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 |
|---|---|
| Title of host publication | 1991 IEEE International Joint Conference on Neural Networks |
| Place of Publication | Piscataway, NJ, United States |
| Publisher | Publ by IEEE |
| Pages | 268-274 |
| Number of pages | 7 |
| ISBN (Print) | 0780302273 |
| Publication status | Published - 1992 |
| Externally published | Yes |
| Event | 1991 IEEE International Joint Conference on Neural Networks - IJCNN '91 - Singapore, Singapore Duration: 1991 Nov 18 → 1991 Nov 21 |
Other
| Other | 1991 IEEE International Joint Conference on Neural Networks - IJCNN '91 |
|---|---|
| City | Singapore, Singapore |
| Period | 91/11/18 → 91/11/21 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
A proposal for a hierarchical MRF model based on conditional probability. / Igarashi, Harukazu; Kawato, Mitsuo.
1991 IEEE International Joint Conference on Neural Networks. Piscataway, NJ, United States : Publ by IEEE, 1992. p. 268-274.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A proposal for a hierarchical MRF model based on conditional probability
AU - Igarashi, Harukazu
AU - Kawato, Mitsuo
PY - 1992
Y1 - 1992
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=0027037259&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0027037259
SN - 0780302273
SP - 268
EP - 274
BT - 1991 IEEE International Joint Conference on Neural Networks
PB - Publ by IEEE
CY - Piscataway, NJ, United States
ER -