A Probabilistic Grouping Principle to Go from Pixels to Visual Structures
Identifieur interne : 001C62 ( Istex/Corpus ); précédent : 001C61; suivant : 001C63A Probabilistic Grouping Principle to Go from Pixels to Visual Structures
Auteurs : Agnès DesolneuxSource :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: We will describe here how the Helmholtz principle, which is a principle of visual perception, can be translated into a computational tool that can be used for many problems of discrete image analysis. The Helmholtz principle can be formulated as “we immediately perceive whatever has a low likelihood of resulting from accidental arrangement”. To translate this principle into a computational tool, we will introduce a variable called NFA (Number of False Alarms) associated to any geometric event in an image. The NFA of an event is defined as the expectation of the number of occurrences of this event in a pure noise image of same size. Meaningful events will then be events with a very low NFA. We will see how this notion can be efficiently used in many detection problems (alignments, smooth curves, edges, etc.). The common framework of these detection problems is that they can all be translated into the question of knowing whether a given group of pixels is meaningful or not. This is a joint work with Lionel Moisan and Jean-Michel Morel.
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DOI: 10.1007/978-3-642-19867-0_1
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<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">A Probabilistic Grouping Principle to Go from Pixels to Visual Structures</title><author><name sortKey="Desolneux, Agnes" sort="Desolneux, Agnes" uniqKey="Desolneux A" first="Agnès" last="Desolneux">Agnès Desolneux</name><affiliation><mods:affiliation>MAP5 (UMR CNRS 8145), University Paris Descartes, 45 rue des Saints-Pères, 75006, Paris, France</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:7B30D4A5A8C4B9D1EDA215F55BBB42FE3607B8D3</idno><date when="2011" year="2011">2011</date><idno type="doi">10.1007/978-3-642-19867-0_1</idno><idno type="url">https://api.istex.fr/ark:/67375/HCB-BCXNGG8T-T/fulltext.pdf</idno><idno type="wicri:Area/Istex/Corpus">001C62</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">001C62</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">A Probabilistic Grouping Principle to Go from Pixels to Visual Structures</title><author><name sortKey="Desolneux, Agnes" sort="Desolneux, Agnes" uniqKey="Desolneux A" first="Agnès" last="Desolneux">Agnès Desolneux</name><affiliation><mods:affiliation>MAP5 (UMR CNRS 8145), University Paris Descartes, 45 rue des Saints-Pères, 75006, Paris, France</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="s" type="main" xml:lang="en">Lecture Notes in Computer Science</title><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: We will describe here how the Helmholtz principle, which is a principle of visual perception, can be translated into a computational tool that can be used for many problems of discrete image analysis. The Helmholtz principle can be formulated as “we immediately perceive whatever has a low likelihood of resulting from accidental arrangement”. To translate this principle into a computational tool, we will introduce a variable called NFA (Number of False Alarms) associated to any geometric event in an image. The NFA of an event is defined as the expectation of the number of occurrences of this event in a pure noise image of same size. Meaningful events will then be events with a very low NFA. We will see how this notion can be efficiently used in many detection problems (alignments, smooth curves, edges, etc.). The common framework of these detection problems is that they can all be translated into the question of knowing whether a given group of pixels is meaningful or not. 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The Helmholtz principle can be formulated as “we immediately perceive whatever has a low likelihood of resulting from accidental arrangement”. To translate this principle into a computational tool, we will introduce a variable called NFA (Number of False Alarms) associated to any geometric event in an image. The NFA of an event is defined as the expectation of the number of occurrences of this event in a pure noise image of same size. Meaningful events will then be events with a very low NFA. We will see how this notion can be efficiently used in many detection problems (alignments, smooth curves, edges, etc.). The common framework of these detection problems is that they can all be translated into the question of knowing whether a given group of pixels is meaningful or not. 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STATES</country></address></affiliation></editor><editor><persName><forename type="first">Doug</forename><surname>Tygar</surname></persName><affiliation><orgName type="institution">University of California</orgName><address><settlement>Berkeley</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Moshe</forename><forename type="first">Y.</forename><surname>Vardi</surname></persName><affiliation><orgName type="institution">Rice University</orgName><address><settlement>Houston</settlement><region>TX</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Gerhard</forename><surname>Weikum</surname></persName><affiliation><orgName type="institution">Max-Planck Institute of Computer Science</orgName><address><settlement>Saarbrücken</settlement><country key="DE">GERMANY</country></address></affiliation></editor><idno 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The Helmholtz principle can be formulated as “we immediately perceive whatever has a low likelihood of resulting from accidental arrangement”. To translate this principle into a computational tool, we will introduce a variable called NFA (Number of False Alarms) associated to any geometric event in an image. The NFA of an event is defined as the expectation of the number of occurrences of this event in a pure noise image of same size. Meaningful events will then be events with a very low NFA. We will see how this notion can be efficiently used in many detection problems (alignments, smooth curves, edges, etc.). The common framework of these detection problems is that they can all be translated into the question of knowing whether a given group of pixels is meaningful or not. This is a joint work with Lionel Moisan and Jean-Michel Morel.</p></abstract><textClass ana="keyword"><keywords xml:lang="en"><term>grouping laws</term><term>Gestalt theory</term><term>Helmholtz principle</term><term>rare events</term><term>alignments</term><term>edge detection</term><term>segmentation</term></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject-collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject"><list><label>I</label><item><term type="Primary">Computer Science</term></item><label>I22013</label><item><term type="Secondary" subtype="priority-1">Computer Graphics</term></item><label>I22021</label><item><term type="Secondary" subtype="priority-2">Image Processing and Computer Vision</term></item><label>I22005</label><item><term type="Secondary" subtype="priority-3">Computer Imaging, Vision, Pattern Recognition and 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University</OrgName><OrgAddress><City>Lancaster</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgName>Carnegie Mellon University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgName>University of Surrey</OrgName><OrgAddress><City>Guildford</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgName>Cornell University</OrgName><OrgAddress><City>Ithaca</City><State>NY</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff5"><OrgName>ETH Zurich</OrgName><OrgAddress><City>Zurich</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff6"><OrgName>Stanford University</OrgName><OrgAddress><City>Stanford</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgName>Weizmann Institute of Science</OrgName><OrgAddress><City>Rehovot</City><Country>Israel</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><City>Bern</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><City>Dortmund</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><State>MA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>University of California</OrgName><OrgAddress><City>Los Angeles</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-642-19867-0</BookID><BookTitle>Discrete Geometry for Computer Imagery</BookTitle><BookSubTitle>16th IAPR International Conference, DGCI 2011, Nancy, France, April 6-8, 2011. Proceedings</BookSubTitle><BookVolumeNumber>6607</BookVolumeNumber><BookSequenceNumber>6607</BookSequenceNumber><BookDOI>10.1007/978-3-642-19867-0</BookDOI><BookTitleID>217662</BookTitleID><BookPrintISBN>978-3-642-19866-3</BookPrintISBN><BookElectronicISBN>978-3-642-19867-0</BookElectronicISBN><BookChapterCount>43</BookChapterCount><BookCopyright><CopyrightHolderName>Springer Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2011</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I22013" Priority="1" Type="Secondary">Computer Graphics</BookSubject><BookSubject Code="I22021" Priority="2" Type="Secondary">Image Processing and Computer Vision</BookSubject><BookSubject Code="I22005" Priority="3" Type="Secondary">Computer Imaging, Vision, Pattern Recognition and Graphics</BookSubject><BookSubject Code="I2203X" Priority="4" Type="Secondary">Pattern Recognition</BookSubject><BookSubject Code="I17028" Priority="5" Type="Secondary">Discrete Mathematics in Computer Science</BookSubject><BookSubject Code="I16021" Priority="6" Type="Secondary">Algorithm Analysis and Problem Complexity</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Isabelle</GivenName><FamilyName>Debled-Rennesson</FamilyName></EditorName><Contact><Email>isabelle.debled-rennesson@loria.fr</Email></Contact></Editor><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Eric</GivenName><FamilyName>Domenjoud</FamilyName></EditorName><Contact><Email>eric.domenjoud@loria.fr</Email></Contact></Editor><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Bertrand</GivenName><FamilyName>Kerautret</FamilyName></EditorName><Contact><Email>bertrand.kerautret@loria.fr</Email></Contact></Editor><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Philippe</GivenName><FamilyName>Even</FamilyName></EditorName><Contact><Email>philippe.even@loria.fr</Email></Contact></Editor><Affiliation ID="Aff16"><OrgName>LORIA, Equipe ADAGIo</OrgName><OrgAddress><Street>Campus Scientifique, BP 239</Street><Postcode>54506</Postcode><City>Vandœuvre-lès-Nancy Cedex</City><Country>France</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part1"><PartInfo TocLevels="0"><PartID>1</PartID><PartSequenceNumber>1</PartSequenceNumber><PartTitle>Invited Speakers</PartTitle><PartChapterCount>3</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Discrete Geometry for Computer Imagery</BookTitle></PartContext></PartInfo><Chapter ID="Chap1" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>1</ChapterID><ChapterDOI>10.1007/978-3-642-19867-0_1</ChapterDOI><ChapterSequenceNumber>1</ChapterSequenceNumber><ChapterTitle Language="En">A Probabilistic Grouping Principle to Go from Pixels to Visual Structures</ChapterTitle><ChapterFirstPage>1</ChapterFirstPage><ChapterLastPage>12</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2011</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>1</PartID><BookID>978-3-642-19867-0</BookID><BookTitle>Discrete Geometry for Computer Imagery</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff17"><AuthorName DisplayOrder="Western"><GivenName>Agnès</GivenName><FamilyName>Desolneux</FamilyName></AuthorName></Author><Affiliation ID="Aff17"><OrgDivision>MAP5 (UMR CNRS 8145)</OrgDivision><OrgName>University Paris Descartes</OrgName><OrgAddress><Street>45 rue des Saints-Pères</Street><Postcode>75006</Postcode><City>Paris</City><Country>France</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>We will describe here how the Helmholtz principle, which is a principle of visual perception, can be translated into a computational tool that can be used for many problems of discrete image analysis. The Helmholtz principle can be formulated as “we immediately perceive whatever has a low likelihood of resulting from accidental arrangement”. To translate this principle into a computational tool, we will introduce a variable called NFA (Number of False Alarms) associated to any geometric event in an image. The NFA of an event is defined as the expectation of the number of occurrences of this event in a pure noise image of same size. Meaningful events will then be events with a very low NFA. We will see how this notion can be efficiently used in many detection problems (alignments, smooth curves, edges, etc.). The common framework of these detection problems is that they can all be translated into the question of knowing whether a given group of pixels is meaningful or not. This is a joint work with Lionel Moisan and Jean-Michel Morel.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>grouping laws</Keyword><Keyword>Gestalt theory</Keyword><Keyword>Helmholtz principle</Keyword><Keyword>rare events</Keyword><Keyword>alignments</Keyword><Keyword>edge detection</Keyword><Keyword>segmentation</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>A Probabilistic Grouping Principle to Go from Pixels to Visual Structures</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>A Probabilistic Grouping Principle to Go from Pixels to Visual Structures</title></titleInfo><name type="personal"><namePart type="given">Agnès</namePart><namePart type="family">Desolneux</namePart><affiliation>MAP5 (UMR CNRS 8145), University Paris Descartes, 45 rue des Saints-Pères, 75006, Paris, France</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" type="conference" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2011</dateIssued><copyrightDate encoding="w3cdtf">2011</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: We will describe here how the Helmholtz principle, which is a principle of visual perception, can be translated into a computational tool that can be used for many problems of discrete image analysis. The Helmholtz principle can be formulated as “we immediately perceive whatever has a low likelihood of resulting from accidental arrangement”. To translate this principle into a computational tool, we will introduce a variable called NFA (Number of False Alarms) associated to any geometric event in an image. The NFA of an event is defined as the expectation of the number of occurrences of this event in a pure noise image of same size. Meaningful events will then be events with a very low NFA. We will see how this notion can be efficiently used in many detection problems (alignments, smooth curves, edges, etc.). The common framework of these detection problems is that they can all be translated into the question of knowing whether a given group of pixels is meaningful or not. This is a joint work with Lionel Moisan and Jean-Michel Morel.</abstract><subject lang="en"><genre>Keywords</genre><topic>grouping laws</topic><topic>Gestalt theory</topic><topic>Helmholtz principle</topic><topic>rare events</topic><topic>alignments</topic><topic>edge detection</topic><topic>segmentation</topic></subject><relatedItem type="host"><titleInfo><title>Discrete Geometry for Computer Imagery</title><subTitle>16th IAPR International Conference, DGCI 2011, Nancy, France, April 6-8, 2011. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Isabelle</namePart><namePart type="family">Debled-Rennesson</namePart><affiliation>LORIA, Equipe ADAGIo, Campus Scientifique, BP 239, 54506, Vandœuvre-lès-Nancy Cedex, France</affiliation><affiliation>E-mail: isabelle.debled-rennesson@loria.fr</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Eric</namePart><namePart type="family">Domenjoud</namePart><affiliation>LORIA, Equipe ADAGIo, Campus Scientifique, BP 239, 54506, Vandœuvre-lès-Nancy Cedex, France</affiliation><affiliation>E-mail: eric.domenjoud@loria.fr</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Bertrand</namePart><namePart type="family">Kerautret</namePart><affiliation>LORIA, Equipe ADAGIo, Campus Scientifique, BP 239, 54506, Vandœuvre-lès-Nancy Cedex, France</affiliation><affiliation>E-mail: bertrand.kerautret@loria.fr</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Philippe</namePart><namePart type="family">Even</namePart><affiliation>LORIA, Equipe ADAGIo, Campus Scientifique, BP 239, 54506, Vandœuvre-lès-Nancy Cedex, France</affiliation><affiliation>E-mail: philippe.even@loria.fr</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0G6R5W5T-Z">book-series</genre><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">2011</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="I22013">Computer Graphics</topic><topic authority="SpringerSubjectCodes" authorityURI="I22021">Image Processing and Computer Vision</topic><topic authority="SpringerSubjectCodes" authorityURI="I22005">Computer Imaging, Vision, Pattern Recognition and Graphics</topic><topic authority="SpringerSubjectCodes" authorityURI="I2203X">Pattern Recognition</topic><topic authority="SpringerSubjectCodes" authorityURI="I17028">Discrete Mathematics in Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I16021">Algorithm Analysis and Problem Complexity</topic></subject><identifier type="DOI">10.1007/978-3-642-19867-0</identifier><identifier type="ISBN">978-3-642-19866-3</identifier><identifier type="eISBN">978-3-642-19867-0</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier 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type="given">Takeo</namePart><namePart type="family">Kanade</namePart><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Josef</namePart><namePart type="family">Kittler</namePart><affiliation>University of Surrey, Guildford, UK</affiliation><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><affiliation>Cornell University, Ithaca, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><affiliation>ETH Zurich, Zurich, Switzerland</affiliation><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><affiliation>Stanford University, Stanford, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moni</namePart><namePart type="family">Naor</namePart><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Oscar</namePart><namePart type="family">Nierstrasz</namePart><affiliation>University of Bern, Bern, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">C.</namePart><namePart type="family">Pandu Rangan</namePart><affiliation>Indian Institute of Technology, Madras, India</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Bernhard</namePart><namePart type="family">Steffen</namePart><affiliation>University of Dortmund, Dortmund, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Madhu</namePart><namePart type="family">Sudan</namePart><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Demetri</namePart><namePart type="family">Terzopoulos</namePart><affiliation>University of California, Los Angeles, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Doug</namePart><namePart type="family">Tygar</namePart><affiliation>University of California, Berkeley, CA, USA</affiliation><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><affiliation>Rice University, Houston, TX, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Weikum</namePart><affiliation>Max-Planck Institute of Computer Science, Saarbrücken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">2011</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 Berlin Heidelberg, 2011</recordOrigin></recordInfo></relatedItem><identifier type="istex">7B30D4A5A8C4B9D1EDA215F55BBB42FE3607B8D3</identifier><identifier type="ark">ark:/67375/HCB-BCXNGG8T-T</identifier><identifier type="DOI">10.1007/978-3-642-19867-0_1</identifier><identifier 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