A proposal for a hierarchical MRF model based on conditional probability

Harukazu Igarashi, Mitsuo Kawato

研究成果: Conference contribution

抜粋

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.

元の言語English
ホスト出版物のタイトル1991 IEEE International Joint Conference on Neural Networks
出版者Publ by IEEE
ページ268-274
ページ数7
ISBN(印刷物)0780302273
出版物ステータスPublished - 1992 12 1
イベント1991 IEEE International Joint Conference on Neural Networks - IJCNN '91 - Singapore, Singapore
継続期間: 1991 11 181991 11 21

出版物シリーズ

名前1991 IEEE International Joint Conference on Neural Networks

Other

Other1991 IEEE International Joint Conference on Neural Networks - IJCNN '91
Singapore, Singapore
期間91/11/1891/11/21

ASJC Scopus subject areas

  • Engineering(all)

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  • これを引用

    Igarashi, H., & Kawato, M. (1992). A proposal for a hierarchical MRF model based on conditional probability. : 1991 IEEE International Joint Conference on Neural Networks (pp. 268-274). (1991 IEEE International Joint Conference on Neural Networks). Publ by IEEE.