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Noise suppression over bi-level graphical documents by sparse representation

Identifieur interne : 001C10 ( Main/Exploration ); précédent : 001C09; suivant : 001C11

Noise suppression over bi-level graphical documents by sparse representation

Auteurs : Thanh Ha Do [France] ; Salvatore Tabbone [France] ; Oriol Ramos-Terrades [Espagne]

Source :

RBID : Hal:hal-00759555

English descriptors

Abstract

In this paper, we explore the use of learning algorithm (K-SVD) for building dictionaries adapted to the image properties. In addition, in our model, we also modeled the energy of the noise basing on the function of the normalized cross-correlation between noised and non noised documents identified in training set. We have evaluated this method on the Grec2005 dataset. The experimental results demonstrate the robustness of our approach by comparing it with state-of-the-art methods.

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Le document en format XML

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<addrLine>Edifici O - Campus UAB - 08193 Bellaterra (Cerdanyola) - Barcelona, Spain</addrLine>
<country key="ES"></country>
</address>
<ref type="url">http://www.cvc.uab.es/</ref>
</desc>
<listRelation>
<relation active="#struct-98227" type="direct"></relation>
</listRelation>
</org>
</tutelle>
<tutelle active="#struct-98227" type="indirect">
<org type="institution" xml:id="struct-98227" status="VALID">
<orgName>Universitat Autònoma de Barcelona [Barcelona]</orgName>
<orgName type="acronym">UAB</orgName>
<desc>
<address>
<addrLine>UAB Campus 08193 Bellaterra Barcelona</addrLine>
<country key="ES"></country>
</address>
<ref type="url">http://www.uab.es/english/</ref>
</desc>
</org>
</tutelle>
</tutelles>
</hal:affiliation>
<country>Espagne</country>
</affiliation>
</author>
</analytic>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc>
<textClass>
<keywords scheme="mix" xml:lang="en">
<term>K-SVD</term>
<term>Sparse representation</term>
<term>graphical documents</term>
<term>learned dictionary</term>
<term>noise removal</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">In this paper, we explore the use of learning algorithm (K-SVD) for building dictionaries adapted to the image properties. In addition, in our model, we also modeled the energy of the noise basing on the function of the normalized cross-correlation between noised and non noised documents identified in training set. We have evaluated this method on the Grec2005 dataset. The experimental results demonstrate the robustness of our approach by comparing it with state-of-the-art methods.</div>
</front>
</TEI>
<affiliations>
<list>
<country>
<li>Espagne</li>
<li>France</li>
</country>
<region>
<li>Grand Est</li>
<li>Lorraine (région)</li>
</region>
<settlement>
<li>Metz</li>
<li>Nancy</li>
</settlement>
<orgName>
<li>Université de Lorraine</li>
</orgName>
</list>
<tree>
<country name="France">
<region name="Grand Est">
<name sortKey="Do, Thanh Ha" sort="Do, Thanh Ha" uniqKey="Do T" first="Thanh Ha" last="Do">Thanh Ha Do</name>
</region>
<name sortKey="Tabbone, Salvatore" sort="Tabbone, Salvatore" uniqKey="Tabbone S" first="Salvatore" last="Tabbone">Salvatore Tabbone</name>
</country>
<country name="Espagne">
<noRegion>
<name sortKey="Ramos Terrades, Oriol" sort="Ramos Terrades, Oriol" uniqKey="Ramos Terrades O" first="Oriol" last="Ramos-Terrades">Oriol Ramos-Terrades</name>
</noRegion>
</country>
</tree>
</affiliations>
</record>

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