Meaningful Matches: Experiments on LLD and MSER
Identifieur interne : 002366 ( Istex/Corpus ); précédent : 002365; suivant : 002367Meaningful Matches: Experiments on LLD and MSER
Auteurs : Frédéric Cao ; José-Luis Lisani ; Jean-Michel Morel ; Pablo Musé ; Frédéric SurSource :
- Lecture Notes in Mathematics [ 0075-8434 ]
Abstract
This chapter tests the shape matching method described in the previous chapter. Section 6.1 deals with the semi-local invariant recognition method. Both similarity and affine methods are considered, and a comparative study based on examples is presented. When images differ by a similarity, affine matching usually returns less matches because affine encoding is more demanding. Nevertheless, affine encoding proves more robust as soon as there is a slight perspective effect, and yields much smaller NFAs.We will also test an improved MSER method (namely a global affine matching algorithm of closed level lines). This algorithm works but we will point out a problem with convex shapes, which turn out to be very hard to distinguish up to an affine transformation. Finally the context-dependence of recognition will be illustrated by striking experiments on character recognition. Now comes the time to check the applicability of the shape comparison scheme described in the previous chapters. All the experiments presented thereafter follow the same procedure: detection of meaningful boundaries (Chap. 2), affine invariant smoothing (Chap. 3, Sect. 3.3), similarity or affine normalization-encoding (Chap. 3 and 4), and then matching (Chap. 5).
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DOI: 10.1007/978-3-540-68481-7_6
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Nevertheless, affine encoding proves more robust as soon as there is a slight perspective effect, and yields much smaller NFAs.We will also test an improved MSER method (namely a global affine matching algorithm of closed level lines). This algorithm works but we will point out a problem with convex shapes, which turn out to be very hard to distinguish up to an affine transformation. Finally the context-dependence of recognition will be illustrated by striking experiments on character recognition. Now comes the time to check the applicability of the shape comparison scheme described in the previous chapters. 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Section 6.1 deals with the semi-local invariant recognition method. Both similarity and affine methods are considered, and a comparative study based on examples is presented. When images differ by a similarity, affine matching usually returns less matches because affine encoding is more demanding. Nevertheless, affine encoding proves more robust as soon as there is a slight perspective effect, and yields much smaller NFAs.We will also test an improved MSER method (namely a global affine matching algorithm of closed level lines). This algorithm works but we will point out a problem with convex shapes, which turn out to be very hard to distinguish up to an affine transformation. Finally the context-dependence of recognition will be illustrated by striking experiments on character recognition.</p><p>Now comes the time to check the applicability of the shape comparison scheme described in the previous chapters. 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Robotics)</BookSubject><BookSubject Code="SCI22005" Priority="5" Type="Secondary">Computer Imaging, Vision, Pattern Recognition and Graphics</BookSubject><BookSubject Code="SCM13011" Priority="6" Type="Secondary">Game Theory, Economics, Social and Behav. Sciences</BookSubject><SubjectCollection Code="SUCO11649">Mathematics and Statistics</SubjectCollection></BookSubjectGroup></BookInfo><BookHeader><AuthorGroup><Author AffiliationIDS="Aff4"><AuthorName DisplayOrder="Western"><GivenName>Frédéric</GivenName><FamilyName>Cao</FamilyName></AuthorName><Contact><Email>fcao@dxo.com</Email></Contact></Author><Author AffiliationIDS="Aff5"><AuthorName DisplayOrder="Western"><GivenName>José-Luis</GivenName><FamilyName>Lisani</FamilyName></AuthorName><Contact><Email>joseluis.lisani@uib.es</Email></Contact></Author><Author AffiliationIDS="Aff6"><AuthorName DisplayOrder="Western"><GivenName>Jean-Michel</GivenName><FamilyName>Morel</FamilyName></AuthorName><Contact><Email>morel@cmla.ens-cachan.fr</Email></Contact></Author><Author AffiliationIDS="Aff7"><AuthorName DisplayOrder="Western"><GivenName>Pablo</GivenName><FamilyName>Musé</FamilyName></AuthorName><Contact><Email>pmuse@fing.edu.uy</Email></Contact></Author><Author AffiliationIDS="Aff8"><AuthorName DisplayOrder="Western"><GivenName>Frédéric</GivenName><FamilyName>Sur</FamilyName></AuthorName><Contact><Email>sur@loria.fr</Email></Contact></Author><Affiliation ID="Aff4"><OrgName>DxO Labs</OrgName><OrgAddress><Street>3 rue Nationale</Street><Postcode>92100 Boulogne Billancourt</Postcode><Country>France</Country></OrgAddress></Affiliation><Affiliation ID="Aff5"><OrgDivision>Dep. Matemàtiques i Informàtica</OrgDivision><OrgName>University Balearic Islands</OrgName><OrgAddress><Street>ctra. Valldemossa km.7,5</Street><City>Balears</City><Postcode>07122 Palma de Mallorca</Postcode><Country>Spain</Country></OrgAddress></Affiliation><Affiliation ID="Aff6"><OrgDivision>Ecole Normale Supérieure de Cachan</OrgDivision><OrgName>CMLA</OrgName><OrgAddress><Street>61 av. du Président Wilson</Street><Postcode>94235 Cachan Cédex</Postcode><Country>France</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgName>Instituto de Ingeniería Eléctrica</OrgName><OrgAddress><Street>Julio Herrera y Reissig 565</Street><Postcode>11300 Montevideo</Postcode><Country>Uruguay</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>Loria Bat. C - projet Magrit Campus Scientifique</OrgName><OrgAddress><Street>54506 Vandoeuvre-lès-Nancy Cédex</Street><Postcode>BP 239</Postcode><Country>France</Country></OrgAddress></Affiliation></AuthorGroup></BookHeader><Part ID="Part3"><PartInfo TocLevels="0"><PartID>3</PartID><PartNumber>III</PartNumber><PartSequenceNumber>3</PartSequenceNumber><PartTitle>Recognizing Level Lines</PartTitle><PartChapterCount>2</PartChapterCount></PartInfo><Chapter ID="Chap6" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingStyle="ChapterContent" TocLevels="0"><ChapterID>6</ChapterID><ChapterNumber>6</ChapterNumber><ChapterDOI>10.1007/978-3-540-68481-7_6</ChapterDOI><ChapterSequenceNumber>6</ChapterSequenceNumber><ChapterTitle Language="En">Meaningful Matches: Experiments on LLD and MSER</ChapterTitle><ChapterFirstPage>93</ChapterFirstPage><ChapterLastPage>125</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2008</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>304</SeriesID><PartID>3</PartID><BookID>978-3-540-68481-7</BookID><BookTitle>A Theory of Shape Identification</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><Abstract ID="Abs1" Language="En"><Para TextBreak="No">This chapter tests the shape matching method described in the previous chapter. Section 6.1 deals with the semi-local invariant recognition method. Both similarity and affine methods are considered, and a comparative study based on examples is presented. When images differ by a similarity, affine matching usually returns less matches because affine encoding is more demanding. Nevertheless, affine encoding proves more robust as soon as there is a slight perspective effect, and yields much smaller NFAs.We will also test an improved MSER method (namely a global affine matching algorithm of closed level lines). This algorithm works but we will point out a problem with convex shapes, which turn out to be very hard to distinguish up to an affine transformation. Finally the context-dependence of recognition will be illustrated by striking experiments on character recognition.</Para><Para TextBreak="No">Now comes the time to check the applicability of the shape comparison scheme described in the previous chapters. All the experiments presented thereafter follow the same procedure: detection of meaningful boundaries (Chap. 2), affine invariant smoothing (Chap. 3, Sect. 3.3), similarity or affine normalization-encoding (Chap. 3 and 4), and then matching (Chap. 5).</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Meaningful Matches: Experiments on LLD and MSER</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Meaningful Matches: Experiments on LLD and MSER</title></titleInfo><name type="personal"><namePart type="given">Frédéric</namePart><namePart type="family">Cao</namePart><affiliation>DxO Labs, 3 rue Nationale, 92100 Boulogne Billancourt, France</affiliation><affiliation>E-mail: fcao@dxo.com</affiliation></name><name type="personal"><namePart type="given">José-Luis</namePart><namePart type="family">Lisani</namePart><affiliation>Dep. Matemàtiques i Informàtica, University Balearic Islands, ctra. Valldemossa km.7,5, 07122 Palma de Mallorca, Balears, Spain</affiliation><affiliation>E-mail: joseluis.lisani@uib.es</affiliation></name><name type="personal"><namePart type="given">Jean-Michel</namePart><namePart type="family">Morel</namePart><affiliation>Ecole Normale Supérieure de Cachan, CMLA, 61 av. du Président Wilson, 94235 Cachan Cédex, France</affiliation><affiliation>E-mail: morel@cmla.ens-cachan.fr</affiliation></name><name type="personal"><namePart type="given">Pablo</namePart><namePart type="family">Musé</namePart><affiliation>Instituto de Ingeniería Eléctrica, Julio Herrera y Reissig 565, 11300 Montevideo, Uruguay</affiliation><affiliation>E-mail: pmuse@fing.edu.uy</affiliation></name><name type="personal"><namePart type="given">Frédéric</namePart><namePart type="family">Sur</namePart><affiliation>Loria Bat. C - projet Magrit Campus Scientifique, 54506 Vandoeuvre-lès-Nancy Cédex, BP 239, France</affiliation><affiliation>E-mail: sur@loria.fr</affiliation></name><typeOfResource>text</typeOfResource><genre displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" type="research-article" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-1JC4F85T-7">research-article</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2008</dateIssued><copyrightDate encoding="w3cdtf">2008</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">This chapter tests the shape matching method described in the previous chapter. Section 6.1 deals with the semi-local invariant recognition method. Both similarity and affine methods are considered, and a comparative study based on examples is presented. When images differ by a similarity, affine matching usually returns less matches because affine encoding is more demanding. Nevertheless, affine encoding proves more robust as soon as there is a slight perspective effect, and yields much smaller NFAs.We will also test an improved MSER method (namely a global affine matching algorithm of closed level lines). This algorithm works but we will point out a problem with convex shapes, which turn out to be very hard to distinguish up to an affine transformation. Finally the context-dependence of recognition will be illustrated by striking experiments on character recognition. Now comes the time to check the applicability of the shape comparison scheme described in the previous chapters. All the experiments presented thereafter follow the same procedure: detection of meaningful boundaries (Chap. 2), affine invariant smoothing (Chap. 3, Sect. 3.3), similarity or affine normalization-encoding (Chap. 3 and 4), and then matching (Chap. 5).</abstract><relatedItem type="host"><titleInfo><title>A Theory of Shape Identification</title></titleInfo><name type="personal"><namePart type="given">Frédéric</namePart><namePart type="family">Cao</namePart><affiliation>DxO Labs, 3 rue Nationale, 92100 Boulogne Billancourt, France</affiliation><affiliation>E-mail: fcao@dxo.com</affiliation></name><name type="personal"><namePart type="given">José-Luis</namePart><namePart type="family">Lisani</namePart><affiliation>Dep. Matemàtiques i Informàtica, University Balearic Islands, ctra. Valldemossa km.7,5, 07122 Palma de Mallorca, Balears, Spain</affiliation><affiliation>E-mail: joseluis.lisani@uib.es</affiliation></name><name type="personal"><namePart type="given">Jean-Michel</namePart><namePart type="family">Morel</namePart><affiliation>Ecole Normale Supérieure de Cachan, CMLA, 61 av. du Président Wilson, 94235 Cachan Cédex, France</affiliation><affiliation>E-mail: morel@cmla.ens-cachan.fr</affiliation></name><name type="personal"><namePart type="given">Pablo</namePart><namePart type="family">Musé</namePart><affiliation>Instituto de Ingeniería Eléctrica, Julio Herrera y Reissig 565, 11300 Montevideo, Uruguay</affiliation><affiliation>E-mail: pmuse@fing.edu.uy</affiliation></name><name type="personal"><namePart type="given">Frédéric</namePart><namePart type="family">Sur</namePart><affiliation>Loria Bat. C - projet Magrit Campus Scientifique, 54506 Vandoeuvre-lès-Nancy Cédex, BP 239, France</affiliation><affiliation>E-mail: sur@loria.fr</affiliation></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">2008</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book-Subject-Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11649">Mathematics and Statistics</topic></subject><subject><genre>Book-Subject-Group</genre><topic authority="SpringerSubjectCodes" authorityURI="SCM">Mathematics</topic><topic authority="SpringerSubjectCodes" authorityURI="SCM21006">Geometry</topic><topic authority="SpringerSubjectCodes" authorityURI="SCM14034">Visualization</topic><topic authority="SpringerSubjectCodes" authorityURI="SCI22021">Image Processing and Computer Vision</topic><topic authority="SpringerSubjectCodes" authorityURI="SCI21017">Artificial Intelligence (incl. 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