TY - GEN
T1 - A proposal for a hierarchical MRF model based on conditional probability
AU - Igarashi, Harukazu
AU - Kawato, Mitsuo
PY - 1992/12/1
Y1 - 1992/12/1
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.
UR - http://www.scopus.com/inward/record.url?scp=0027037259&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:0027037259
SN - 0780302273
T3 - 1991 IEEE International Joint Conference on Neural Networks
SP - 268
EP - 274
BT - 1991 IEEE International Joint Conference on Neural Networks
PB - Publ by IEEE
T2 - 1991 IEEE International Joint Conference on Neural Networks - IJCNN '91
Y2 - 18 November 1991 through 21 November 1991
ER -