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Image reconstruction using high-level constraints

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3 Author(s)
Tsuruta, N. ; Dept. of Intelligent Syst., Kyushu Univ., Fukuoka, Japan ; Taniguchi, R. ; Amamiya, M.

In this paper, we propose a strategy to improve the performance of image reconstruction using a selective attention mechanism in a multi-layered neural network. The selective attention mechanism enables us to use top-down information as high-level and global constraints. The traditional algorithms using regularization techniques are quite sensitive to values of parameters, and it is quite difficult to select their appropriate values, because the algorithms use low-level and local constraints. Our strategy uses high-level and global constraints, and modifies the values of parameters locally and automatically

Published in:

Pattern Recognition, 1996., Proceedings of the 13th International Conference on  (Volume:4 )

Date of Conference:

25-29 Aug 1996