Noise Removal and Restoration Using Voting-Based Analysis and Image Segmentation Based on Statistical Models
Identifieur interne : 000958 ( Istex/Corpus ); précédent : 000957; suivant : 000959Noise Removal and Restoration Using Voting-Based Analysis and Image Segmentation Based on Statistical Models
Auteurs : Jonghyun Park ; Trung Kien ; Gueesang LeeSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2007.
Abstract
Abstract: Restoration and segmentation in corrupted text images are very important processing steps in digital image processing and several different methods were proposed in the open literature. In this paper, the restoration and segmentation problem in corrupted color text images are addressed by tensor voting and statistical method. In the proposed approach, we assume to have corruptions in text images. Our approach consists of two steps. The first one uses the tensor voting algorithm. It encodes every data point as a particle which sends out a vector field. This can be used to decompose the pointness, edgeness and surfaceness of the data points. And then noises in a corrupted region are removed and restored by generalized adaptive vector sigma filters iteratively. In the second step, density mode detection and segmentation using statistical method based on Gaussian mixture model are performed in values according to hue and intensity components in the image. The experimental results show that proposed approach is efficient and robust in terms of restoration and segmentation corrupted text images.
Url:
DOI: 10.1007/978-3-540-74198-5_19
Links to Exploration step
ISTEX:96F4EF5B76A2D28F2FA091A5285A1E4157017109Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Noise Removal and Restoration Using Voting-Based Analysis and Image Segmentation Based on Statistical Models</title><author><name sortKey="Park, Jonghyun" sort="Park, Jonghyun" uniqKey="Park J" first="Jonghyun" last="Park">Jonghyun Park</name><affiliation><mods:affiliation>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju, Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: jhpark@chonnam.ac.kr</mods:affiliation></affiliation></author><author><name sortKey="Kien, Trung" sort="Kien, Trung" uniqKey="Kien T" first="Trung" last="Kien">Trung Kien</name><affiliation><mods:affiliation>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju, Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: trung_kien_kg@yahoo.com</mods:affiliation></affiliation></author><author><name sortKey="Lee, Gueesang" sort="Lee, Gueesang" uniqKey="Lee G" first="Gueesang" last="Lee">Gueesang Lee</name><affiliation><mods:affiliation>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju, Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: gslee@chonnam.ac.kr</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:96F4EF5B76A2D28F2FA091A5285A1E4157017109</idno><date when="2007" year="2007">2007</date><idno type="doi">10.1007/978-3-540-74198-5_19</idno><idno type="url">https://api.istex.fr/document/96F4EF5B76A2D28F2FA091A5285A1E4157017109/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">000958</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Noise Removal and Restoration Using Voting-Based Analysis and Image Segmentation Based on Statistical Models</title><author><name sortKey="Park, Jonghyun" sort="Park, Jonghyun" uniqKey="Park J" first="Jonghyun" last="Park">Jonghyun Park</name><affiliation><mods:affiliation>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju, Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: jhpark@chonnam.ac.kr</mods:affiliation></affiliation></author><author><name sortKey="Kien, Trung" sort="Kien, Trung" uniqKey="Kien T" first="Trung" last="Kien">Trung Kien</name><affiliation><mods:affiliation>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju, Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: trung_kien_kg@yahoo.com</mods:affiliation></affiliation></author><author><name sortKey="Lee, Gueesang" sort="Lee, Gueesang" uniqKey="Lee G" first="Gueesang" last="Lee">Gueesang Lee</name><affiliation><mods:affiliation>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju, Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: gslee@chonnam.ac.kr</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2007</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">96F4EF5B76A2D28F2FA091A5285A1E4157017109</idno><idno type="DOI">10.1007/978-3-540-74198-5_19</idno><idno type="ChapterID">19</idno><idno type="ChapterID">Chap19</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Restoration and segmentation in corrupted text images are very important processing steps in digital image processing and several different methods were proposed in the open literature. In this paper, the restoration and segmentation problem in corrupted color text images are addressed by tensor voting and statistical method. In the proposed approach, we assume to have corruptions in text images. Our approach consists of two steps. The first one uses the tensor voting algorithm. It encodes every data point as a particle which sends out a vector field. This can be used to decompose the pointness, edgeness and surfaceness of the data points. And then noises in a corrupted region are removed and restored by generalized adaptive vector sigma filters iteratively. In the second step, density mode detection and segmentation using statistical method based on Gaussian mixture model are performed in values according to hue and intensity components in the image. The experimental results show that proposed approach is efficient and robust in terms of restoration and segmentation corrupted text images.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Jonghyun Park</name><affiliations><json:string>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju, Korea</json:string><json:string>E-mail: jhpark@chonnam.ac.kr</json:string></affiliations></json:item><json:item><name>Nguyen Trung Kien</name><affiliations><json:string>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju, Korea</json:string><json:string>E-mail: trung_kien_kg@yahoo.com</json:string></affiliations></json:item><json:item><name>Gueesang Lee</name><affiliations><json:string>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju, Korea</json:string><json:string>E-mail: gslee@chonnam.ac.kr</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: Restoration and segmentation in corrupted text images are very important processing steps in digital image processing and several different methods were proposed in the open literature. 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In this paper, the restoration and segmentation problem in corrupted color text images are addressed by tensor voting and statistical method. In the proposed approach, we assume to have corruptions in text images. Our approach consists of two steps. The first one uses the tensor voting algorithm. It encodes every data point as a particle which sends out a vector field. This can be used to decompose the pointness, edgeness and surfaceness of the data points. And then noises in a corrupted region are removed and restored by generalized adaptive vector sigma filters iteratively. In the second step, density mode detection and segmentation using statistical method based on Gaussian mixture model are performed in values according to hue and intensity components in the image. The experimental results show that proposed approach is efficient and robust in terms of restoration and segmentation corrupted text images.</p></abstract><textClass><keywords scheme="Book Subject Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book Subject Group"><list><label>I</label><label>I2203X</label><label>I22021</label><label>I21017</label><label>I22013</label><label>I16021</label><label>I18030</label><item><term>Computer Science</term></item><item><term>Pattern Recognition</term></item><item><term>Image Processing and Computer Vision</term></item><item><term>Artificial Intelligence (incl. 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Proceedings</BookSubTitle><BookVolumeNumber>4679</BookVolumeNumber><BookSequenceNumber>4679</BookSequenceNumber><BookDOI>10.1007/978-3-540-74198-5</BookDOI><BookTitleID>151892</BookTitleID><BookPrintISBN>978-3-540-74195-4</BookPrintISBN><BookElectronicISBN>978-3-540-74198-5</BookElectronicISBN><BookChapterCount>37</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2007</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I2203X" Priority="1" Type="Secondary">Pattern Recognition</BookSubject><BookSubject Code="I22021" Priority="2" Type="Secondary">Image Processing and Computer Vision</BookSubject><BookSubject Code="I21017" Priority="3" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><BookSubject Code="I22013" Priority="4" Type="Secondary">Computer Graphics</BookSubject><BookSubject Code="I16021" Priority="5" Type="Secondary">Algorithm Analysis and Problem Complexity</BookSubject><BookSubject Code="I18030" Priority="6" Type="Secondary">Data Mining and Knowledge Discovery</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup></BookInfo><BookHeader><EditorGroup><Editor><EditorName DisplayOrder="Western"><GivenName>Alan</GivenName><GivenName>L.</GivenName><FamilyName>Yuille</FamilyName></EditorName><Contact><Email>yuille@stat.ucla.edu</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Song-Chun</GivenName><FamilyName>Zhu</FamilyName></EditorName><Contact><Email>sczhu@stat.ucla.edu</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Daniel</GivenName><FamilyName>Cremers</FamilyName></EditorName><Contact><Email>dcremers@cs.uni-bonn.de</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Yongtian</GivenName><FamilyName>Wang</FamilyName></EditorName><Contact><Email>wyt@bit.edu.cn</Email></Contact></Editor></EditorGroup></BookHeader><Part ID="Part4"><PartInfo TocLevels="0"><PartID>4</PartID><PartSequenceNumber>4</PartSequenceNumber><PartTitle>Image Processing</PartTitle><PartChapterCount>3</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Energy Minimization Methods in Computer Vision and Pattern Recognition</BookTitle></PartContext></PartInfo><Chapter ID="Chap19" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingStyle="Unnumbered" TocLevels="0"><ChapterID>19</ChapterID><ChapterDOI>10.1007/978-3-540-74198-5_19</ChapterDOI><ChapterSequenceNumber>19</ChapterSequenceNumber><ChapterTitle Language="En">Noise Removal and Restoration Using Voting-Based Analysis and Image Segmentation Based on Statistical Models</ChapterTitle><ChapterFirstPage>242</ChapterFirstPage><ChapterLastPage>252</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2007</CopyrightYear></ChapterCopyright><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>4</PartID><BookID>978-3-540-74198-5</BookID><BookTitle>Energy Minimization Methods in Computer Vision and Pattern Recognition</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Jonghyun</GivenName><FamilyName>Park</FamilyName></AuthorName><Contact><Email>jhpark@chonnam.ac.kr</Email></Contact></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Nguyen</GivenName><GivenName>Trung</GivenName><FamilyName>Kien</FamilyName></AuthorName><Contact><Email>trung_kien_kg@yahoo.com</Email></Contact></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Gueesang</GivenName><FamilyName>Lee</FamilyName></AuthorName><Contact><Email>gslee@chonnam.ac.kr</Email></Contact></Author><Affiliation ID="Aff1"><OrgName>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju</OrgName><OrgAddress><Country>Korea</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Restoration and segmentation in corrupted text images are very important processing steps in digital image processing and several different methods were proposed in the open literature. In this paper, the restoration and segmentation problem in corrupted color text images are addressed by tensor voting and statistical method. In the proposed approach, we assume to have corruptions in text images. Our approach consists of two steps. The first one uses the tensor voting algorithm. It encodes every data point as a particle which sends out a vector field. This can be used to decompose the pointness, edgeness and surfaceness of the data points. And then noises in a corrupted region are removed and restored by generalized adaptive vector sigma filters iteratively. In the second step, density mode detection and segmentation using statistical method based on Gaussian mixture model are performed in values according to hue and intensity components in the image. The experimental results show that proposed approach is efficient and robust in terms of restoration and segmentation corrupted text images.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>corrupted image restoration</Keyword><Keyword>segmentation</Keyword><Keyword>tensor field</Keyword><Keyword>voting</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Noise Removal and Restoration Using Voting-Based Analysis and Image Segmentation Based on Statistical Models</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Noise Removal and Restoration Using Voting-Based Analysis and Image Segmentation Based on Statistical Models</title></titleInfo><name type="personal"><namePart type="given">Jonghyun</namePart><namePart type="family">Park</namePart><affiliation>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju, Korea</affiliation><affiliation>E-mail: jhpark@chonnam.ac.kr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Nguyen</namePart><namePart type="given">Trung</namePart><namePart type="family">Kien</namePart><affiliation>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju, Korea</affiliation><affiliation>E-mail: trung_kien_kg@yahoo.com</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Gueesang</namePart><namePart type="family">Lee</namePart><affiliation>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju, Korea</affiliation><affiliation>E-mail: gslee@chonnam.ac.kr</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">2007</dateIssued><copyrightDate encoding="w3cdtf">2007</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: Restoration and segmentation in corrupted text images are very important processing steps in digital image processing and several different methods were proposed in the open literature. In this paper, the restoration and segmentation problem in corrupted color text images are addressed by tensor voting and statistical method. In the proposed approach, we assume to have corruptions in text images. Our approach consists of two steps. The first one uses the tensor voting algorithm. It encodes every data point as a particle which sends out a vector field. This can be used to decompose the pointness, edgeness and surfaceness of the data points. And then noises in a corrupted region are removed and restored by generalized adaptive vector sigma filters iteratively. In the second step, density mode detection and segmentation using statistical method based on Gaussian mixture model are performed in values according to hue and intensity components in the image. The experimental results show that proposed approach is efficient and robust in terms of restoration and segmentation corrupted text images.</abstract><relatedItem type="host"><titleInfo><title>Energy Minimization Methods in Computer Vision and Pattern Recognition</title><subTitle>6th International Conference, EMMCVPR 2007, Ezhou, China, August 27-29, 2007. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Alan</namePart><namePart type="given">L.</namePart><namePart type="family">Yuille</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Song-Chun</namePart><namePart type="family">Zhu</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Daniel</namePart><namePart type="family">Cremers</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Yongtian</namePart><namePart type="family">Wang</namePart><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2007</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book Subject Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book Subject Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I2203X">Pattern Recognition</topic><topic authority="SpringerSubjectCodes" authorityURI="I22021">Image Processing and Computer Vision</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I22013">Computer Graphics</topic><topic authority="SpringerSubjectCodes" authorityURI="I16021">Algorithm Analysis and Problem Complexity</topic><topic authority="SpringerSubjectCodes" authorityURI="I18030">Data Mining and Knowledge Discovery</topic></subject><identifier type="DOI">10.1007/978-3-540-74198-5</identifier><identifier type="ISBN">978-3-540-74195-4</identifier><identifier type="eISBN">978-3-540-74198-5</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">151892</identifier><identifier type="BookID">978-3-540-74198-5</identifier><identifier type="BookChapterCount">37</identifier><identifier type="BookVolumeNumber">4679</identifier><identifier type="BookSequenceNumber">4679</identifier><identifier type="PartChapterCount">3</identifier><part><date>2007</date><detail type="part"><title>Image Processing</title></detail><detail 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