Extracting Meaningful Curves from Images
Identifieur interne : 004501 ( Main/Merge ); précédent : 004500; suivant : 004502Extracting Meaningful Curves from Images
Auteurs : Frédéric Cao [France] ; José-Luis Lisani [Espagne] ; Jean-Michel Morel [France] ; Pablo Musé [Uruguay] ; Frédéric Sur [France]Source :
- Lecture Notes in Mathematics [ 0075-8434 ]
Abstract
The set of level lines of an image (isophotes) or topographic map is a complete and contrast invariant representation of an image. Level lines are ordered by inclusion in a tree structure. These two structure properties make level lines excellent candidates to shape representatives. However, some complexity issues have to be handled: The number of level lines in eight-bits encoded images of size 512×512 is typically 105. Most of them are very small lines due to noise or micro-texture. So the stable level lines must be selected, namely the ones that are likely to correspond to image contours. The starting point is the MSER method, a variant of the Monasse and Guichard Fast Level Set Transform. The MSER selects a set of level lines which are local extrema of contrast. This method will be put in the Helmholtz framework, following the a contrario boundary detection algorithm by Desolneux, Moisan and Morel [51], [54] and two powerful recent variants. The experiments in this chapter will show that selecting the most meaningful level lines reduces their number by a factor 100 without significant shape contents loss. A method which selects one out of hundred level lines in the image without significant information loss is necessarily sophisticated. Sect. 2.1 briefly reviews the level line tree of a digital image. Sect. 2.2 describes a first way to extract well contrasted level lines, the MSER method. Sect. 2.3 makes an account of the Desolneux et al. maximal meaningful boundaries and Sect. 2.4 gives a mathematical justification which was actually missing in the original theory. Sect. 2.5 is devoted to a multiscale extension which avoids missing boundaries because of high noise level and Sect. 2.6 deals with the so called “blue sky” effect which can lead to over-detections in textured parts of the image.
Url:
DOI: 10.1007/978-3-540-68481-7_2
Links toward previous steps (curation, corpus...)
- to stream Istex, to step Corpus: 000824
- to stream Istex, to step Curation: 000819
- to stream Istex, to step Checkpoint: 000E18
Links to Exploration step
ISTEX:247E8CE7C3BBEAB502372C25E886D4F903F4A476Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Extracting Meaningful Curves from Images</title>
<author wicri:is="90%"><name sortKey="Cao, Frederic" sort="Cao, Frederic" uniqKey="Cao F" first="Frédéric" last="Cao">Frédéric Cao</name>
</author>
<author wicri:is="90%"><name sortKey="Lisani, Jose Luis" sort="Lisani, Jose Luis" uniqKey="Lisani J" first="José-Luis" last="Lisani">José-Luis Lisani</name>
</author>
<author wicri:is="90%"><name sortKey="Morel, Jean Michel" sort="Morel, Jean Michel" uniqKey="Morel J" first="Jean-Michel" last="Morel">Jean-Michel Morel</name>
</author>
<author wicri:is="90%"><name sortKey="Muse, Pablo" sort="Muse, Pablo" uniqKey="Muse P" first="Pablo" last="Musé">Pablo Musé</name>
</author>
<author wicri:is="90%"><name sortKey="Sur, Frederic" sort="Sur, Frederic" uniqKey="Sur F" first="Frédéric" last="Sur">Frédéric Sur</name>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">ISTEX</idno>
<idno type="RBID">ISTEX:247E8CE7C3BBEAB502372C25E886D4F903F4A476</idno>
<date when="2008" year="2008">2008</date>
<idno type="doi">10.1007/978-3-540-68481-7_2</idno>
<idno type="url">https://api.istex.fr/ark:/67375/HCB-M2WBVBBM-9/fulltext.pdf</idno>
<idno type="wicri:Area/Istex/Corpus">000824</idno>
<idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">000824</idno>
<idno type="wicri:Area/Istex/Curation">000819</idno>
<idno type="wicri:Area/Istex/Checkpoint">000E18</idno>
<idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Checkpoint">000E18</idno>
<idno type="wicri:doubleKey">0075-8434:2008:Cao F:extracting:meaningful:curves</idno>
<idno type="wicri:Area/Main/Merge">004501</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Extracting Meaningful Curves from Images</title>
<author wicri:is="90%"><name sortKey="Cao, Frederic" sort="Cao, Frederic" uniqKey="Cao F" first="Frédéric" last="Cao">Frédéric Cao</name>
<affiliation wicri:level="3"><country xml:lang="fr">France</country>
<wicri:regionArea>DxO Labs, 3 rue Nationale, 92100 Boulogne Billancourt</wicri:regionArea>
<placeName><region type="region" nuts="2">Île-de-France</region>
<settlement type="city">Boulogne-Billancourt</settlement>
</placeName>
</affiliation>
<affiliation></affiliation>
</author>
<author wicri:is="90%"><name sortKey="Lisani, Jose Luis" sort="Lisani, Jose Luis" uniqKey="Lisani J" first="José-Luis" last="Lisani">José-Luis Lisani</name>
<affiliation wicri:level="1"><country xml:lang="fr">Espagne</country>
<wicri:regionArea>Dep. Matemàtiques i Informàtica, University Balearic Islands, ctra. Valldemossa km.7,5, 07122 Palma de Mallorca, Balears</wicri:regionArea>
<wicri:noRegion>Balears</wicri:noRegion>
</affiliation>
<affiliation wicri:level="1"><country wicri:rule="url">Espagne</country>
</affiliation>
</author>
<author wicri:is="90%"><name sortKey="Morel, Jean Michel" sort="Morel, Jean Michel" uniqKey="Morel J" first="Jean-Michel" last="Morel">Jean-Michel Morel</name>
<affiliation wicri:level="3"><country xml:lang="fr">France</country>
<wicri:regionArea>Ecole Normale Supérieure de Cachan, CMLA, 61 av. du Président Wilson, 94235 Cachan Cédex</wicri:regionArea>
<placeName><region type="region" nuts="2">Île-de-France</region>
<settlement type="city">Cachan Cédex</settlement>
</placeName>
</affiliation>
<affiliation wicri:level="1"><country wicri:rule="url">France</country>
</affiliation>
</author>
<author wicri:is="90%"><name sortKey="Muse, Pablo" sort="Muse, Pablo" uniqKey="Muse P" first="Pablo" last="Musé">Pablo Musé</name>
<affiliation wicri:level="1"><country xml:lang="fr">Uruguay</country>
<wicri:regionArea>Instituto de Ingeniería Eléctrica, Julio Herrera y Reissig 565, 11300 Montevideo</wicri:regionArea>
<wicri:noRegion>11300 Montevideo</wicri:noRegion>
</affiliation>
<affiliation wicri:level="1"><country wicri:rule="url">Uruguay</country>
</affiliation>
</author>
<author wicri:is="90%"><name sortKey="Sur, Frederic" sort="Sur, Frederic" uniqKey="Sur F" first="Frédéric" last="Sur">Frédéric Sur</name>
<affiliation wicri:level="1"><country xml:lang="fr">France</country>
<wicri:regionArea>Loria Bat. C - projet Magrit Campus Scientifique, 54506 Vandoeuvre-lès-Nancy Cédex, BP 239</wicri:regionArea>
<wicri:noRegion>BP 239</wicri:noRegion>
<wicri:noRegion>BP 239</wicri:noRegion>
</affiliation>
<affiliation wicri:level="1"><country wicri:rule="url">France</country>
</affiliation>
</author>
</analytic>
<monogr></monogr>
<series><title level="s" type="main" xml:lang="en">Lecture Notes in Mathematics</title>
<idno type="ISSN">0075-8434</idno>
<idno type="ISSN">0075-8434</idno>
</series>
</biblStruct>
</sourceDesc>
<seriesStmt><idno type="ISSN">0075-8434</idno>
</seriesStmt>
</fileDesc>
<profileDesc><textClass></textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">The set of level lines of an image (isophotes) or topographic map is a complete and contrast invariant representation of an image. Level lines are ordered by inclusion in a tree structure. These two structure properties make level lines excellent candidates to shape representatives. However, some complexity issues have to be handled: The number of level lines in eight-bits encoded images of size 512×512 is typically 105. Most of them are very small lines due to noise or micro-texture. So the stable level lines must be selected, namely the ones that are likely to correspond to image contours. The starting point is the MSER method, a variant of the Monasse and Guichard Fast Level Set Transform. The MSER selects a set of level lines which are local extrema of contrast. This method will be put in the Helmholtz framework, following the a contrario boundary detection algorithm by Desolneux, Moisan and Morel [51], [54] and two powerful recent variants. The experiments in this chapter will show that selecting the most meaningful level lines reduces their number by a factor 100 without significant shape contents loss. A method which selects one out of hundred level lines in the image without significant information loss is necessarily sophisticated. Sect. 2.1 briefly reviews the level line tree of a digital image. Sect. 2.2 describes a first way to extract well contrasted level lines, the MSER method. Sect. 2.3 makes an account of the Desolneux et al. maximal meaningful boundaries and Sect. 2.4 gives a mathematical justification which was actually missing in the original theory. Sect. 2.5 is devoted to a multiscale extension which avoids missing boundaries because of high noise level and Sect. 2.6 deals with the so called “blue sky” effect which can lead to over-detections in textured parts of the image.</div>
</front>
</TEI>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Lorraine/explor/InforLorV4/Data/Main/Merge
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 004501 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Merge/biblio.hfd -nk 004501 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Wicri/Lorraine |area= InforLorV4 |flux= Main |étape= Merge |type= RBID |clé= ISTEX:247E8CE7C3BBEAB502372C25E886D4F903F4A476 |texte= Extracting Meaningful Curves from Images }}
![]() | This area was generated with Dilib version V0.6.33. | ![]() |