Abstract
This paper addresses the problem of timber board positioning within the log where they were sawn from. The method takes as input log and board end cross-section images. It uses a two-step image matching method based on scale invariant feature transform and normalized correlation coefficient (NCC). In the first step, the scale factor and rotation angle of board end images are estimated from the board images that are correctly identified on the log end image by SIFT. Then, the accurate position of each board within the log end image is achieved by the NCC method. The method has been tested on 70 different log images and the 798 corresponding board images of various visual aspects and coming from three different species (Douglas fir, Norway spruce, and oak). The results fully demonstrate that the proposed method is not only rotation and scale invariant, but also has high accuracy properties.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
References
Aicher S, Christian Z, Hirsch M (2016) Rolling shear modulus and strength of beech wood laminations. Holzforschung 70(8):773–781. https://doi.org/10.1515/hf-2015-0229
Aicher S, Dill-Langer G (2005) Effect of lamination anisotropy and lay-up in glued-laminated timbers. J Struct Eng 131(7):1095–1103. https://doi.org/10.1061/(ASCE)0733-9445(2005)131:7(1095)
Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359. https://doi.org/10.1016/j.cviu.2007.09.014
Booker RE, Ward N, Williams Q (1992) A theory of cross-sectional shrinkage distortion and its experimental verification. Wood Sci Technol 26(5):353–368. https://doi.org/10.1007/BF00226077
Boukadida H, Longuetaud F, Colin F, Freyburger C, Constant T, Leban JM, Mothe F (2012) PithExtract: a robust algorithm for pith detection in computer tomography images of wood - Application to 125 logs from 17 tree species. Comput Electron Agric 85:90–98. https://doi.org/10.1016/j.compag.2012.03.012
Bradski G (2000) The OpenCV library. Dr. Dobb’s J Softw Tools
Breinig L, Broman O, Brüchert F, Becker G (2015) Optimization potential for perception-oriented appearance classification by simulated sawing of computed tomography-scanned logs of Norway spruce. Wood Mater Sci Eng 10(4):319–334. https://doi.org/10.1080/17480272.2014.977944
Calonder M, Lepetit V, Strecha C, Fua P (2010) BRIEF: binary robust independent elementary features, computer vision-ECCV 2010. Springer, Berlin Heidelberg, pp 778–792. https://doi.org/10.1007/978-3-642-15561-1_56
Dong PT (2013) A review on image feature extraction and representation techniques. Int J Multimed Ubiquitous Eng 8(4):385–395
Drewett TA (2015) The growth and quality of UK-grown Douglas-fir. Ph. D. thesis, Edinburgh Napier University
Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2012) The PASCAL visual object classes challenge 2012 results
Fischler MA, Bolles RC (1987) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Read Comput Vis. https://doi.org/10.1016/b978-0-08-051581-6.50070-2
Fredriksson M (2014) Log sawing position optimization using computed tomography scanning. Wood Mater Sci Eng 9(2):110–119. https://doi.org/10.1080/17480272.2014.904430
Habite T, Abdeljaber O, Olsson A (2021) Automatic detection of annual rings and pith location along Norway spruce timber boards using conditional adversarial networks. Wood Sci Technol. https://doi.org/10.1007/s00226-021-01266-w
Habite T, Abdeljaber O, Olsson A (2022) Determination of pith location along Norway spruce timber boards using one dimensional convolutional neural networks trained on virtual timber boards. Construct Build Mater 329:127129. https://doi.org/10.1016/j.conbuildmat.2022.127129
Habite T, Olsson A, Oscarsson J (2020) Automatic detection of pith location along Norway spruce timber boards on the basis of optical scanning. Eur J Wood Prod 78(6):1061–1074. https://doi.org/10.1007/s00107-020-01558-1
Hu M, Olsson A, Johansson M, Oscarsson J (2018) Modelling local bending stiffness based on fibre orientation in sawn timber. Eur J Wood Prod 76(6):1605–1621. https://doi.org/10.1007/s00107-018-1348-2
Huber JA, Broman O, Ekevad M, Oja J, Hansson L (2022) A method for generating finite element models of wood boards from X-ray computed tomography scans. Comput Struct 260:106702. https://doi.org/10.1016/j.compstruc.2021.106702
Kandler G, Lukacevic M, Füssl J (2016) An algorithm for the geometric reconstruction of knots within timber boards based on fibre angle measurements. Construct Build Mater 124(Supplement C):945–960. https://doi.org/10.1016/j.conbuildmat.2016.08.001
Karami E, Prasad S, Shehata M (2015) Image matching using sift, surf, brief and orb: Performance comparison for distorted images. In: 2015 newfoundland electrical and computer engineering conference
Kliger IR, Perstorper M, Johansson G (1998) Bending properties of Norway spruce timber. Comparison between fast- and slow-grown stands and influence of radial position of sawn timber. Ann Sci For 55(3):349–358. https://doi.org/10.1051/forest:19980306
Li X, Pot G, Ngo P, Viguier J, Penvern H (2023) An image processing method to recognize position of sawn boards within the log. https://doi.org/10.57745/XCVQSG
Longuetaud F, Leban JM, Mothe F, Kerrien E, Berger MO (2004) Automatic detection of pith on CT images of spruce logs. Comput Electron Agric 44(2):107–119. https://doi.org/10.1016/j.compag.2004.03.005
Longuetaud F, Mothe F, Leban JM (2007) Automatic detection of the heartwood/sapwood boundary within Norway spruce (Picea abies (L.) Karst.) logs by means of CT images. Comput Electron Agric 58(2):100–111. https://doi.org/10.1016/j.compag.2007.03.010
Longuetaud F, Pot G, Mothe F, Barthelemy A, Decelle R, Delconte F, Ge X, Guillaume G, Mancini T, Ravoajanahary T, Butaud JC, Collet R, Debled-Rennesson I, Marcon B, Ngo P, Roux B, Viguier J (2022) Traceability and quality assessment of Douglas fir (Pseudotsuga menziesii (Mirb.) Franco) logs: the TreeTrace_douglas database. Annals For Sci. https://doi.org/10.1186/s13595-022-01163-7
Lowe D (1999) Object recognition from local scale-invariant features. In: Proceedings of the seventh IEEE international conference on computer vision. IEEE
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110. https://doi.org/10.1023/b:visi.0000029664.99615.94
Lukacevic M, Kandler G, Hu M, Olsson A, Füssl J (2019) A 3D model for knots and related fiber deviations in sawn timber for prediction of mechanical properties of boards. Mater Des 166:107617. https://doi.org/10.1016/j.matdes.2019.107617
Muja M, Lowe DG (2009) Fast approximate nearest neighbors with automatic algorithm configuration. In: proceedings of the fourth international conference on computer vision theory and applications. SciTePress-Science and and Technology Publications
Olsson A, Pot G, Viguier J, Hu M, Oscarsson J (2022) Performance of timber board models for prediction of local bending stiffness and strength-with application on douglas fir sawn timber. Wood Fiber Sci 54(4):226–245
Ormarsson S, Dahlblom O, Johansson M (2009) Finite element study of growth stress formation in wood and related distortion of sawn timber. Wood Sci Technol 43(5):387–403. https://doi.org/10.1007/s00226-008-0209-2
Ormarsson S, Dahlblom O, Petersson H (2000) A numerical study of the shape stability of sawn timber subjected to moisture variation - Part 3: influence of annual ring orientation. Wood Sci Technol 34(3):207–219. https://doi.org/10.1007/s002260000042
Perstorper M, Pellicane PJ, Kliger IR, Johansson G (1995) Quality of timber products from Norway spruce. Wood Sci Technol 29(3):157–170. https://doi.org/10.1007/BF00204581
Rais A, Ursella E, Vicario E, Giudiceandrea F (2017) The use of the first industrial X-ray CT scanner increases the lumber recovery value: case study on visually strength-graded Douglas-fir timber. Ann For Sci 74(2):1–9. https://doi.org/10.1007/s13595-017-0630-5
Rublee E, Rabaud V, Konolige K, Bradski G (2011) ORB: an efficient alternative to SIFT or SURF. In: 2011 international conference on computer vision. IEEE
Shmulsky R, Dahlen J (2007) Influence of sawing solution and pith location on warp in 2 by 4 lumber sawn from small-diameter loblolly pine stems. Forest Prod J 57(7):73
Swaroop P, Sharma N (2016) An overview of various template matching methodologies in image processing. Int J Comput Appl 153(10):8–14. https://doi.org/10.5120/ijca2016912165
Uranishi Y (2018) Opencv: open source computer vision library. J Inst Image Inf Telev Eng
Acknowledgements
This research is funded by the French national research agency (EffiQuAss project ANR-21-CE10-0002-01). The authors are very grateful to Bongard-Bazot & Fils company and its employees for allowing us to perform the sampling and sawing.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Li, X., Pot, G., Ngo, P. et al. An image processing method to recognize position of sawn boards within the log. Wood Sci Technol 57, 1401–1420 (2023). https://doi.org/10.1007/s00226-023-01495-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00226-023-01495-1