InforLorV4, PascalFrancis, Corpus, bibRecord, 000640

Automatic detection of pith on CT images of spruce logs

Identifieur interne : 000640 ( PascalFrancis/Corpus ); précédent : 000639; suivant : 000641

Automatic detection of pith on CT images of spruce logs

Auteurs : Fleur Longuetaud ; Jean-Michel Leban ; Frédéric Mothe ; Erwan Kerrien ; Marie-Odile Berger

Source :

Computers and electronics in agriculture [ 0168-1699 ] ; 2004.

RBID : Pascal:04-0440223

Descripteurs français

Pascal (Inist)
- Automatique, Tomographie, Traitement image, Grume.

English descriptors

KwdEn :
- Automatic, Image processing, Log(wood), Tomography.

Abstract

Computer tomography is a non-destructive method that can be used to analyze certain characteristics inside logs. Data are acquired in a series of 2-D slices regularly spaced within the log and it is possible to identify the pith, annual growth rings, knots, cracks, resin pockets, etc. This paper focus on automatic pith detection, which can be considered as a preliminary step to detection of other objects such as knots. Firstly, a literature review on this subject is presented. Then, a method for a detection of the pith along a log is proposed, which is able to deal with the problem of the presence of whorls. The whole sample used contained 18,700 images of spruce taken within 42 logs from 12 trees cut from two stands. Four trees constituted the base sample which was used to fix the parameters of the algorithm; the rest of the trees constituted the test sample. The mean distance between the automatically and manually detected pith positions was only 0.75 mm and for more than 95% of the slices the accuracy of the detection was better than 2 mm.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

A01	`01`	`1`		`@0 0168-1699`
A02	`01`			`@0 CEAGE6`
A03		`1`		`@0 Comput. electron. agric.`
A05				`@2 44`
A06				`@2 2`
A08	`01`	`1`	`ENG`	`@1 Automatic detection of pith on CT images of spruce logs`
A11	`01`	`1`		`@1 LONGUETAUD (Fleur)`
A11	`02`	`1`		`@1 LEBAN (Jean-Michel)`
A11	`03`	`1`		`@1 MOTHE (Frédéric)`
A11	`04`	`1`		`@1 KERRIEN (Erwan)`
A11	`05`	`1`		`@1 BERGER (Marie-Odile)`
A14	`01`			`@1 Centre INRA de Nancy, Equipe de recherche sur la Qualité des Bois, Laboratoire d'Etude des Ressources Forêt-Bois @2 54280 Champenoux @3 FRA @Z 1 aut. @Z 2 aut. @Z 3 aut.`
A14	`02`			`@1 INRIA Lorraine and LORIA, 615 rue du Jardin Botanique, BP 101 @2 54602 Villers Les Nancy @3 FRA @Z 4 aut. @Z 5 aut.`
A20				`@1 107-119`
A21				`@1 2004`
A23	`01`			`@0 ENG`
A43	`01`			`@1 INIST @2 21007 @5 354000110576770020`
A44				`@0 0000 @1 © 2004 INIST-CNRS. All rights reserved.`
A45				`@0 20 ref.`
A47	`01`	`1`		`@0 04-0440223`
A60				`@1 P`
A61				`@0 A`
A64	`01`	`1`		`@0 Computers and electronics in agriculture`
A66	`01`			`@0 NLD`
C01	`01`		`ENG`	@0 Computer tomography is a non-destructive method that can be used to analyze certain characteristics inside logs. Data are acquired in a series of 2-D slices regularly spaced within the log and it is possible to identify the pith, annual growth rings, knots, cracks, resin pockets, etc. This paper focus on automatic pith detection, which can be considered as a preliminary step to detection of other objects such as knots. Firstly, a literature review on this subject is presented. Then, a method for a detection of the pith along a log is proposed, which is able to deal with the problem of the presence of whorls. The whole sample used contained 18,700 images of spruce taken within 42 logs from 12 trees cut from two stands. Four trees constituted the base sample which was used to fix the parameters of the algorithm; the rest of the trees constituted the test sample. The mean distance between the automatically and manually detected pith positions was only 0.75 mm and for more than 95% of the slices the accuracy of the detection was better than 2 mm.
C02	`01`	`X`		`@0 002A32`
C03	`01`	`X`	`FRE`	`@0 Automatique @5 01`
C03	`01`	`X`	`ENG`	`@0 Automatic @5 01`
C03	`01`	`X`	`SPA`	`@0 Automático @5 01`
C03	`02`	`X`	`FRE`	`@0 Tomographie @5 02`
C03	`02`	`X`	`ENG`	`@0 Tomography @5 02`
C03	`02`	`X`	`SPA`	`@0 Tomografía @5 02`
C03	`03`	`X`	`FRE`	`@0 Traitement image @5 03`
C03	`03`	`X`	`ENG`	`@0 Image processing @5 03`
C03	`03`	`X`	`SPA`	`@0 Procesamiento imagen @5 03`
C03	`04`	`X`	`FRE`	`@0 Grume @4 CD @5 96`
C03	`04`	`X`	`ENG`	`@0 Log(wood) @4 CD @5 96`
N21				`@1 250`
N44	`01`			`@1 OTO`
N82				`@1 OTO`

Format Inist (serveur)

NO :	PASCAL 04-0440223 INIST
ET :	Automatic detection of pith on CT images of spruce logs
AU :	LONGUETAUD (Fleur); LEBAN (Jean-Michel); MOTHE (Frédéric); KERRIEN (Erwan); BERGER (Marie-Odile)
AF :	Centre INRA de Nancy, Equipe de recherche sur la Qualité des Bois, Laboratoire d'Etude des Ressources Forêt-Bois/54280 Champenoux/France (1 aut., 2 aut., 3 aut.); INRIA Lorraine and LORIA, 615 rue du Jardin Botanique, BP 101/54602 Villers Les Nancy/France (4 aut., 5 aut.)
DT :	Publication en série; Niveau analytique
SO :	Computers and electronics in agriculture; ISSN 0168-1699; Coden CEAGE6; Pays-Bas; Da. 2004; Vol. 44; No. 2; Pp. 107-119; Bibl. 20 ref.
LA :	Anglais
EA :	Computer tomography is a non-destructive method that can be used to analyze certain characteristics inside logs. Data are acquired in a series of 2-D slices regularly spaced within the log and it is possible to identify the pith, annual growth rings, knots, cracks, resin pockets, etc. This paper focus on automatic pith detection, which can be considered as a preliminary step to detection of other objects such as knots. Firstly, a literature review on this subject is presented. Then, a method for a detection of the pith along a log is proposed, which is able to deal with the problem of the presence of whorls. The whole sample used contained 18,700 images of spruce taken within 42 logs from 12 trees cut from two stands. Four trees constituted the base sample which was used to fix the parameters of the algorithm; the rest of the trees constituted the test sample. The mean distance between the automatically and manually detected pith positions was only 0.75 mm and for more than 95% of the slices the accuracy of the detection was better than 2 mm.
CC :	002A32
FD :	Automatique; Tomographie; Traitement image; Grume
ED :	Automatic; Tomography; Image processing; Log(wood)
SD :	Automático; Tomografía; Procesamiento imagen
LO :	INIST-21007.354000110576770020
ID :	04-0440223

Links to Exploration step

Pascal:04-0440223

Le document en format XML

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<front><div type="abstract" xml:lang="en">Computer tomography is a non-destructive method that can be used to analyze certain characteristics inside logs. Data are acquired in a series of 2-D slices regularly spaced within the log and it is possible to identify the pith, annual growth rings, knots, cracks, resin pockets, etc. This paper focus on automatic pith detection, which can be considered as a preliminary step to detection of other objects such as knots. Firstly, a literature review on this subject is presented. Then, a method for a detection of the pith along a log is proposed, which is able to deal with the problem of the presence of whorls. The whole sample used contained 18,700 images of spruce taken within 42 logs from 12 trees cut from two stands. Four trees constituted the base sample which was used to fix the parameters of the algorithm; the rest of the trees constituted the test sample. The mean distance between the automatically and manually detected pith positions was only 0.75 mm and for more than 95% of the slices the accuracy of the detection was better than 2 mm.</div>
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<ET>Automatic detection of pith on CT images of spruce logs</ET>
<AU>LONGUETAUD (Fleur); LEBAN (Jean-Michel); MOTHE (Frédéric); KERRIEN (Erwan); BERGER (Marie-Odile)</AU>
<AF>Centre INRA de Nancy, Equipe de recherche sur la Qualité des Bois, Laboratoire d'Etude des Ressources Forêt-Bois/54280 Champenoux/France (1 aut., 2 aut., 3 aut.); INRIA Lorraine and LORIA, 615 rue du Jardin Botanique, BP 101/54602 Villers Les Nancy/France (4 aut., 5 aut.)</AF>
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<EA>Computer tomography is a non-destructive method that can be used to analyze certain characteristics inside logs. Data are acquired in a series of 2-D slices regularly spaced within the log and it is possible to identify the pith, annual growth rings, knots, cracks, resin pockets, etc. This paper focus on automatic pith detection, which can be considered as a preliminary step to detection of other objects such as knots. Firstly, a literature review on this subject is presented. Then, a method for a detection of the pith along a log is proposed, which is able to deal with the problem of the presence of whorls. The whole sample used contained 18,700 images of spruce taken within 42 logs from 12 trees cut from two stands. Four trees constituted the base sample which was used to fix the parameters of the algorithm; the rest of the trees constituted the test sample. The mean distance between the automatically and manually detected pith positions was only 0.75 mm and for more than 95% of the slices the accuracy of the detection was better than 2 mm.</EA>
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Automatic detection of pith on CT images of spruce logs

Automatic detection of pith on CT images of spruce logs

Source :

Descripteurs français

English descriptors

Abstract

Notice en format standard (ISO 2709)

Format Inist (serveur)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri