CATALOG: a system for detection and rendering of internal log defects using computer tomography

Bhandarkar, Suchendra M.; Faust, Timothy D.; Tang, Mengjin

doi:10.1007/s001380050100

Cited by 61 publications

(43 citation statements)

References 9 publications

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“…Techniques for internal defect identification and classification in cross-sectional CT images of logs include gray-level thresholding and binarization [13], [34], neural network-based classification [29], integration of shape and texture features [1], [2], [5] and DempsterSchafer theory-based evidential reasoning on the 3-D geometric features of the defects [36]. Bhandarkar et al [1], [2] and Samson [26] present geometrical modeling algorithms to describe the structure of internal defects within the logs and their appearance on the surfaces of the lumber beams sawn from those logs, and to compute the effect of the presence of knots in the conversion of logs into structural lumber [2], [27].…”

Section: Brief Literature Reviewmentioning

confidence: 99%

“…Bhandarkar et al [1], [2] and Samson [26] present geometrical modeling algorithms to describe the structure of internal defects within the logs and their appearance on the surfaces of the lumber beams sawn from those logs, and to compute the effect of the presence of knots in the conversion of logs into structural lumber [2], [27]. The results of internal defect identification and localization can be used to reconstruct a 3-D model of the log along with its internal defects [1], [2]. Software programs that simulate various machining operations such as sawing and veneering on the virtual 3-D log reconstruction have been described in the literature [1], [2], [8], [15], [23], [28].…”

Section: Brief Literature Reviewmentioning

confidence: 99%

“…Studies of computed axial tomography (CAT or CT) and magnetic resonance imaging (MRI) (also known as nuclear magnetic resonance (NMR) imaging) for internal log defects [1], [2], [6], [13], [34] have demonstrated that the CT and MRI technologies available today can be used successfully to image the internal features of logs. On account of their inherent sensitivity to the water content of the imaged sample, MRI techniques are particularly well suited for detecting internal features of logs, such as knots, reaction wood, wetwood, and gum spots, that are characterized by varying moisture content in the underlying wood [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated Planning and Optimization of Lumber Production Using Machine Vision and Computed Tomography

Bhandarkar

Luo

Daniels

et al. 2008

IEEE Trans. Automat. Sci. Eng.

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Abstract-An automated system for planning and optimization of lumber production using Machine Vision and Computed Tomography (CT) is proposed. Cross-sectional CT images of hardwood logs are analyzed using Machine Vision algorithms. Internal defects in the hardwood logs pockets are identified and localized. A virtual in silico 3-D reconstruction of the hardwood log and its internal defects is generated using Kalman filter-based tracking algorithms. Various sawing operations are simulated on the virtual 3-D reconstruction of the log and the resulting virtual lumber products automatically graded using rules stipulated by the National Hardwood Lumber Association (NHLA). Knowledge of the internal log defects is suitably exploited to formulate sawing strategies that optimize the value yield recovery of the resulting lumber products. A prototype implementation shows significant gains in value yield recovery when compared to lumber processing strategies that use only the information derived from the external log structure. The system is intended as a decision aid for lumber production planning and an interactive training tool for novice sawyers and machinists in the lumber industry.

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Section: Brief Literature Reviewmentioning

confidence: 99%

Section: Brief Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Planning and Optimization of Lumber Production Using Machine Vision and Computed Tomography

Bhandarkar

Luo

Daniels

et al. 2008

IEEE Trans. Automat. Sci. Eng.

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“…CT scanning of wood has been used for research purposes for more than two decades (Lindgren [6], Zhu et al [7], Bhandarkar et al [8], Moberg [9], Alkan et al [10], Brüchert et al [11], Hou et al [12], Longuetaud et al [13]). Wei et al [14] has published a review covering most of the research done on CT scanning of wood up until the year 2010.…”

Section: Introductionmentioning

confidence: 99%

Optimizing sawing of boards for furniture production using CT log scanning

Fredriksson

2015

J Wood Sci

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The inherent variability of wood material together with sub-optimization in production processes means that a lot of potential value is lost. Computed tomography scanning together with simulation models of the production processes could remedy this, and ensure optimization of the entire production process. Therefore, the purpose of this study was to investigate if such methods can be used to optimize the sawing position of logs in a production process including further processing, in this case crosscutting to make a furniture product with strict quality requirements on dead knots. This was done on 47 Scots pine (Pinus sylvestris L.) logs. The results show a potential yield increase of more than 11 percentage points in the crosscutting operation and more than 4 percentage points when viewing the process as a whole, compared to sawing the logs horns down and centered.

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“…En esta dirección, el ultrasonido (Birkeland y Han, 1991), la resonancia magnética nuclear (Chang et al 1989;Coates et al 1998) y Radiación gamma (Hagman 1993, Karsulovic et al 2002y 2005 son algunas de las técnicas utilizadas para este n. Sin embargo, la Tomografía Computarizada (TC) de rayos X, ha mostrado un gran potencial para identi car defectos y características internas en trozos (Taylor et al 1984, Funt y Bryant 1987, Zhu et al 1991, Li et al 1996, Guddanti y Chang 1998, Bhandarkar et al 1999, Schmoldt et al 1995, 2000, Oja y Temnerud 1999, 2000, Nordmark 2002, 2003, Longuetaud et al 2004y Rojas et al 2005. La mayoría de estos autores coinciden en que la variación del contenido de humedad en la troza en algunos casos di culta la identi cación.…”

Section: Introduccionunclassified

Identificación del cilindro nudoso en imágenes TC de trozas de Pinus radiata: Estudio Comparativo

Espinoza¹,

Iribarren²

2012

Maderas, Cienc. tecnol.

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RESUMENEl objetivo de este estudio fue comparar la precisión de los algoritmos de máxima verosimilitud (MV) y otro basado en redes neuronales arti ciales (RNA), en la identi cación del cilindro nudoso a partir de imágenes TC (Tomografía Computarizada) de trozas podadas de pino radiata (Pinus radiata D.Don). Para este n, treinta trozas podadas fueron seleccionadas y luego escaneadas en un escáner médico multi-slice de rayos X (Tomografía Computarizada). Del total de imágenes TC obtenidas del escáner, 270 fueron seleccionadas para este estudio. Estas imágenes TC fueron clasi cadas utilizando ambos algoritmos y los mapas temáticos obtenidos de este proceso, fueron posteriormente ltrados utilizando un ltro median de 7 x 7. Los resultados de la evaluación cuantitativa indicaron que el cilindro nudoso puede ser identi cado con una precisión de 98.5 % y 96.3 % utilizando el clasi cador MV y RNA, respectivamente. Aunque ambos algoritmos presentaron elevados valores de precisión para identi car el cilindro nudoso, el análisis estadístico de estos resultados arrojo diferencias signi cativas entre ambos valores de precisión; por lo tanto se concluye que el algoritmo de máxima verosimilitud presenta un mejor desempeño que el algoritmo basado en redes neuronales arti ciales, en la identi cación del cilindro nudoso en imágenes TC de trozas de pino radiata (Pinus radiata D.Don).Palabras claves: Cilindro nudoso, tomografía computarizada (TC), pino radiata, máxima verosimilitud, redes neuronales arti ciales ABSTRACT e aim of this study was to compare the accuracy of both the maximum likelihood classi er (ML) algorithm and another one based on an arti cial neural networks classi er (ANN) algorithm for knotty core identi cation in CT images of pruned radiata pine (Pinus radiata D. Don) logs. For this purpose, thirty pruned radiata pine logs were chosen and then scanned in an X-ray multi-slice medical scanner (Computed Tomography (CT)). From the total CT images obtained, a sample of 270 CT images was selected for this study. is CT images were classi ed using both methods and the thematic map obtained a erwards, were ltered by a 7 x 7 median lter. Quantitative assessment results showed that knotty core can be identi ed with 98.5 % and 96.3 % accuracy by using the ML and ANN classi ers respectively. Although both algorithms showed a high capacity level to detect knotty core statistical analysis showed signi cant di erences among those accuracy values; this is an indication that the maximum likelihood classi er algorithm shows a better performance compared Ciencia y tecnología, 14(1): [65][66][67][68][69][70][71][72][73][74][75][76][77] 2012 Maderas.

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CATALOG: a system for detection and rendering of internal log defects using computer tomography

Cited by 61 publications

References 9 publications

Automated Planning and Optimization of Lumber Production Using Machine Vision and Computed Tomography

Automated Planning and Optimization of Lumber Production Using Machine Vision and Computed Tomography

Optimizing sawing of boards for furniture production using CT log scanning

Identificación del cilindro nudoso en imágenes TC de trozas de Pinus radiata: Estudio Comparativo

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