InforLorV4, PascalFrancis, Corpus, bibRecord, 000010

Knot segmentation in 3D CT images of wet wood

Identifieur interne : 000010 ( PascalFrancis/Corpus ); précédent : 000009; suivant : 000011

Knot segmentation in 3D CT images of wet wood

Auteurs : Adrien Kr Henbühl ; Bertrand Kerautret ; Isabelle Debled-Rennesson ; Frédéric Mothe ; Fleur Longuetaud

Source :

Pattern recognition [ 0031-3203 ] ; 2014.

RBID : Pascal:14-0239569

Descripteurs français

Pascal (Inist)
- Segmentation, Image tridimensionnelle, Tomographie numérique, Radiographie RX, Scanneur, Mesure automatique, Algorithme, Estimation robuste, Détection forme, Estimation paramètre, Etat actuel, Traitement en ligne, Géométrie discrète.

English descriptors

KwdEn :
- Algorithm, Automatic measurement, Computerized tomography, Discrete geometry, On line processing, Parameter estimation, Robust estimation, Scanner, Segmentation, Shape detection, State of the art, Tridimensional image, X ray radiography.

Abstract

This paper proposes a fully automatic method to segment wood knots from images obtained by an X-ray Computed Tomography scanner. Wood knot segmentation is known to be a difficult problem in the presence of sapwood because of the quite similar density of knots and wet sapwood. Classical segmentation techniques produce unsatisfactory results due to the very weak distinction between these two intensities. To overcome this limitation caused by physical characteristics, we propose to exploit the geometric properties of both the knot shapes and knot-sapwood interface. Based on previous work related to automatic knot detection, a new segmentation algorithm is proposed that uses a robust curvature estimation of 2D digital contours. The segmentation process is fast, easily parallelizable and produces better segmentation results than other state-of-the-art algorithms. It may be reproduced from the precise description given here or from source code available online.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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Format Inist (serveur)

NO :	PASCAL 14-0239569 INIST
ET :	Knot segmentation in 3D CT images of wet wood
AU :	KRÄHENBÜHL (Adrien); KERAUTRET (Bertrand); DEBLED-RENNESSON (Isabelle); MOTHE (Frédéric); LONGUETAUD (Fleur)
AF :	Université de Lorraine, LORIA, Adagio team. UMR 7503/54506 Nancy/France (1 aut., 2 aut., 3 aut.); INRA, UMR1092 LERFoB/54280 Champenoux/France (4 aut., 5 aut.); AgroParisTech, UMR1092 LERFoB/54000 Nancy/France (4 aut., 5 aut.)
DT :	Publication en série; Niveau analytique
SO :	Pattern recognition; ISSN 0031-3203; Coden PTNRA8; Royaume-Uni; Da. 2014; Vol. 47; No. 12; Pp. 3852-3869; Bibl. 25 ref.
LA :	Anglais
EA :	This paper proposes a fully automatic method to segment wood knots from images obtained by an X-ray Computed Tomography scanner. Wood knot segmentation is known to be a difficult problem in the presence of sapwood because of the quite similar density of knots and wet sapwood. Classical segmentation techniques produce unsatisfactory results due to the very weak distinction between these two intensities. To overcome this limitation caused by physical characteristics, we propose to exploit the geometric properties of both the knot shapes and knot-sapwood interface. Based on previous work related to automatic knot detection, a new segmentation algorithm is proposed that uses a robust curvature estimation of 2D digital contours. The segmentation process is fast, easily parallelizable and produces better segmentation results than other state-of-the-art algorithms. It may be reproduced from the precise description given here or from source code available online.
CC :	001D04A05A; 001D04A04A2
FD :	Segmentation; Image tridimensionnelle; Tomographie numérique; Radiographie RX; Scanneur; Mesure automatique; Algorithme; Estimation robuste; Détection forme; Estimation paramètre; Etat actuel; Traitement en ligne; Géométrie discrète
ED :	Segmentation; Tridimensional image; Computerized tomography; X ray radiography; Scanner; Automatic measurement; Algorithm; Robust estimation; Shape detection; Parameter estimation; State of the art; On line processing; Discrete geometry
SD :	Segmentación; Imagen tridimensional; Radiografía RX; Escáner; Medición automática; Algoritmo; Estimación robusta; Detección forma; Estimación parámetro; Estado actual; Tratamiento en línea; Geometría discreta
LO :	INIST-15220.354000502620140100
ID :	14-0239569

Links to Exploration step

Pascal:14-0239569

Le document en format XML

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<front><div type="abstract" xml:lang="en">This paper proposes a fully automatic method to segment wood knots from images obtained by an X-ray Computed Tomography scanner. Wood knot segmentation is known to be a difficult problem in the presence of sapwood because of the quite similar density of knots and wet sapwood. Classical segmentation techniques produce unsatisfactory results due to the very weak distinction between these two intensities. To overcome this limitation caused by physical characteristics, we propose to exploit the geometric properties of both the knot shapes and knot-sapwood interface. Based on previous work related to automatic knot detection, a new segmentation algorithm is proposed that uses a robust curvature estimation of 2D digital contours. The segmentation process is fast, easily parallelizable and produces better segmentation results than other state-of-the-art algorithms. It may be reproduced from the precise description given here or from source code available online.</div>
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<fC01 i1="01" l="ENG"><s0>This paper proposes a fully automatic method to segment wood knots from images obtained by an X-ray Computed Tomography scanner. Wood knot segmentation is known to be a difficult problem in the presence of sapwood because of the quite similar density of knots and wet sapwood. Classical segmentation techniques produce unsatisfactory results due to the very weak distinction between these two intensities. To overcome this limitation caused by physical characteristics, we propose to exploit the geometric properties of both the knot shapes and knot-sapwood interface. Based on previous work related to automatic knot detection, a new segmentation algorithm is proposed that uses a robust curvature estimation of 2D digital contours. The segmentation process is fast, easily parallelizable and produces better segmentation results than other state-of-the-art algorithms. It may be reproduced from the precise description given here or from source code available online.</s0>
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<server><NO>PASCAL 14-0239569 INIST</NO>
<ET>Knot segmentation in 3D CT images of wet wood</ET>
<AU>KRÄHENBÜHL (Adrien); KERAUTRET (Bertrand); DEBLED-RENNESSON (Isabelle); MOTHE (Frédéric); LONGUETAUD (Fleur)</AU>
<AF>Université de Lorraine, LORIA, Adagio team. UMR 7503/54506 Nancy/France (1 aut., 2 aut., 3 aut.); INRA, UMR1092 LERFoB/54280 Champenoux/France (4 aut., 5 aut.); AgroParisTech, UMR1092 LERFoB/54000 Nancy/France (4 aut., 5 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Pattern recognition; ISSN 0031-3203; Coden PTNRA8; Royaume-Uni; Da. 2014; Vol. 47; No. 12; Pp. 3852-3869; Bibl. 25 ref.</SO>
<LA>Anglais</LA>
<EA>This paper proposes a fully automatic method to segment wood knots from images obtained by an X-ray Computed Tomography scanner. Wood knot segmentation is known to be a difficult problem in the presence of sapwood because of the quite similar density of knots and wet sapwood. Classical segmentation techniques produce unsatisfactory results due to the very weak distinction between these two intensities. To overcome this limitation caused by physical characteristics, we propose to exploit the geometric properties of both the knot shapes and knot-sapwood interface. Based on previous work related to automatic knot detection, a new segmentation algorithm is proposed that uses a robust curvature estimation of 2D digital contours. The segmentation process is fast, easily parallelizable and produces better segmentation results than other state-of-the-art algorithms. It may be reproduced from the precise description given here or from source code available online.</EA>
<CC>001D04A05A; 001D04A04A2</CC>
<FD>Segmentation; Image tridimensionnelle; Tomographie numérique; Radiographie RX; Scanneur; Mesure automatique; Algorithme; Estimation robuste; Détection forme; Estimation paramètre; Etat actuel; Traitement en ligne; Géométrie discrète</FD>
<ED>Segmentation; Tridimensional image; Computerized tomography; X ray radiography; Scanner; Automatic measurement; Algorithm; Robust estimation; Shape detection; Parameter estimation; State of the art; On line processing; Discrete geometry</ED>
<SD>Segmentación; Imagen tridimensional; Radiografía RX; Escáner; Medición automática; Algoritmo; Estimación robusta; Detección forma; Estimación parámetro; Estado actual; Tratamiento en línea; Geometría discreta</SD>
<LO>INIST-15220.354000502620140100</LO>
<ID>14-0239569</ID>
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Knot segmentation in 3D CT images of wet wood

Knot segmentation in 3D CT images of wet wood

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