Electron. Eng., Pusan Nat. Univ., South Korea ; Ki Gon Nam ; Yoon, Tae-Hoon ; Jae Chang Kim
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In this letter we propose a neural network model that performs the Gaussian operation efficiently by the diffusion process. Diffusion of an external spot excitation to the neighbouring pixels results in a Gaussian distribution. We apply this diffusion model to the DOG (difference of Gaussian) operation to detect the intensity changes in an image. In this model each cell has four fixed-weighted interconnections to the neighboring cells for a two-dimensional image. A different spatial frequency component can be obtained in each step of a sequential diffusion process. Therefore, the diffusion model is simpler and more efficient than the well-known LOG masking method. As far as we know, this is the only model for edge detection that can be implemented in hardware

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:7 )

Date of Conference:

27 Jun-2 Jul 1994