A proposal for a hierarchical MRF model based on conditional probability

Harukazu Igarashi, Mitsuo Kawato

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

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 languageEnglish
Title of host publication1991 IEEE International Joint Conference on Neural Networks
PublisherPubl by IEEE
Pages268-274
Number of pages7
ISBN (Print)0780302273
Publication statusPublished - 1992 Dec 1
Event1991 IEEE International Joint Conference on Neural Networks - IJCNN '91 - Singapore, Singapore
Duration: 1991 Nov 181991 Nov 21

Publication series

Name1991 IEEE International Joint Conference on Neural Networks

Other

Other1991 IEEE International Joint Conference on Neural Networks - IJCNN '91
CitySingapore, Singapore
Period91/11/1891/11/21

ASJC Scopus subject areas

  • Engineering(all)

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  • 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.