Image reconstruction using high-level constraints

N. Tsuruta, Rin-Ichiro Taniguchi, M. Amamiya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationTrack D: Parallel and Connectionist Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages401-405
Number of pages5
Volume4
ISBN (Print)081867282X, 9780818672828
DOIs
Publication statusPublished - 1996
Event13th International Conference on Pattern Recognition, ICPR 1996 - Vienna, Austria
Duration: Aug 25 1996Aug 29 1996

Other

Other13th International Conference on Pattern Recognition, ICPR 1996
CountryAustria
CityVienna
Period8/25/968/29/96

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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    Tsuruta, N., Taniguchi, R-I., & Amamiya, M. (1996). Image reconstruction using high-level constraints. In Track D: Parallel and Connectionist Systems (Vol. 4, pp. 401-405). [547597] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.1996.547597