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Wood fingerprint recognition using knot neighborhood K-plet descriptors
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.ORCID iD: 0000-0001-8404-7356
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-6186-7116
2015 (English)In: Wood Science and Technology, ISSN 0043-7719, E-ISSN 1432-5225, Vol. 49, no 1, 7-20 p.Article in journal (Refereed) Published
Abstract [en]

In the wood industry, there is a wish to recognize and track wood products through production chains. Traceability would facilitate improved process control and extraction of quality measures of various production steps. In this paper, a novel wood surface recognition system that uses scale and rotationally invariant feature descriptors called K-plets is described and evaluated. The idea behind these descriptors is to use information of how knots are positioned in relation to each other. The performance and robustness of the proposed system were tested on 212 wood panel images with varying levels of normally distributed errors applied to the knot positions. The results showed that the proposed method is able to successfully identify 99–100 % of all panel images with knot positional error levels that can be expected in practical applications

Place, publisher, year, edition, pages
2015. Vol. 49, no 1, 7-20 p.
National Category
Other Mechanical Engineering Signal Processing
Research subject
Signal Processing; Wood Science and Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-7733DOI: 10.1007/s00226-014-0679-3Local ID: 6266a684-7c00-49d2-ac80-b65cd350c889OAI: oai:DiVA.org:ltu-7733DiVA: diva2:980623
Note

Validerad; 2015; Nivå 2; 20140429 (erikjo)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved
In thesis
1. Wood fingerprint recognition and detection of thin cracks
Open this publication in new window or tab >>Wood fingerprint recognition and detection of thin cracks
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The first part of this thesis deals with recognition of wood fingerprints extracted from timber surfaces. It presents different methods to track sawn wood products through an industrial process using cameras. The possibility of identifying individual wood products comes from the biological variation of trees, where the genetic code, environment, and breakdown process means that every board has a unique appearance. Wood fingerprint recognition experiences many of the same challenges as found in human biometrics applications. 

The vision for the future is to be able to utilize existing imaging sensors in the production line to track individual products through a disordered and diverging product flow. The flow speed in wood industries is usually very high, 2-15 meters per second, with a high degree of automation. Wood fingerprints combined with automated inspection makes it possible to tailor subsequent processing steps for each product and can be used to deliver customized products. Wood tracking can also give the machine operators vital feedback on the process parameters. 

The motivation for recognition comes from the need for the wood industry to keep track of products without using invasive methods, such as bar code stickers or painted labels. In the project Hol-i-Wood Patching Robot, an automatic scanner- and robot system was developed. In this project, there was a wish to keep track of the shuttering panels that were going to be repaired by the automatic robots. 

In this thesis, three different strategies to recognize previously scanned sawn wood products are presented. The first approach uses feature detectors to find matching features between two images. This approach proved to be robust, even when subjected to moderate geometric- and radiometric image distortions. The recognition accuracy reached 100% when using high quality scans of Scots pine boards that had more than 20 knots. 

The second approach uses local knot neighborhood geometry to find point matches between images. The recognition accuracy reached above 99% when matching simulated Scots pine panels with realistically added noise to the knot positions, given the assumption that 85% of the knots could be detected.

The third approach uses template matching to match a small part of a board against a large set of full-length boards. Cropping and heavy downsampling was implemented in this study. The intensity normalized algorithms using cross-correlation (CC-N) and correlation coefficient (CCF-N) obtained the highest recognition accuracy and had very similar overall performance. For instance, the matching accuracy for the CCF-N method reached above 99% for query images of length 1 m when the pixel density was above 0.08 pixels/mm.

The last part of this thesis deals with the detection of thin cracks on oak flooring lamellae using ultrasound-excited thermography and machine learning. Today, many people manually grade and detect defects on wooden lamellae in the parquet flooring industry. The last appended paper investigates the possibility to use ensemble methods random forests and boosting to automate the process. When friction occurs in thin cracks they become warm and thus visible for a thermographic camera. Several image processing techniques were used to suppress noise and enhance likely cracks in the images. The best ensemble methods reached an average classification accuracy of 0.8, which was very close to the authors own manual attempt at separating the images (0.83).

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2017. 168 p.
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Wood Science
Research subject
Wood Science and Engineering
Identifiers
urn:nbn:se:ltu:diva-65701 (URN)978-91-7583-967-7 (ISBN)978-91-7583-968-4 (ISBN)
Public defence
2017-10-20, Hörsal A, Skellefteå, 09:00 (English)
Opponent
Supervisors
Funder
EU, FP7, Seventh Framework Programme, 284573
Available from: 2017-09-19 Created: 2017-09-18 Last updated: 2017-11-24Bibliographically approved

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Pahlberg, TobiasJohansson, ErikHagman, OlleThurley, Matthew
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