Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Feature recognition and fingerprint sensing for guiding a wood patching robot
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
2012 (English)In: World Conference on Timber Engineering: WCTE Auckland New Zealand 15-19 July 2012 / [ed] Pierre Quenneville, Auckland: New Zealand Timber Design Society , 2012, 724-733 p.Conference paper, Published paper (Other academic)
Abstract [en]

This paper includes a summary of a few commonly used object recognition techniques, as well as a sensitivity analysis of two feature point recognition methods. The robustness was analyzed by automatically trying to recognize 886 images of pine floorboards after applying different levels of distortions. Recognition was also tested on a subset of 5% of the boards which were both re-scanned using a line scan camera and photographed using a digital camera. Experiments showed that both the Block matching method and the SURF method are valid options for recognizing wood products covered with distinct features. The Block matching method outperformed the SURF method for small geometric distortions and moderate radiometric distortions. The SURF method, in its turn, performed better compared to the other method when faced with low resolution digital images.

Place, publisher, year, edition, pages
Auckland: New Zealand Timber Design Society , 2012. 724-733 p.
Keyword [en]
Information technology - Computer science
Keyword [sv]
Image analysis, Classification, Feature recognition, Fingerprint, Sensor fusion, Holonic, Patching robot, Wood, Hol-i-Wood PR, Informationsteknik - Datorvetenskap
National Category
Other Mechanical Engineering
Research subject
Wood Products Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-35703Local ID: a5a89059-d44a-43d5-95fd-13de2346604bISBN: 9781622763054 (print)OAI: oai:DiVA.org:ltu-35703DiVA: diva2:1008956
Conference
World Conference on Timber Engineering : 15/07/2012 - 19/07/2012
Projects
Holonic Integration of Cognition, Communication and Control for a Wood Patching Robot
Note
Godkänd; 2012; 20120816 (tobpah)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically 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

Open Access in DiVA

fulltext(1213 kB)25 downloads
File information
File name FULLTEXT01.pdfFile size 1213 kBChecksum SHA-512
248c8ba7cc17720ea93eb4f4aa11f5bc5441f5bccc0c92c4313c0ef05ef34099a56c3533fd34d8e0596bc40c30c91ca9a3955a60baabda48bf18328c1c991dd9
Type fulltextMimetype application/pdf

Authority records BETA

Pahlberg, TobiasHagman, Olle

Search in DiVA

By author/editor
Pahlberg, TobiasHagman, Olle
By organisation
Wood Science and Engineering
Other Mechanical Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 25 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 40 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf