On the suitability of colour and texture analysis for detecting the presence of bark on a log

Weidenhiller, Andreas; Denzler, Julia K.

doi:10.1016/j.compag.2014.05.010

Cited by 6 publications

(7 citation statements)

References 13 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%

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%

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

Weidenhiller

Denzler

Golser

2015

Scandinavian Journal of Forest Research

Self Cite

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“…Recent work in this area closely related to wood defect detection involves detecting bark on logs [9]. According to Weidenhiller and Denzler [9], utilizing texture features derived from dependence matrices has produced promising results in bark detection.…”

Section: Methodsmentioning

confidence: 99%

“…According to Weidenhiller and Denzler [9], utilizing texture features derived from dependence matrices has produced promising results in bark detection. Findings of a defect classification study on plywood noted that features from a dependence matrix were useful in separating clear wood and defects [10].…”

Section: Methodsmentioning

confidence: 99%

“…2, July 2017, pp. 56-67 work of Weidenhiller and Denzler [9] and our defect detection problem and given that both focus on wood products (logs and timber) which are, therefore, likely to be similar in appearance inspired us to investigate similar statistics to those employed in their study. However, our approach to generating the dependence matrix differed from their approach as our work considers many types of defects rather than bark detection only.…”

Section: Methodsmentioning

confidence: 99%

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Systematic feature analysis on timber defect images

Hashim

Muda

et al. 2017

Int. J. Adv. Intell. Informatics

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Feature extraction is unquestionably an important process in a pattern recognition system. A defined set of features makes the identification task more efficiently. This paper addresses the extraction and analysis of features based on statistical texture to characterize images of timber defects. A series of procedures including feature extraction and feature analysis was executed to construct an appropriate feature set that could significantly separate amongst defects and clear wood classes. The feature set aimed for later use in a timber defect detection system. For Accessing the discrimination capability of the features extracted, visual exploratory analysis and confirmatory statistical analysis were performed on defect and clear wood images of Meranti (Shorea spp.) timber species. Results from the analysis demonstrated that there was a significant distinction between defect classes and clear wood utilizing the proposed set of texture features.

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“…For example, the images can be used to increase the yield and marginable profit by pre-calculating the cutting sequence within sawmills [21,23,24]. Concerning bark measurements, the systems are able to provide additional bark information, such as the debarking percentage and absolute and relative bark volumes in comparison to the total stem volume [25,26]. The systems mentioned above have all been tested and generally perform very well.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Photo-Based Measurement System to Calculate the Debarking Percentages of Processed Logs

et al. 2019

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Within a research project investigating the applicability and performance of modified harvesting heads used during the debarking of coniferous tree species, the actual debarking percentage of processed logs needed to be evaluated. Therefore, a computer-based photo-optical measurement system (Stemsurf) designed to assess the debarking percentage recorded in the field was developed, tested under laboratory conditions, and applied in live field operations. In total, 1720 processed logs of coniferous species from modified harvesting heads were recorded and analyzed within Stemsurf. With a single log image as the input, the overall debarking percentage was calculated by further estimating the un-displayed part of the log surface by defining polygons representing the differently debarked areas of the log surface. To assess the precision and bias of the developed measurement system, 480 images were captured under laboratory conditions on an artificial log with defined surface polygons. Within the laboratory test, the standard deviation of average debarking percentages remained within a 4% variation. A positive bias of 6.7% was caused by distortion and perspective effects. This resulted in an average underestimation of 1.1% for the summer debarking percentages gathered from field operations. The software generally performed as anticipated through field and lab testing and offered a suitable alternative of assessing stem debarking percentage, a task that should increase in importance as more operations are targeting debarked products.

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On the suitability of colour and texture analysis for detecting the presence of bark on a log

Cited by 6 publications

References 13 publications

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

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

Systematic feature analysis on timber defect images

Development and Validation of a Photo-Based Measurement System to Calculate the Debarking Percentages of Processed Logs

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