Noise Removal and Restoration Using Voting-Based Analysis and Image Segmentation Based on Statistical Models
Identifieur interne : 000E35 ( Main/Merge ); précédent : 000E34; suivant : 000E36Noise Removal and Restoration Using Voting-Based Analysis and Image Segmentation Based on Statistical Models
Auteurs : Jonghyun Park [Corée du Sud] ; Trung Kien [Corée du Sud] ; Gueesang Lee [Corée du Sud]Source :
- 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.
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DOI: 10.1007/978-3-540-74198-5_19
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<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></author><author><name sortKey="Kien, Trung" sort="Kien, Trung" uniqKey="Kien T" first="Trung" last="Kien">Trung Kien</name></author><author><name sortKey="Lee, Gueesang" sort="Lee, Gueesang" uniqKey="Lee G" first="Gueesang" last="Lee">Gueesang Lee</name></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><idno type="wicri:Area/Istex/Curation">000948</idno><idno type="wicri:Area/Istex/Checkpoint">000837</idno><idno type="wicri:doubleKey">0302-9743:2007:Park J:noise:removal:and</idno><idno type="wicri:Area/Main/Merge">000E35</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 wicri:level="1"><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju</wicri:regionArea><wicri:noRegion>Gwangju</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Corée du Sud</country></affiliation></author><author><name sortKey="Kien, Trung" sort="Kien, Trung" uniqKey="Kien T" first="Trung" last="Kien">Trung Kien</name><affiliation wicri:level="1"><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju</wicri:regionArea><wicri:noRegion>Gwangju</wicri:noRegion></affiliation><affiliation><wicri:noCountry code="no comma">E-mail: trung_kien_kg@yahoo.com</wicri:noCountry></affiliation></author><author><name sortKey="Lee, Gueesang" sort="Lee, Gueesang" uniqKey="Lee G" first="Gueesang" last="Lee">Gueesang Lee</name><affiliation wicri:level="1"><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>Dept. of Computer Science, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju</wicri:regionArea><wicri:noRegion>Gwangju</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Corée du Sud</country></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></record>Pour manipuler ce document sous Unix (Dilib)
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