{"title":"Modeling Gauss Markov random fields at multiple resolutions","authors":"S. Krishnamachari, Ramalingam Chellappa","doi":"10.1109/WITS.1994.513917","DOIUrl":null,"url":null,"abstract":"A multiresolution model for Gauss Markov random fields (GMRF) is presented. Based on information theoretic measures, techniques are presented to estimate the GMRF parameters of a process at coarser resolutions from the parameters at fine resolution.","PeriodicalId":423518,"journal":{"name":"Proceedings of 1994 Workshop on Information Theory and Statistics","volume":"124 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 Workshop on Information Theory and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WITS.1994.513917","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
A multiresolution model for Gauss Markov random fields (GMRF) is presented. Based on information theoretic measures, techniques are presented to estimate the GMRF parameters of a process at coarser resolutions from the parameters at fine resolution.
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