InforLorV4, PascalFrancis, Corpus, bibRecord, 000263

Curvature estimation along noisy digital contours by approximate global optimization

Identifieur interne : 000263 ( PascalFrancis/Corpus ); précédent : 000262; suivant : 000264

Curvature estimation along noisy digital contours by approximate global optimization

Auteurs : B. Kerautret ; J.-O. Lachaud

Source :

Pattern recognition [ 0031-3203 ] ; 2009.

RBID : Pascal:09-0360326

Descripteurs français

Pascal (Inist)
- Estimation paramètre, Détection forme, Optimum global, Image floue, Endommagement, Géométrie discrète, Détection contour, Extraction caractéristique, Robustesse, Traitement signal.

English descriptors

KwdEn :
- Blurred image, Damaging, Discrete geometry, Edge detection, Feature extraction, Global optimum, Parameter estimation, Robustness, Shape detection, Signal processing.

Abstract

In this paper, we introduce a new curvature estimator along digital contours, which we called global min-curvature (GMC) estimator. As opposed to previous curvature estimators, it considers all the possible shapes that are digitized as this contour, and selects the most probable one with a global optimization approach. The GMC estimator exploits the geometric properties of digital contours by using local bounds on tangent directions defined by the maximal digital straight segments. The estimator is then adapted to noisy contours by replacing maximal segments with maximal blurred digital straight segments. Experiments on perfect and damaged digital contours are performed and in both cases, comparisons with other existing methods are presented.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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A02	`01`			`@0 PTNRA8`
A03		`1`		`@0 Pattern recogn.`
A05				`@2 42`
A06				`@2 10`
A08	`01`	`1`	`ENG`	`@1 Curvature estimation along noisy digital contours by approximate global optimization`
A09	`01`	`1`	`ENG`	`@1 Selected Papers from the 14th IAPR International Conference on Discrete Geometry for Computer Imagery 2008`
A11	`01`	`1`		`@1 KERAUTRET (B.)`
A11	`02`	`1`		`@1 LACHAUD (J.-O.)`
A12	`01`	`1`		`@1 SIVIGNON (Isabelle) @9 ed.`
A12	`02`	`1`		`@1 COEURJOLLY (David) @9 ed.`
A12	`03`	`1`		`@1 TOUGNE (Laures) @9 ed.`
A14	`01`			`@1 LORIA, UMR CNRS 7503, Nancy University, Campus Scientifique @2 54506 Vandœuvre-lès-Nancy @3 FRA @Z 1 aut.`
A14	`02`			`@1 LAMA, UMR CNRS 5127, University of Savoie @2 73376 Le Bourget du Lac @3 FRA @Z 2 aut.`
A15	`01`			`@1 Université Claude Bernard Lyon 1, LIRIS, UMR CNRS 5205 @2 69622 Villeurbanne @3 FRA @Z 1 aut. @Z 2 aut. @Z 3 aut.`
A20				`@1 2265-2278`
A21				`@1 2009`
A23	`01`			`@0 ENG`
A43	`01`			`@1 INIST @2 15220 @5 354000187200040060`
A44				`@0 0000 @1 © 2009 INIST-CNRS. All rights reserved.`
A45				`@0 25 ref.`
A47	`01`	`1`		`@0 09-0360326`
A60				`@1 P`
A61				`@0 A`
A64	`01`	`1`		`@0 Pattern recognition`
A66	`01`			`@0 GBR`
C01	`01`		`ENG`	@0 In this paper, we introduce a new curvature estimator along digital contours, which we called global min-curvature (GMC) estimator. As opposed to previous curvature estimators, it considers all the possible shapes that are digitized as this contour, and selects the most probable one with a global optimization approach. The GMC estimator exploits the geometric properties of digital contours by using local bounds on tangent directions defined by the maximal digital straight segments. The estimator is then adapted to noisy contours by replacing maximal segments with maximal blurred digital straight segments. Experiments on perfect and damaged digital contours are performed and in both cases, comparisons with other existing methods are presented.
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C03	`01`	`X`	`FRE`	`@0 Estimation paramètre @5 01`
C03	`01`	`X`	`ENG`	`@0 Parameter estimation @5 01`
C03	`01`	`X`	`SPA`	`@0 Estimación parámetro @5 01`
C03	`02`	`X`	`FRE`	`@0 Détection forme @5 02`
C03	`02`	`X`	`ENG`	`@0 Shape detection @5 02`
C03	`02`	`X`	`SPA`	`@0 Detección forma @5 02`
C03	`03`	`X`	`FRE`	`@0 Optimum global @5 03`
C03	`03`	`X`	`ENG`	`@0 Global optimum @5 03`
C03	`03`	`X`	`SPA`	`@0 Optimo global @5 03`
C03	`04`	`X`	`FRE`	`@0 Image floue @5 04`
C03	`04`	`X`	`ENG`	`@0 Blurred image @5 04`
C03	`04`	`X`	`SPA`	`@0 Imagen borrosa @5 04`
C03	`05`	`X`	`FRE`	`@0 Endommagement @5 05`
C03	`05`	`X`	`ENG`	`@0 Damaging @5 05`
C03	`05`	`X`	`SPA`	`@0 Deterioración @5 05`
C03	`06`	`X`	`FRE`	`@0 Géométrie discrète @5 06`
C03	`06`	`X`	`ENG`	`@0 Discrete geometry @5 06`
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C03	`07`	`X`	`FRE`	`@0 Détection contour @5 07`
C03	`07`	`X`	`ENG`	`@0 Edge detection @5 07`
C03	`07`	`X`	`SPA`	`@0 Detección contorno @5 07`
C03	`08`	`3`	`FRE`	`@0 Extraction caractéristique @5 08`
C03	`08`	`3`	`ENG`	`@0 Feature extraction @5 08`
C03	`09`	`X`	`FRE`	`@0 Robustesse @5 09`
C03	`09`	`X`	`ENG`	`@0 Robustness @5 09`
C03	`09`	`X`	`SPA`	`@0 Robustez @5 09`
C03	`10`	`X`	`FRE`	`@0 Traitement signal @5 31`
C03	`10`	`X`	`ENG`	`@0 Signal processing @5 31`
C03	`10`	`X`	`SPA`	`@0 Procesamiento señal @5 31`
N21				`@1 257`
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Format Inist (serveur)

NO :	PASCAL 09-0360326 INIST
ET :	Curvature estimation along noisy digital contours by approximate global optimization
AU :	KERAUTRET (B.); LACHAUD (J.-O.); SIVIGNON (Isabelle); COEURJOLLY (David); TOUGNE (Laures)
AF :	LORIA, UMR CNRS 7503, Nancy University, Campus Scientifique/54506 Vandœuvre-lès-Nancy/France (1 aut.); LAMA, UMR CNRS 5127, University of Savoie/73376 Le Bourget du Lac/France (2 aut.); Université Claude Bernard Lyon 1, LIRIS, UMR CNRS 5205/69622 Villeurbanne/France (1 aut., 2 aut., 3 aut.)
DT :	Publication en série; Niveau analytique
SO :	Pattern recognition; ISSN 0031-3203; Coden PTNRA8; Royaume-Uni; Da. 2009; Vol. 42; No. 10; Pp. 2265-2278; Bibl. 25 ref.
LA :	Anglais
EA :	In this paper, we introduce a new curvature estimator along digital contours, which we called global min-curvature (GMC) estimator. As opposed to previous curvature estimators, it considers all the possible shapes that are digitized as this contour, and selects the most probable one with a global optimization approach. The GMC estimator exploits the geometric properties of digital contours by using local bounds on tangent directions defined by the maximal digital straight segments. The estimator is then adapted to noisy contours by replacing maximal segments with maximal blurred digital straight segments. Experiments on perfect and damaged digital contours are performed and in both cases, comparisons with other existing methods are presented.
CC :	001D04A04A2; 001D04A05A; 001D04A05D
FD :	Estimation paramètre; Détection forme; Optimum global; Image floue; Endommagement; Géométrie discrète; Détection contour; Extraction caractéristique; Robustesse; Traitement signal
ED :	Parameter estimation; Shape detection; Global optimum; Blurred image; Damaging; Discrete geometry; Edge detection; Feature extraction; Robustness; Signal processing
SD :	Estimación parámetro; Detección forma; Optimo global; Imagen borrosa; Deterioración; Geometría discreta; Detección contorno; Robustez; Procesamiento señal
LO :	INIST-15220.354000187200040060
ID :	09-0360326

Links to Exploration step

Pascal:09-0360326

Le document en format XML

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<ET>Curvature estimation along noisy digital contours by approximate global optimization</ET>
<AU>KERAUTRET (B.); LACHAUD (J.-O.); SIVIGNON (Isabelle); COEURJOLLY (David); TOUGNE (Laures)</AU>
<AF>LORIA, UMR CNRS 7503, Nancy University, Campus Scientifique/54506 Vandœuvre-lès-Nancy/France (1 aut.); LAMA, UMR CNRS 5127, University of Savoie/73376 Le Bourget du Lac/France (2 aut.); Université Claude Bernard Lyon 1, LIRIS, UMR CNRS 5205/69622 Villeurbanne/France (1 aut., 2 aut., 3 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Pattern recognition; ISSN 0031-3203; Coden PTNRA8; Royaume-Uni; Da. 2009; Vol. 42; No. 10; Pp. 2265-2278; Bibl. 25 ref.</SO>
<LA>Anglais</LA>
<EA>In this paper, we introduce a new curvature estimator along digital contours, which we called global min-curvature (GMC) estimator. As opposed to previous curvature estimators, it considers all the possible shapes that are digitized as this contour, and selects the most probable one with a global optimization approach. The GMC estimator exploits the geometric properties of digital contours by using local bounds on tangent directions defined by the maximal digital straight segments. The estimator is then adapted to noisy contours by replacing maximal segments with maximal blurred digital straight segments. Experiments on perfect and damaged digital contours are performed and in both cases, comparisons with other existing methods are presented.</EA>
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Serveur d'exploration sur la recherche en informatique en Lorraine

Curvature estimation along noisy digital contours by approximate global optimization

Curvature estimation along noisy digital contours by approximate global optimization

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