Abstract
The standard regularization theory extended to problems where generic constraints or knowledge are expressed within the framework of a Markov random field (MRF) model. This extended theory is applied to image restoration in which a desired state in the line process is given as a constraint. The forward process in transformation between two kinds of visual information, from information of pixel intensity to information of edge configuration, is modeled with a renormalization group technique rather than with the usual optics. Perfect restorations were obtained for some simple pictures.
| Original language | English |
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| Title of host publication | 1991 IEEE International Joint Conference on Neural Networks |
| Publisher | Publ by IEEE |
| Pages | 268-274 |
| Number of pages | 7 |
| ISBN (Print) | 0780302273 |
| Publication status | Published - 1992 Dec 1 |
| Event | 1991 IEEE International Joint Conference on Neural Networks - IJCNN '91 - Singapore, Singapore Duration: 1991 Nov 18 → 1991 Nov 21 |
Publication series
| Name | 1991 IEEE International Joint Conference on Neural Networks |
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Other
| Other | 1991 IEEE International Joint Conference on Neural Networks - IJCNN '91 |
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| City | Singapore, Singapore |
| Period | 91/11/18 → 91/11/21 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Igarashi, H., & Kawato, M. (1992). A proposal for a hierarchical MRF model based on conditional probability. In 1991 IEEE International Joint Conference on Neural Networks (pp. 268-274). (1991 IEEE International Joint Conference on Neural Networks). Publ by IEEE.