Automated knot detection for high speed computed tomography on Pinus sylvestris L. and Picea abies (L.) Karst. using ellipse fitting in concentric surfaces

Johansson, Erik M. J.; Johansson, Dennis; Skog, Johan; Fredriksson, Magnus

doi:10.1016/j.compag.2013.06.003

Cited by 51 publications

(51 citation statements)

References 9 publications

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“…In particular, the knot detection algorithm described by Johansson et al (2013) depends on an 51 accurate detection of pith, sapwood-heartwood border and outer shape of the log. In a fully 52 dried log, the sapwood density will be very close to that of the heartwood, thus making 53 distinction between the two nearly impossible.…”

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Knot detection in computed tomography images of partially dried jack pine (Pinus banksiana) and white spruce (Picea glauca) logs from a Nelder type plantation

et al. 2017

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1X-ray computed tomography (CT) of logs means possibilities for optimizing breakdown in 2 sawmills. This depends on accurate detection of knots to assess internal quality. However, as 3 logs are stored they dry to some extent, and this drying affects the density variation in the log, 4 and therefore the X-ray images. For this reason it is hypothetically difficult to detect log 5 features in partially dried logs using X-ray CT. This paper investigates the effect of improper 6 heartwood-sapwood border detection, possibly due to partial drying, on knot detection in jack 7 pine (Pinus banksiana Lamb.) and white spruce (Picea glauca (Moench) Voss) logs from New 8 Brunswick, Canada. An automatic knot detection algorithm was compared to manual 9 reference knot measurements, and the results showed that knot detection was affected by 10 detected heartwood shape. It was also shown that logs can be sorted into two groups based on 11 how well the heartwood-sapwood border is detected, to separate logs with a high knot 12 detection rate from those with a low detection rate. In that way, a decision can be made 13 whether or not to trust the knot models obtained from CT scanning. This can potentially aid 14 both sawmills and researchers working with log models based on CT.

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confidence: 99%

“…Given the hypothetical difficulties of detecting knots properly in partially dried out logs or 67 with large knots, the objective of this study was to apply the knot detection algorithm 68 developed by Johansson et al (2013) …”

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Knot detection in computed tomography images of partially dried jack pine (Pinus banksiana) and white spruce (Picea glauca) logs from a Nelder type plantation

et al. 2017

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“…For instance, the sawing process is not always accurate, and the positioning of the log during sawing cannot be fully controlled (Vuorilehto and Tulokas [31]). Also, the automatic algorithms used to detect knots in a log are not fully accurate either (Johansson et al [32]). This was beyond the scope of this paper, but if an industrial application were to be realized, a sensitivity analysis should be performed first, and a working solution should be robust enough to handle possible errors.…”

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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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“…The logs were scanned with a computer tomograph (MiCROTEC CT.LOG ® ). Parametrical log models (also referred to as 'virtual logs') representing log outer shape, heartwood-sapwood border, pith and knots were extracted from the CT images by means of the knotdetection software developed by Johansson et al (2013) and saved.…”

Section: Methodsmentioning

confidence: 99%

“…Funt andBryant 1987, Rinnhofer et al 2003). Internal wood properties such as the position of the pith, the heartwood-sapwood border and knots can be detected with sufficient accuracy (Longuetaud et al 2004, Longuetaud et al 2007, Johansson et al 2013. Recent development of purpose-built CT scanning equipment for sawmills employing highspeed cone-beam tomography has made this technology usable for industrial application (Giudiceandrea et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Optimization potential for perception-oriented appearance classification by simulated sawing of computed tomography-scanned logs of Norway spruce

Breinig

Broman

Brüchert

et al. 2014

Wood Material Science & Engineering

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Wood, as a natural material, has favourable properties in both technical and aesthetic aspects. Due to its inherent variability, production of high-quality sawn timber demands adequate control of log conversion, which is feasible with computed tomography (CT) log scanning. Existing appearance grading rules for sawn timber might not fully reflect people's visual perception of wood surfaces, and therefore, an alternative, more perception-oriented appearance classification could be beneficial. An appearance classification of sawn timber based on partial least squares discriminant analysis (PLS-DA) of knot-pattern variables was developed and tested. Knot-pattern variables derived from images of board faces were used in training PLS-DA models against an initial classification of the board faces previously established by aid of cluster analysis. Virtual board faces obtained from simulated breakdown of 57 CT-scanned Norway spruce logs were graded according to the developed classification. Visual assessment of the grading results indicated that the classification was largely consistent with human perception of board appearance. An initial estimation of the potential to optimize log rotation, based on CT data, for the established appearance grades was derived from the simulations. Considerable potential to increase the yield of a desired appearance grade, compared to conventional log positioning, was observed.

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Automated knot detection for high speed computed tomography on Pinus sylvestris L. and Picea abies (L.) Karst. using ellipse fitting in concentric surfaces

Cited by 51 publications

References 9 publications

Knot detection in computed tomography images of partially dried jack pine (Pinus banksiana) and white spruce (Picea glauca) logs from a Nelder type plantation

Knot detection in computed tomography images of partially dried jack pine (Pinus banksiana) and white spruce (Picea glauca) logs from a Nelder type plantation

Optimizing sawing of boards for furniture production using CT log scanning

Optimization potential for perception-oriented appearance classification by simulated sawing of computed tomography-scanned logs of Norway spruce

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