Relating Statistical Image Differences and Degradation Features
Identifieur interne : 000D88 ( Istex/Corpus ); précédent : 000D87; suivant : 000D89Relating Statistical Image Differences and Degradation Features
Auteurs : Barney Smith ; Xiaohui QiuSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2002.
Abstract
Abstract: Document images are degraded through bilevel processes such as scanning, printing, and photocopying. The resulting image degradations can be categorized based either on observable degradation features or on degradation model parameters. The degradation features can be related mathematically to model parameters. In this paper we statistically compare pairs of populations of degraded character images created with different model parameters. The changes in the probability that the characters are from different populations when the model parameters vary correlate with the relationship between observable degradation features and the model parameters. The paper also shows which features have the largest impact on the image.
Url:
DOI: 10.1007/3-540-45869-7_1
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ISTEX:A05358B8EF1CB4B2C5C9E966513416A761D14F9FLe document en format XML
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The resulting image degradations can be categorized based either on observable degradation features or on degradation model parameters. The degradation features can be related mathematically to model parameters. In this paper we statistically compare pairs of populations of degraded character images created with different model parameters. The changes in the probability that the characters are from different populations when the model parameters vary correlate with the relationship between observable degradation features and the model parameters. The paper also shows which features have the largest impact on the image.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Elisa Barney Smith</name><affiliations><json:string>Boise State University, 83725, Boise, Idaho, USA</json:string><json:string>E-mail: EBarneySmith@boisestate.edu</json:string></affiliations></json:item><json:item><name>Xiaohui Qiu</name><affiliations><json:string>Nanjing University of Post and Telecommunication, China</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: Document images are degraded through bilevel processes such as scanning, printing, and photocopying. The resulting image degradations can be categorized based either on observable degradation features or on degradation model parameters. The degradation features can be related mathematically to model parameters. In this paper we statistically compare pairs of populations of degraded character images created with different model parameters. The changes in the probability that the characters are from different populations when the model parameters vary correlate with the relationship between observable degradation features and the model parameters. 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V</BookTitle></PartContext></PartInfo><Chapter ID="Chap1" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" Language="En" NumberingStyle="Unnumbered" TocLevels="0"><ChapterID>1</ChapterID><ChapterDOI>10.1007/3-540-45869-7_1</ChapterDOI><ChapterSequenceNumber>1</ChapterSequenceNumber><ChapterTitle Language="En">Relating Statistical Image Differences and Degradation Features</ChapterTitle><ChapterFirstPage>1</ChapterFirstPage><ChapterLastPage>12</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2002</CopyrightYear></ChapterCopyright><ChapterHistory><RegistrationDate><Year>2002</Year><Month>8</Month><Day>8</Day></RegistrationDate><OnlineDate><Year>2002</Year><Month>8</Month><Day>9</Day></OnlineDate></ChapterHistory><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>1</PartID><BookID>3-540-45869-7</BookID><BookTitle>Document Analysis Systems V</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff6"><AuthorName DisplayOrder="Western"><GivenName>Elisa</GivenName><GivenName>Barney</GivenName><FamilyName>Smith</FamilyName></AuthorName><Contact><Email>EBarneySmith@boisestate.edu</Email><URL>http://coen.boisestate.edu/EBarneySmith</URL></Contact></Author><Author AffiliationIDS="Aff7"><AuthorName DisplayOrder="Western"><GivenName>Xiaohui</GivenName><FamilyName>Qiu</FamilyName></AuthorName></Author><Affiliation ID="Aff6"><OrgName>Boise State University</OrgName><OrgAddress><Postcode>83725</Postcode><City>Boise</City><State>Idaho</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgName>Nanjing University of Post and Telecommunication</OrgName><OrgAddress><Country>China</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Document images are degraded through bilevel processes such as scanning, printing, and photocopying. The resulting image degradations can be categorized based either on observable degradation features or on degradation model parameters. The degradation features can be related mathematically to model parameters. In this paper we statistically compare pairs of populations of degraded character images created with different model parameters. The changes in the probability that the characters are from different populations when the model parameters vary correlate with the relationship between observable degradation features and the model parameters. The paper also shows which features have the largest impact on the image.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Relating Statistical Image Differences and Degradation Features</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Relating Statistical Image Differences and Degradation Features</title></titleInfo><name type="personal"><namePart type="given">Elisa</namePart><namePart type="given">Barney</namePart><namePart type="family">Smith</namePart><affiliation>Boise State University, 83725, Boise, Idaho, USA</affiliation><affiliation>E-mail: EBarneySmith@boisestate.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Xiaohui</namePart><namePart type="family">Qiu</namePart><affiliation>Nanjing University of Post and Telecommunication, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2002</dateIssued><copyrightDate encoding="w3cdtf">2002</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: Document images are degraded through bilevel processes such as scanning, printing, and photocopying. The resulting image degradations can be categorized based either on observable degradation features or on degradation model parameters. The degradation features can be related mathematically to model parameters. In this paper we statistically compare pairs of populations of degraded character images created with different model parameters. The changes in the probability that the characters are from different populations when the model parameters vary correlate with the relationship between observable degradation features and the model parameters. 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