Comparison of different approaches for automatic bark detection on log images

Denzler, Julia K.; Weidenhiller, Andreas; Golser, Michael

doi:10.1007/s00226-013-0536-9

Cited by 9 publications

(2 citation statements)

References 6 publications

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“…The method "T" was based on thresholds for the tracheid data. For further details, see Denzler et al (2013) as well as Weidenhiller and Denzler (2014).…”

Section: Methods For Bark Detectionmentioning

confidence: 99%

“…3D scanners), so the tracheid effect can easily be used for bark detection if some cameras for filming the laser line are added. Colour images of logs can also be used to distinguish bark and wood if suitable image processing algorithms are applied (Nilsson & Edlund 2005;Denzler et al 2013). For documentation purposes, imaging systems are already in use in many sawmills.…”

Section: Introductionmentioning

confidence: 99%

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Detecting spruce and fir log diameter under bark by using shape and image data

Weidenhiller

Denzler

Golser

2015

Scandinavian Journal of Forest Research

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The sawmilling industry stores and measures logs in bark in order to maximise efficiency, quality conservation and preservation. Billing is based on the diameter under bark, estimated based on manual or automatic bark detection. Although an automatic system is desirable for the industry, existing systems like tracheid effect scanners, X-ray or computed tomography either do not work reliably during all seasons or are very cost-intensive. This paper presents an approach for automatic determination of diameter under bark based on a multi-sensor approach including shape data, colour image data and tracheid effect data using laser scanning. For 686 spruce (Picea abies) and 79 fir (Abies alba) logs, diameters under bark are estimated and compared to the diameter after machine debarking. Estimation errors are close to zero and are below ±10 mm for more than 94% of the logs. Influences of season or characteristics like bark beetle are small. Specialised algorithms for diameter estimation in the presence of snow or bark beetle might further improve the result at the cost of required extra manual input.

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“…The method "T" was based on thresholds for the tracheid data. For further details, see Denzler et al (2013) as well as Weidenhiller and Denzler (2014).…”

Section: Methods For Bark Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%