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. 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>
<qualityIndicators><score>5.528</score>
<pdfVersion>1.3</pdfVersion>
<pdfPageSize>430 x 660 pts</pdfPageSize>
<refBibsNative>false</refBibsNative>
<keywordCount>0</keywordCount>
<abstractCharCount>1116</abstractCharCount>
<pdfWordCount>3476</pdfWordCount>
<pdfCharCount>18420</pdfCharCount>
<pdfPageCount>11</pdfPageCount>
<abstractWordCount>171</abstractWordCount>
</qualityIndicators>
<title>Noise Removal and Restoration Using Voting-Based Analysis and Image Segmentation Based on Statistical Models</title>
<genre.original><json:string>OriginalPaper</json:string>
</genre.original>
<chapterId><json:string>19</json:string>
<json:string>Chap19</json:string>
</chapterId>
<genre><json:string>conference [eBooks]</json:string>
</genre>
<serie><editor><json:item><name>David Hutchison</name>
</json:item>
<json:item><name>Takeo Kanade</name>
</json:item>
<json:item><name>Josef Kittler</name>
</json:item>
<json:item><name>Jon M. Kleinberg</name>
</json:item>
<json:item><name>Friedemann Mattern</name>
</json:item>
<json:item><name>John C. Mitchell</name>
</json:item>
<json:item><name>Moni Naor</name>
</json:item>
<json:item><name>Oscar Nierstrasz</name>
</json:item>
<json:item><name>C. Pandu Rangan</name>
</json:item>
<json:item><name>Bernhard Steffen</name>
</json:item>
<json:item><name>Madhu Sudan</name>
</json:item>
<json:item><name>Demetri Terzopoulos</name>
</json:item>
<json:item><name>Doug Tygar</name>
</json:item>
<json:item><name>Moshe Y. Vardi</name>
</json:item>
<json:item><name>Gerhard Weikum</name>
</json:item>
</editor>
<issn><json:string>0302-9743</json:string>
</issn>
<language><json:string>unknown</json:string>
</language>
<eissn><json:string>1611-3349</json:string>
</eissn>
<title>Lecture Notes in Computer Science</title>
<copyrightDate>2007</copyrightDate>
</serie>
<host><editor><json:item><name>Alan L. Yuille</name>
</json:item>
<json:item><name>Song-Chun Zhu</name>
</json:item>
<json:item><name>Daniel Cremers</name>
</json:item>
<json:item><name>Yongtian Wang</name>
</json:item>
</editor>
<subject><json:item><value>Computer Science</value>
</json:item>
<json:item><value>Computer Science</value>
</json:item>
<json:item><value>Pattern Recognition</value>
</json:item>
<json:item><value>Image Processing and Computer Vision</value>
</json:item>
<json:item><value>Artificial Intelligence (incl. Robotics)</value>
</json:item>
<json:item><value>Computer Graphics</value>
</json:item>
<json:item><value>Algorithm Analysis and Problem Complexity</value>
</json:item>
<json:item><value>Data Mining and Knowledge Discovery</value>
</json:item>
</subject>
<isbn><json:string>978-3-540-74195-4</json:string>
</isbn>
<language><json:string>unknown</json:string>
</language>
<eissn><json:string>1611-3349</json:string>
</eissn>
<title>Energy Minimization Methods in Computer Vision and Pattern Recognition</title>
<genre.original><json:string>Proceedings</json:string>
</genre.original>
<bookId><json:string>978-3-540-74198-5</json:string>
</bookId>
<volume>4679</volume>
<pages><last>252</last>
<first>242</first>
</pages>
<issn><json:string>0302-9743</json:string>
</issn>
<genre><json:string>Book Series</json:string>
</genre>
<eisbn><json:string>978-3-540-74198-5</json:string>
</eisbn>
<copyrightDate>2007</copyrightDate>
<doi><json:string>10.1007/978-3-540-74198-5</json:string>
</doi>
</host>
<publicationDate>2007</publicationDate>
<copyrightDate>2007</copyrightDate>
<doi><json:string>10.1007/978-3-540-74198-5_19</json:string>
</doi>
<id>96F4EF5B76A2D28F2FA091A5285A1E4157017109</id>
<fulltext><json:item><original>true</original>
<mimetype>application/pdf</mimetype>
<extension>pdf</extension>
<uri>https://api.istex.fr/document/96F4EF5B76A2D28F2FA091A5285A1E4157017109/fulltext/pdf</uri>
</json:item>
<json:item><original>false</original>
<mimetype>application/zip</mimetype>
<extension>zip</extension>
<uri>https://api.istex.fr/document/96F4EF5B76A2D28F2FA091A5285A1E4157017109/fulltext/zip</uri>
</json:item>
<istex:fulltextTEI uri="https://api.istex.fr/document/96F4EF5B76A2D28F2FA091A5285A1E4157017109/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">Noise Removal and Restoration Using Voting-Based Analysis and Image Segmentation Based on Statistical Models</title>
<respStmt xml:id="ISTEX-API" resp="Références bibliographiques récupérées via GROBID" name="ISTEX-API (INIST-CNRS)"></respStmt>
</titleStmt>
<publicationStmt><authority>ISTEX</authority>
<publisher>Springer Berlin Heidelberg</publisher>
<pubPlace>Berlin, Heidelberg</pubPlace>
<availability><p>SPRINGER</p>
</availability>
<date>2007</date>
</publicationStmt>
<sourceDesc><biblStruct type="inbook"><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><persName><forename type="first">Jonghyun</forename>
<surname>Park</surname>
</persName>
<email>jhpark@chonnam.ac.kr</email>
<affiliation>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju, Korea</affiliation>
</author>
<author><persName><forename type="first">Nguyen</forename>
<surname>Kien</surname>
</persName>
<email>trung_kien_kg@yahoo.com</email>
<affiliation>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju, Korea</affiliation>
</author>
<author><persName><forename type="first">Gueesang</forename>
<surname>Lee</surname>
</persName>
<email>gslee@chonnam.ac.kr</email>
<affiliation>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju, Korea</affiliation>
</author>
</analytic>
<monogr><title level="m">Energy Minimization Methods in Computer Vision and Pattern Recognition</title>
<title level="m" type="sub">6th International Conference, EMMCVPR 2007, Ezhou, China, August 27-29, 2007. Proceedings</title>
<idno type="pISBN">978-3-540-74195-4</idno>
<idno type="eISBN">978-3-540-74198-5</idno>
<idno type="pISSN">0302-9743</idno>
<idno type="eISSN">1611-3349</idno>
<idno type="DOI">10.1007/978-3-540-74198-5</idno>
<idno type="BookID">978-3-540-74198-5</idno>
<idno type="BookTitleID">151892</idno>
<idno type="BookSequenceNumber">4679</idno>
<idno type="BookVolumeNumber">4679</idno>
<idno type="BookChapterCount">37</idno>
<editor><persName><forename type="first">Alan</forename>
<forename type="first">L.</forename>
<surname>Yuille</surname>
</persName>
</editor>
<editor><persName><forename type="first">Song-Chun</forename>
<surname>Zhu</surname>
</persName>
</editor>
<editor><persName><forename type="first">Daniel</forename>
<surname>Cremers</surname>
</persName>
</editor>
<editor><persName><forename type="first">Yongtian</forename>
<surname>Wang</surname>
</persName>
</editor>
<imprint><publisher>Springer Berlin Heidelberg</publisher>
<pubPlace>Berlin, Heidelberg</pubPlace>
<date type="published" when="2007"></date>
<biblScope unit="volume">4679</biblScope>
<biblScope unit="page" from="242">242</biblScope>
<biblScope unit="page" to="252">252</biblScope>
</imprint>
</monogr>
<series><title level="s">Lecture Notes in Computer Science</title>
<editor><persName><forename type="first">David</forename>
<surname>Hutchison</surname>
</persName>
</editor>
<editor><persName><forename type="first">Takeo</forename>
<surname>Kanade</surname>
</persName>
</editor>
<editor><persName><forename type="first">Josef</forename>
<surname>Kittler</surname>
</persName>
</editor>
<editor><persName><forename type="first">Jon</forename>
<forename type="first">M.</forename>
<surname>Kleinberg</surname>
</persName>
</editor>
<editor><persName><forename type="first">Friedemann</forename>
<surname>Mattern</surname>
</persName>
</editor>
<editor><persName><forename type="first">John</forename>
<forename type="first">C.</forename>
<surname>Mitchell</surname>
</persName>
</editor>
<editor><persName><forename type="first">Moni</forename>
<surname>Naor</surname>
</persName>
</editor>
<editor><persName><forename type="first">Oscar</forename>
<surname>Nierstrasz</surname>
</persName>
</editor>
<editor><persName><forename type="first">C.</forename>
<surname>Pandu Rangan</surname>
</persName>
</editor>
<editor><persName><forename type="first">Bernhard</forename>
<surname>Steffen</surname>
</persName>
</editor>
<editor><persName><forename type="first">Madhu</forename>
<surname>Sudan</surname>
</persName>
</editor>
<editor><persName><forename type="first">Demetri</forename>
<surname>Terzopoulos</surname>
</persName>
</editor>
<editor><persName><forename type="first">Doug</forename>
<surname>Tygar</surname>
</persName>
</editor>
<editor><persName><forename type="first">Moshe</forename>
<forename type="first">Y.</forename>
<surname>Vardi</surname>
</persName>
</editor>
<editor><persName><forename type="first">Gerhard</forename>
<surname>Weikum</surname>
</persName>
</editor>
<biblScope><date>2007</date>
</biblScope>
<idno type="pISSN">0302-9743</idno>
<idno type="eISSN">1611-3349</idno>
<idno type="seriesId">558</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>
</fileDesc>
<profileDesc><creation><date>2007</date>
</creation>
<langUsage><language ident="en">en</language>
</langUsage>
<abstract xml:lang="en"><p>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.</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. Robotics)</term>
</item>
<item><term>Computer Graphics</term>
</item>
<item><term>Algorithm Analysis and Problem Complexity</term>
</item>
<item><term>Data Mining and Knowledge Discovery</term>
</item>
</list>
</keywords>
</textClass>
</profileDesc>
<revisionDesc><change when="2007">Published</change>
<change xml:id="refBibs-istex" who="#ISTEX-API" when="2016-3-19">References added</change>
</revisionDesc>
</teiHeader>
</istex:fulltextTEI>
<json:item><original>false</original>
<mimetype>text/plain</mimetype>
<extension>txt</extension>
<uri>https://api.istex.fr/document/96F4EF5B76A2D28F2FA091A5285A1E4157017109/fulltext/txt</uri>
</json:item>
</fulltext>
<metadata><istex:metadataXml wicri:clean="Springer, Publisher found" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="UTF-8"</istex:xmlDeclaration>
<istex:docType PUBLIC="-//Springer-Verlag//DTD A++ V2.4//EN" URI="http://devel.springer.de/A++/V2.4/DTD/A++V2.4.dtd" name="istex:docType"></istex:docType>
<istex:document><Publisher><PublisherInfo><PublisherName>Springer Berlin Heidelberg</PublisherName>
<PublisherLocation>Berlin, Heidelberg</PublisherLocation>
</PublisherInfo>
<Series><SeriesInfo SeriesType="Series" TocLevels="0"><SeriesID>558</SeriesID>
<SeriesPrintISSN>0302-9743</SeriesPrintISSN>
<SeriesElectronicISSN>1611-3349</SeriesElectronicISSN>
<SeriesTitle Language="En">Lecture Notes in Computer Science</SeriesTitle>
</SeriesInfo>
<SeriesHeader><EditorGroup><Editor><EditorName DisplayOrder="Western"><GivenName>David</GivenName>
<FamilyName>Hutchison</FamilyName>
</EditorName>
</Editor>
<Editor><EditorName DisplayOrder="Western"><GivenName>Takeo</GivenName>
<FamilyName>Kanade</FamilyName>
</EditorName>
</Editor>
<Editor><EditorName DisplayOrder="Western"><GivenName>Josef</GivenName>
<FamilyName>Kittler</FamilyName>
</EditorName>
</Editor>
<Editor><EditorName DisplayOrder="Western"><GivenName>Jon</GivenName>
<GivenName>M.</GivenName>
<FamilyName>Kleinberg</FamilyName>
</EditorName>
</Editor>
<Editor><EditorName DisplayOrder="Western"><GivenName>Friedemann</GivenName>
<FamilyName>Mattern</FamilyName>
</EditorName>
</Editor>
<Editor><EditorName DisplayOrder="Western"><GivenName>John</GivenName>
<GivenName>C.</GivenName>
<FamilyName>Mitchell</FamilyName>
</EditorName>
</Editor>
<Editor><EditorName DisplayOrder="Western"><GivenName>Moni</GivenName>
<FamilyName>Naor</FamilyName>
</EditorName>
</Editor>
<Editor><EditorName DisplayOrder="Western"><GivenName>Oscar</GivenName>
<FamilyName>Nierstrasz</FamilyName>
</EditorName>
</Editor>
<Editor><EditorName DisplayOrder="Western"><GivenName>C.</GivenName>
<FamilyName>Pandu Rangan</FamilyName>
</EditorName>
</Editor>
<Editor><EditorName DisplayOrder="Western"><GivenName>Bernhard</GivenName>
<FamilyName>Steffen</FamilyName>
</EditorName>
</Editor>
<Editor><EditorName DisplayOrder="Western"><GivenName>Madhu</GivenName>
<FamilyName>Sudan</FamilyName>
</EditorName>
</Editor>
<Editor><EditorName DisplayOrder="Western"><GivenName>Demetri</GivenName>
<FamilyName>Terzopoulos</FamilyName>
</EditorName>
</Editor>
<Editor><EditorName DisplayOrder="Western"><GivenName>Doug</GivenName>
<FamilyName>Tygar</FamilyName>
</EditorName>
</Editor>
<Editor><EditorName DisplayOrder="Western"><GivenName>Moshe</GivenName>
<GivenName>Y.</GivenName>
<FamilyName>Vardi</FamilyName>
</EditorName>
</Editor>
<Editor><EditorName DisplayOrder="Western"><GivenName>Gerhard</GivenName>
<FamilyName>Weikum</FamilyName>
</EditorName>
</Editor>
</EditorGroup>
</SeriesHeader>
<Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingStyle="Unnumbered" OutputMedium="All" TocLevels="0"><BookID>978-3-540-74198-5</BookID>
<BookTitle>Energy Minimization Methods in Computer Vision and Pattern Recognition</BookTitle>
<BookSubTitle>6th International Conference, EMMCVPR 2007, Ezhou, China, August 27-29, 2007. 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 type="volume"><number>4679</number>
<caption>vol.</caption>
</detail>
<extent unit="pages"><start>242</start>
<end>252</end>
</extent>
</part>
<recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2007</recordOrigin>
</recordInfo>
</relatedItem>
<relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title>
</titleInfo>
<name type="personal"><namePart type="given">David</namePart>
<namePart type="family">Hutchison</namePart>
<role><roleTerm type="text">editor</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">Takeo</namePart>
<namePart type="family">Kanade</namePart>
<role><roleTerm type="text">editor</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">Josef</namePart>
<namePart type="family">Kittler</namePart>
<role><roleTerm type="text">editor</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">Jon</namePart>
<namePart type="given">M.</namePart>
<namePart type="family">Kleinberg</namePart>
<role><roleTerm type="text">editor</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">Friedemann</namePart>
<namePart type="family">Mattern</namePart>
<role><roleTerm type="text">editor</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">John</namePart>
<namePart type="given">C.</namePart>
<namePart type="family">Mitchell</namePart>
<role><roleTerm type="text">editor</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">Moni</namePart>
<namePart type="family">Naor</namePart>
<role><roleTerm type="text">editor</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">Oscar</namePart>
<namePart type="family">Nierstrasz</namePart>
<role><roleTerm type="text">editor</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">C.</namePart>
<namePart type="family">Pandu Rangan</namePart>
<role><roleTerm type="text">editor</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">Bernhard</namePart>
<namePart type="family">Steffen</namePart>
<role><roleTerm type="text">editor</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">Madhu</namePart>
<namePart type="family">Sudan</namePart>
<role><roleTerm type="text">editor</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">Demetri</namePart>
<namePart type="family">Terzopoulos</namePart>
<role><roleTerm type="text">editor</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">Doug</namePart>
<namePart type="family">Tygar</namePart>
<role><roleTerm type="text">editor</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">Moshe</namePart>
<namePart type="given">Y.</namePart>
<namePart type="family">Vardi</namePart>
<role><roleTerm type="text">editor</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">Gerhard</namePart>
<namePart type="family">Weikum</namePart>
<role><roleTerm type="text">editor</roleTerm>
</role>
</name>
<originInfo><copyrightDate encoding="w3cdtf">2007</copyrightDate>
<issuance>serial</issuance>
</originInfo>
<identifier type="ISSN">0302-9743</identifier>
<identifier type="eISSN">1611-3349</identifier>
<identifier type="SeriesID">558</identifier>
<recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2007</recordOrigin>
</recordInfo>
</relatedItem>
<identifier type="istex">96F4EF5B76A2D28F2FA091A5285A1E4157017109</identifier>
<identifier type="DOI">10.1007/978-3-540-74198-5_19</identifier>
<identifier type="ChapterID">19</identifier>
<identifier type="ChapterID">Chap19</identifier>
<accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag Berlin Heidelberg, 2007</accessCondition>
<recordInfo><recordContentSource>SPRINGER</recordContentSource>
<recordOrigin>Springer-Verlag Berlin Heidelberg, 2007</recordOrigin>
</recordInfo>
</mods>
</metadata>
<enrichments><istex:refBibTEI uri="https://api.istex.fr/document/96F4EF5B76A2D28F2FA091A5285A1E4157017109/enrichments/refBib"><teiHeader></teiHeader>
<text><front></front>
<body></body>
<back><listBibl><biblStruct xml:id="b0"><analytic><title level="a" type="main">A review on image segmentation techniques</title>
<author><persName><forename type="first">N</forename>
<forename type="middle">R</forename>
<surname>Pal</surname>
</persName>
</author>
<author><persName><forename type="first">S</forename>
<forename type="middle">K</forename>
<surname>Pal</surname>
</persName>
</author>
</analytic>
<monogr><title level="j">Pattern Recognition</title>
<imprint><biblScope unit="volume">26</biblScope>
<biblScope unit="issue">9</biblScope>
<biblScope unit="page" from="1277" to="1294"></biblScope>
<date type="published" when="1993"></date>
</imprint>
</monogr>
</biblStruct>
<biblStruct xml:id="b1"><analytic><title level="a" type="main">A fuzzy region growing approach for segmentation of color images</title>
<author><persName><forename type="first">A</forename>
<surname>Moghaddamzadeh</surname>
</persName>
</author>
<author><persName><forename type="first">N</forename>
<surname>Bourbakis</surname>
</persName>
</author>
</analytic>
<monogr><title level="j">Pattern Recognition</title>
<imprint><biblScope unit="volume">30</biblScope>
<biblScope unit="issue">6</biblScope>
<biblScope unit="page" from="867" to="881"></biblScope>
<date type="published" when="1997"></date>
</imprint>
</monogr>
</biblStruct>
<biblStruct xml:id="b2"><analytic><title level="a" type="main">A new method of color image segmentation based on intensity and hue clustering</title>
<author><persName><forename type="first">C</forename>
<surname>Zhang</surname>
</persName>
</author>
<author><persName><forename type="first">P</forename>
<surname>Wang</surname>
</persName>
</author>
</analytic>
<monogr><title level="m">IEEE Int. Conference on Pattern Recognition</title>
<imprint><date type="published" when="2000"></date>
<biblScope unit="page" from="3617" to="3621"></biblScope>
</imprint>
</monogr>
</biblStruct>
<biblStruct xml:id="b3"><analytic><title level="a" type="main">Natural color image segmentation</title>
<author><persName><forename type="first">X</forename>
<surname>Jie</surname>
</persName>
</author>
<author><persName><forename type="first">S</forename>
<surname>Peng-Fei</surname>
</persName>
</author>
</analytic>
<monogr><title level="m">IEEE Int. conference on Image Processing</title>
<imprint><date type="published" when="2003"></date>
<biblScope unit="page" from="14" to="17"></biblScope>
</imprint>
</monogr>
</biblStruct>
<biblStruct xml:id="b4"><analytic><title level="a" type="main">Color image segmentation: a state-of-the-art survey</title>
<author><persName><forename type="first">L</forename>
<surname>Lucchese</surname>
</persName>
</author>
<author><persName><forename type="first">S</forename>
<forename type="middle">K</forename>
<surname>Mitra</surname>
</persName>
</author>
</analytic>
<monogr><title level="j">Pro. of the Indian National Science Academy</title>
<imprint><biblScope unit="volume">67</biblScope>
<biblScope unit="page" from="207" to="221"></biblScope>
<date type="published" when="2001"></date>
</imprint>
</monogr>
</biblStruct>
<biblStruct xml:id="b5"><analytic><title level="a" type="main">Locating text in complex color images</title>
<author><persName><forename type="first">Y</forename>
<surname>Zhong</surname>
</persName>
</author>
<author><persName><forename type="first">K</forename>
<surname>Karu</surname>
</persName>
</author>
<author><persName><forename type="first">A</forename>
<forename type="middle">K</forename>
<surname>Jain</surname>
</persName>
</author>
</analytic>
<monogr><title level="j">Pattern Recognition</title>
<imprint><biblScope unit="volume">28</biblScope>
<biblScope unit="page" from="1523" to="1536"></biblScope>
<date type="published" when="1995"></date>
</imprint>
</monogr>
</biblStruct>
<biblStruct xml:id="b6"><analytic><title level="a" type="main">Automatic Text location in images and video frames</title>
<author><persName><forename type="first">K</forename>
<surname>Jain</surname>
</persName>
</author>
<author><persName><forename type="first">B</forename>
<surname>Yu</surname>
</persName>
</author>
</analytic>
<monogr><title level="j">Pattern Recognition</title>
<imprint><biblScope unit="volume">31</biblScope>
<biblScope unit="page" from="2055" to="2076"></biblScope>
<date type="published" when="1998"></date>
</imprint>
</monogr>
</biblStruct>
<biblStruct xml:id="b7"><analytic><title level="a" type="main">Scene text extraction and translation for handheld devices</title>
<author><persName><forename type="first">I</forename>
<surname>Haritaoglu</surname>
</persName>
</author>
</analytic>
<monogr><title level="m">IEEE Conference on Computer Vision and Pattern Recognition</title>
<imprint><date type="published" when="2001"></date>
<biblScope unit="page" from="408" to="413"></biblScope>
</imprint>
</monogr>
</biblStruct>
<biblStruct xml:id="b8"><analytic><title level="a" type="main">Automatic text segmentation from complex background</title>
<author><persName><forename type="first">Q</forename>
<surname>Ye</surname>
</persName>
</author>
<author><persName><forename type="first">W</forename>
<surname>Gao</surname>
</persName>
</author>
<author><persName><forename type="first">Q</forename>
<surname>Huang</surname>
</persName>
</author>
</analytic>
<monogr><title level="m">IEEE Int. Conference on Image Processing</title>
<imprint><date type="published" when="2004"></date>
<biblScope unit="page" from="2905" to="2908"></biblScope>
</imprint>
</monogr>
</biblStruct>
<biblStruct xml:id="b9"><analytic><title level="a" type="main">ICDAR: 2003 robust reading competitions</title>
<author><persName><forename type="first">S</forename>
<forename type="middle">M</forename>
<surname>Lucas</surname>
</persName>
</author>
<author><persName><forename type="first">A</forename>
<surname>Panaretos</surname>
</persName>
</author>
<author><persName><forename type="first">L</forename>
<surname>Sosa</surname>
</persName>
</author>
<author><persName><forename type="first">A</forename>
<surname>Tang</surname>
</persName>
</author>
<author><persName><forename type="first">S</forename>
<surname>Wong</surname>
</persName>
</author>
<author><persName><forename type="first">R</forename>
<surname>Young</surname>
</persName>
</author>
</analytic>
<monogr><title level="m">IEEE Int. Conference on Document Analysis and Recognition</title>
<imprint><date type="published" when="2003"></date>
<biblScope unit="page" from="682" to="687"></biblScope>
</imprint>
</monogr>
</biblStruct>
<biblStruct xml:id="b10"><monogr><title level="m" type="main">A Computational Framework for Segmentation and Grouping</title>
<author><persName><forename type="first">G</forename>
<surname>Medioni</surname>
</persName>
</author>
<author><persName><forename type="first">M</forename>
<forename type="middle">S</forename>
<surname>Lee</surname>
</persName>
</author>
<author><persName><forename type="first">C</forename>
<forename type="middle">K</forename>
<surname>Tang</surname>
</persName>
</author>
<imprint><date type="published" when="2000"></date>
<publisher>Elsevier</publisher>
<pubPlace>Amsterdam</pubPlace>
</imprint>
</monogr>
</biblStruct>
<biblStruct xml:id="b11"><analytic><title level="a" type="main">Grouping.,-,AE, into regions, curves, and junctions</title>
<author><persName><forename type="first">M</forename>
<forename type="middle">S</forename>
<surname>Lee</surname>
</persName>
</author>
<author><persName><forename type="first">G</forename>
<surname>Medioni</surname>
</persName>
</author>
</analytic>
<monogr><title level="j">Computer Vision and Image Understanding</title>
<imprint><biblScope unit="volume">76</biblScope>
<biblScope unit="issue">1</biblScope>
<biblScope unit="page" from="54" to="69"></biblScope>
<date type="published" when="1999"></date>
</imprint>
</monogr>
</biblStruct>
<biblStruct xml:id="b12"><analytic><title level="a" type="main">Text segmentation in color images using tensor voting</title>
<author><persName><forename type="first">J</forename>
<forename type="middle">G</forename>
<surname>Lim</surname>
</persName>
</author>
<author><persName><forename type="first">J</forename>
<forename type="middle">H</forename>
<surname>Park</surname>
</persName>
</author>
<author><persName><forename type="first">G</forename>
<forename type="middle">G</forename>
<surname>Medioni</surname>
</persName>
</author>
</analytic>
<monogr><title level="j">Image and Vision Computing</title>
<imprint><biblScope unit="volume">25</biblScope>
<biblScope unit="page" from="671" to="685"></biblScope>
<date type="published" when="2007"></date>
</imprint>
</monogr>
</biblStruct>
<biblStruct xml:id="b13"><analytic><title level="a" type="main">Vector sigma filters for noise detection and removal in color images</title>
<author><persName><forename type="first">R</forename>
<surname>Lukac</surname>
</persName>
</author>
<author><persName><forename type="first">B</forename>
<surname>Smolka</surname>
</persName>
</author>
<author><persName><forename type="first">K</forename>
<forename type="middle">N</forename>
<surname>Plataniotis</surname>
</persName>
</author>
<author><persName><forename type="first">A</forename>
<forename type="middle">N</forename>
<surname>Venetsanopoulos</surname>
</persName>
</author>
</analytic>
<monogr><title level="j">Journal of Visual Communication and Image Representation</title>
<imprint><biblScope unit="volume">17</biblScope>
<biblScope unit="page" from="1" to="26"></biblScope>
<date type="published" when="2006"></date>
</imprint>
</monogr>
</biblStruct>
<biblStruct xml:id="b14"><analytic><title level="a" type="main">A statistically-switched adaptive vector median filter</title>
<author><persName><forename type="first">R</forename>
<surname>Lukac</surname>
</persName>
</author>
<author><persName><forename type="first">K</forename>
<forename type="middle">N</forename>
<surname>Plataniotis</surname>
</persName>
</author>
<author><persName><forename type="first">A</forename>
<forename type="middle">N</forename>
<surname>Venetsanopoulos</surname>
</persName>
</author>
<author><persName><forename type="first">B</forename>
<surname>Smolka</surname>
</persName>
</author>
</analytic>
<monogr><title level="j">J. Intell. Robotic System</title>
<imprint><biblScope unit="volume">42</biblScope>
<biblScope unit="page" from="361" to="391"></biblScope>
<date type="published" when="2005"></date>
</imprint>
</monogr>
</biblStruct>
<biblStruct xml:id="b15"><analytic><title level="a" type="main">Mean shift: a robust approach towards feature space analysis</title>
<author><persName><forename type="first">D</forename>
<surname>Comaniciu</surname>
</persName>
</author>
<author><persName><forename type="first">P</forename>
<surname>Meer</surname>
</persName>
</author>
</analytic>
<monogr><title level="j">IEEE Tran. on Pattern Analysis and Machine Intelligence</title>
<imprint><biblScope unit="volume">24</biblScope>
<biblScope unit="issue">5</biblScope>
<biblScope unit="page" from="1" to="18"></biblScope>
<date type="published" when="2001"></date>
</imprint>
</monogr>
</biblStruct>
<biblStruct xml:id="b16"><analytic><title level="a" type="main">Deterministic annealing EM and its application in natural image segmentation</title>
<author><persName><forename type="first">J</forename>
<forename type="middle">H</forename>
<surname>Park</surname>
</persName>
</author>
<author><persName><forename type="first">W</forename>
<forename type="middle">H</forename>
<surname>Cho</surname>
</persName>
</author>
<author><persName><forename type="first">S</forename>
<forename type="middle">Y</forename>
<surname>Park</surname>
</persName>
</author>
</analytic>
<monogr><title level="m">CIS 2004</title>
<editor>Zhang, J., He, J.-H., Fu, Y.</editor>
<meeting><address><addrLine>Heidelberg</addrLine>
</address>
</meeting>
<imprint><publisher>Springer</publisher>
<date type="published" when="2004"></date>
<biblScope unit="page" from="639" to="644"></biblScope>
</imprint>
</monogr>
</biblStruct>
</listBibl>
</back>
</text>
</istex:refBibTEI>
</enrichments>
</istex>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/OcrV1/Data/Istex/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000958 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Istex/Corpus/biblio.hfd -nk 000958 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Ticri/CIDE |area= OcrV1 |flux= Istex |étape= Corpus |type= RBID |clé= ISTEX:96F4EF5B76A2D28F2FA091A5285A1E4157017109 |texte= Noise Removal and Restoration Using Voting-Based Analysis and Image Segmentation Based on Statistical Models }}
![]() | This area was generated with Dilib version V0.6.32. | ![]() |