Nondestructive Evaluation of Hardwood Logs Using Automated Interpretation of CT Images

Schmoldt, Daniel L.; Zhu, Delong; Conners, Richard W.

doi:10.1007/978-1-4615-2848-7_290

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Also, the segmentation algorithms do not work properly for young trees for which heartwood has a too small area (Nordmark 2002). In other studies, moisture content variation within the green log was identified as a source of error in distinguishing defects from sound wood because the difference in CT numbers is attributed to the difference in moisture content rather than to that in density (Taylor et al 1984;Funt and Bryant 1987;Schmoldt et al 1993;Schad et al 1996;Müller and Teischinger 2001;Bucur 2003a;Middleton et al 2003;Alkan et al 2007;Longuetaud et al 2007;Brüchert et al 2008). The algorithms that labeled the identified regions based on a supposed elliptical shape of knots did not work very well for identifying knots with irregular shapes on CT slices (Bhandarkar et al 1999).…”

Section: Knotsmentioning

confidence: 97%

“…According to Hodges et al (1990), investments in CT scanning systems could be profitable with increases in lumber value yield of 5%-10% in the case of large hardwood sawmills (60-103 m 3 ·year -1 ) and of 30% in the case of smaller hardwood sawmills (12-103 m 3 ·year -1 ). However, it was suggested that the use of CT scanners is only economically sound for high-value logs (Schmoldt et al 1993;Rinnhofer et al 2003;Harding et al 2007) and only on defect areas of logs (Schmoldt et al 1993). …”

Section: Operability Of X-ray Ct Scanners In Real Wood Industry Envirmentioning

confidence: 98%

“…These differences in GL values among log features allow the development of segmentation algorithms that use GL value histogram thresholds to identify potential defect regions on CT images (Taylor et al 1984;Funt and Bryant 1987;Grönlund 1991, 1992;Wells et al 1991;Schmoldt et al 1993;Som et al 1993Som et al , 1995Hagman and Grundberg 1995;Zhu et al 1996;Oja 1997Oja , 2000Bhandarkar et al 1999;Jaeger et al 1999;Aguilera et al 2002;Andreu and Rinnhofer 2003;Longuetaud et al 2005). The labeling of the identified areas is done using various techniques.…”

Section: Knotsmentioning

confidence: 98%

“…Correlation analysis across corresponding defect-like regions in neighbouring CT slices was used to verify whether the region, previously identified as a potential defect region, is indeed a defect-free or not region (Bhandarkar et al 1999). A heuristic rule based recognition algorithm that is based on threshold values of 3D volume descriptors was used to detect defects on red oak (Quercus rubra L.) and yellow-poplar logs (Schmoldt et al 1993). 3D intensity-related and orientationrelated characteristics of log defects were used to label the segmented images into specific typical zones using a knowledge-based method called the Dempster-Shafer theory of evidential reasoning (Zhu et al 1996).…”

Section: Knotsmentioning

confidence: 99%

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On the use of X-ray computed tomography for determining wood properties: a review¹This article is a contribution to the series The Role of Sensors in the New Forest Products Industry and Bioeconomy.

2011

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In several processes of the forest products industry, an in-depth knowledge of log and board internal features is required and their determination needs fast scanning systems. One of the possible technologies is X-ray computed tomography (CT) technology. Our paper reviews applications of this technology in wood density measurements, in wood moisture content monitoring, and in locating internal log features that include pith, sapwood, heartwood, knots, and other defects. Annual growth ring measurements are more problematic to be detected on CT images because of the low spatial resolution of the images used. For log feature identification, our review shows that the feed-forward back-propagation artificial neural network is the most efficient CT image processing method. There are also some studies attempting to reconstruct three-dimensional log or board images from two-dimensional CT images. Several industrial prototypes have been developed because medical CT scanners were shown to be inappropriate for the wood industry. Because of the high cost of X-ray CT scanner equipment, other types of inexpensive sensors should also be investigated, such as electric resistivity tomography and microwaves. It also appears that the best approach uses various different sensors, each of them having its own strengths and weaknesses.Résumé : Plusieurs processus de l'industrie des produits forestiers exigent une connaissance approfondie des caractéristiques internes des billes et des planches dont l'acquisition nécessite des appareils à balayage rapide. Une des technologies potentielles est la tomodensitométrie (TDM). Notre article passe en revue les applications de cette technologie pour mesurer la densité du bois, faire le suivi de la teneur en humidité dans le bois et localiser les caractéristiques internes du bois, telles que la moelle, le bois d'aubier, le bois de coeur, les noeuds et les autres défauts. Il est plus difficile de mesurer les cernes annuels sur les images obtenues par TDM à cause de la faible résolution spatiale de ces images. Pour l'identification des caractéristiques des billes, notre revue montre que le réseau neuronal artificiel multicouche à rétropropagation est la méthode la plus efficace pour traiter les images obtenues par TDM. Il y a aussi certaines études qui tentent de reconstruire des images en trois dimensions à partir des images en deux dimensions obtenues par TDM. Plusieurs prototypes industriels ont été développés parce qu'il a été démontré que les tomographes médicaux ne sont pas appropriés pour l'industrie du bois. Étant donné le coût élevé des appareils de TDM, d'autres types de capteurs bon marché devraient être étudiés, tels que la tomographie de résistivité électrique et les micro-ondes. Il semble aussi que la meilleure approche consiste à utiliser plusieurs capteurs différents, chacun ayant ses forces et ses faiblesses.[Traduit par la Rédaction]

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Section: Knotsmentioning

confidence: 97%

Section: Operability Of X-ray Ct Scanners In Real Wood Industry Envirmentioning

confidence: 98%

Section: Knotsmentioning

confidence: 98%

Section: Knotsmentioning

confidence: 99%

See 2 more Smart Citations

On the use of X-ray computed tomography for determining wood properties: a review¹This article is a contribution to the series The Role of Sensors in the New Forest Products Industry and Bioeconomy.

2011

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Locating and identifying splits and holes on sugi by the laser displacement sensor

Tanaka

Ohtani

2003

J Wood Sci

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The split and the hole are two common defects on sugi (Cryptomeria japonica D.Don). They have a common feature in that they are associated with surface irregularities. We have developed a laser scanning system to detect the splits and the holes based on their thickness, which correlates spatially with the profile information. The displacements measured by the laser sensor were converted to pixel values to generate the displacement profile image. Both the splits and the holes manifested well in the image. A dedicated image-processing program written in Visual Basic has been developed. The defects regions were accurately located by the image processing. To identify the defects, eight recognition rules based on four features have been utilized. Furthermore, a method based on the pixel model was proposed to compute the area of the defect. The results indicated that the defects could be identified correctly, and the areas could be computed accurately using the pixels model.

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Automatic Detection of Annual Growth Units on Picea abies Logs Using Optical and X-Ray Techniques

Longuetaud¹,

Saint‐André²,

Leban³

2005

J Nondestruct Eval

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Nondestructive Evaluation of Hardwood Logs Using Automated Interpretation of CT Images

Cited by 6 publications

References 10 publications

On the use of X-ray computed tomography for determining wood properties: a review¹This article is a contribution to the series The Role of Sensors in the New Forest Products Industry and Bioeconomy.

On the use of X-ray computed tomography for determining wood properties: a review¹This article is a contribution to the series The Role of Sensors in the New Forest Products Industry and Bioeconomy.

Locating and identifying splits and holes on sugi by the laser displacement sensor

Automatic Detection of Annual Growth Units on Picea abies Logs Using Optical and X-Ray Techniques

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Nondestructive Evaluation of Hardwood Logs Using Automated Interpretation of CT Images

Cited by 6 publications

References 10 publications

On the use of X-ray computed tomography for determining wood properties: a review1This article is a contribution to the series The Role of Sensors in the New Forest Products Industry and Bioeconomy.

On the use of X-ray computed tomography for determining wood properties: a review1This article is a contribution to the series The Role of Sensors in the New Forest Products Industry and Bioeconomy.

Locating and identifying splits and holes on sugi by the laser displacement sensor

Automatic Detection of Annual Growth Units on Picea abies Logs Using Optical and X-Ray Techniques

Contact Info

Product

Resources

About

On the use of X-ray computed tomography for determining wood properties: a review¹This article is a contribution to the series The Role of Sensors in the New Forest Products Industry and Bioeconomy.

On the use of X-ray computed tomography for determining wood properties: a review¹This article is a contribution to the series The Role of Sensors in the New Forest Products Industry and Bioeconomy.