Classification of wood defect images using local binary pattern variants

(1) * Rahillda Nadhirah Norizzaty Rahiddin Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(2) Ummi Rabaah Hashim Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(3) Nor Haslinda Ismail Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(4) Lizawati Salahuddin Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(5) Ngo Hea Choon Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(6) Siti Normi Zabri Mail (Centre for Telecommunication Research & Innovation, Universiti Teknikal Malaysia Melaka, Malaysia)
*corresponding author

Abstract


This paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP. For significantly discriminating, the wood defect classes were further evaluated with the use of different classifiers. By comparing the results of the classification performances that had been conducted across the multiple wood species, the Uniform LBP was found to have demonstrated the highest accuracy level in the classification of the wood defects.

Keywords


Automated visual inspection; Defect detection; Wood inspection; Wood defect detection; Local binary pattern

   

DOI

https://doi.org/10.26555/ijain.v6i1.392
      

Article metrics

Abstract views : 347 | PDF views : 68

   

Cite

   

Full Text

Download

References


[1] I. Cetiner, A. A. Var, and H. Cetiner, “Classification of Knot Defect Types Using Wavelets and KNN,” Elektron. ir Elektrotechnika, vol. 22, no. 6, pp. 67–72, 2016, doi: 10.5755/j01.eie.22.6.17227.

[2] U. R. Hashim, S. Z. Hashim, and A. K. Muda, “Image Collection for Non-Segmenting Approach of Timber Surface Defect Detection,” Int. J. Adv. Soft Comput. its Appl., vol. 7, no. 1, pp. 15–34, 2015, available at: Google Scholar.

[3] R. Qayyum, K. Kamal, T. Zafar, and S. Mathavan, “Wood Defects Classification Using GLCM Based Features And PSO Trained Neural Network,” 2016 22nd Int. Conf. Autom. Comput. ICAC 2016 Tackling New Challenges Autom. Comput., pp. 273–277, 2016, doi: 10.1109/IConAC.2016.7604931.

[4] Y. Zhang, S. Liu, W. Tu, H. Yu, and C. Li, “Using computer vision and compressed sensing for wood plate surface detection,” Opt. Eng., vol. 54, no. 10, pp. 103102-1-103102–10, 2015, doi: 10.1117/1.OE.54.10.103102.

[5] Y. Zhang, C. Xu, C. Li, H. Yu, and J. Cao, “Wood defect detection method with PCA feature fusion and compressed sensing,” J. For. Res., vol. 26, no. 3, pp. 745–751, 2015, doi: 10.1007/s11676-015-0066-4.

[6] L. Wang, L. Li, W. Qi, and H. Yang, “Pattern recognition and size determination of internal wood defects based on wavelet neural networks,” Comput. Electron. Agric., vol. 69, no. 2, pp. 142–148, Dec. 2009, doi: 10.1016/j.compag.2009.07.019.

[7] S. Ni, H. Xu, and L. Wang, “Quantitative identification of defects in lumber based on modal frequencies and artificial neural network,” Adv. Mater. Res., vol. 183–185, pp. 2279–2283, 2011, doi: 10.4028/www.scientific.net/AMR.183-185.2279.

[8] Z. Y. Xiang, Z. Y. Qin, L. Ying, J. L. Quan, and C. Z. Wei, “Identification of Wood Defects Based on LBP Features,” in Chinese Control Conference, CCC, 2016, pp. 4202–4205, doi: 10.1109/ChiCC.2016.7554010.

[9] N. Rosa da Silva et al., “Automated classification of wood transverse cross-section micro-imagery from 77 commercial Central-African timber species,” Ann. For. Sci., vol. 74, no. 2, 2017, doi: 10.1007/s13595-017-0619-0.

[10] M. M. Hittawe, S. M. Muddamsetty, D. Sidibe, and F. Meriaudeau, “Multiple Features Extraction For Timber Defects Detection and Classification Using SVM,” Int. Conf. Image Process., pp. 427–431, 2015, doi: 10.1109/ICIP.2015.7350834.

[11] P. S. Hiremath and R. A. Bhusnurmath, “Multiresolution LDBP descriptors for texture classification using anisotropic diffusion with an application to wood texture analysis,” Pattern Recognit. Lett., vol. 89, pp. 8–17, 2017, doi: 10.1016/j.patrec.2017.01.015.

[12] Y. Wang, C. Shi, C. Wang, and B. Xiao, “Ground-based Cloud Classification By Learning Stable Local Binary Patterns,” Atmos. Res., vol. 207, pp. 74–89, 2018, doi: 10.1016/j.atmosres.2018.02.023.

[13] S. Pervan, M. Brezovic, S. Prekrat, M. Klaric, and G. Sazdevski, “Possibilities for Thermography Application in Hydrothermal Wood Processing,” Drv. Ind., vol. 63, no. 4, pp. 277–281, 2012, doi: 10.5552/drind.2012.1209.

[14] G. Lopez, L. A. Basterra, G. R. Cueto, and A. Diego, “Detection of Singularities and Subsurface Defects in Wood by Infrared Thermography,” Int. J. Archit. Herit., vol. 8, no. 4, pp. 517–536, 2014, doi: 10.1080/15583058.2012.702369.

[15] U. R. Hashim, S. Z. M. Hashim, and A. K. Muda, “Performance evaluation of multivariate texture descriptor for classification of timber defect,” Optik (Stuttg)., vol. 127, no. 15, pp. 6071–6080, 2016, doi: 10.1016/j.ijleo.2016.04.005.

[16] L. Wenshu, S. Lijun, and W. Jinzhuo, “Study on Wood Board Defect Detection Based on Artificial Neural Network,” Open Autom. Control Syst. J., vol. 7, no. 1, pp. 290–295, 2015, doi: 10.2174/1874444301507010290.

[17] C. Hu, X. Min, H. Yun, T. Wang, and S. Zhang, “Automatic detection of sound knots and loose knots on sugi using gray level co-occurrence matrix parameters,” Ann. For. Sci., vol. 68, no. 6, pp. 1077–1083, 2011, doi: 10.1007/s13595-011-0123-x.

[18] Z. Chang, J. Cao, and Y. Zhang, “A novel image segmentation approach for wood plate surface defect classification through convex optimization,” J. For. Res., pp. 1–7, 2018, doi: 10.1007/s11676-017-0572-7.

[19] X. Y. Hua and W. J. Cong, “Study on the identification of the wood surface defects based on texture features,” Optik (Stuttg)., vol. 126, no. 19, pp. 2231–2235, 2015, doi: 10.1016/j.ijleo.2015.05.101.

[20] M. Khalid, E. L. Y. Lee, R. Yusof, and M. Nadaraj, “Design of an Intelligent Wood Species Recognition System,” Int. J. Simul. Syst. Sci. Technol., vol. 9, no. 3, pp. 9–19, 2008, available at: Google Scholar.

[21] M. Corradi, T. P. Vo, K. Poologanathan, and A. I. Osofero, “Flexural behaviour of hardwood and softwood beams with mechanically connected GFRP plates,” Compos. Struct., vol. 206, pp. 610–620, 2018, doi: 10.1016/j.compstruct.2018.08.056.

[22] M. D. Burnard, L. Muszyński, S. Leavengood, and L. Ganio, “An optical method for rapid examination of check development in decorative plywood panels,” Eur. J. Wood Wood Prod., vol. 76, no. 5, pp. 1367–1377, 2018, doi: 10.1007/s00107-018-1327-7.

[23] J. Zhang, Y. Gao, J. Zhang, and X. Zhu, “Influence of pretreated wood dowel with CuCl2 on temperature distribution of wood dowel rotation welding,” J. Wood Sci., vol. 64, no. 3, pp. 209–219, 2018, doi: 10.1007/s10086-017-1693-5.

[24] I. M. Khairuddin et al., “Automatic Classification of Wood Texture Using Local Binary Pattern & Fuzzy K-Nearest Neighbor,” Adv. Mater. Res., vol. 903, pp. 315–320, 2014, doi: 10.4028/www.scientific.net/AMR.903.315.

[25] M. Nasirzadeh, A. A. Khazael, and M. Khalid, “Woods Recognition System Based on Local Binary Pattern,” Second Int. Conf. Comput. Intell. Commun. Syst. Networks, no. 2, pp. 2–7, 2010, doi: 10.1109/CICSyN.2010.27.

[26] Prasetiyo, M. Khalid, R. Yusof, and F. Meriaudeau, “A Comparative Study of Feature Extraction Methods for Wood Texture Classification,” 2010 Sixth Int. Conf. Signal-Image Technol. Internet Based Syst., pp. 23–29, 2010, doi: 10.1109/SITIS.2010.15.

[27] R. Li and E. H. Adelson, “Sensing and recognizing surface textures using a gelsight sensor,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013, pp. 1241–1247, doi: 10.1109/CVPR.2013.164.

[28] D. Huang, C. Shan, M. Ardabilian, Y. Wang, and L. Chen, “Local Binary Patterns and Its Application to Facial Image Analysis: A Survey,” Ieee Trans. Syst. Man Cybern. Part C-Applications Rev., vol. 41, no. 6, pp. 765–781, 2011, doi: 10.1109/TSMCC.2011.2118750.

[29] T. Ojala, M. Pietikäinen, and D. Harwood, “Performance evaluation of texture measures with classification based on Kullback discrimination of distributions,” Proc. - Int. Conf. Pattern Recognit., vol. 3, pp. 582–585, 1994, doi: 10.1109/ICPR.1994.576366.

[30] L. Wang and D.-C. He, “Texture Classification Using Texture Spectrum,” Pattern Recognit., vol. 23, no. 8, pp. 905–910, 1990, doi: 10.1016/0031-3203(90)90135-8.

[31] M. Pietikäinen, T. Ojala, and Z. Xu, “Rotation-invariant texture classification using feature distributions,” Pattern Recognit., vol. 33, no. 1, pp. 43–52, 2000, doi: 10.1016/S0031-3203(99)00032-1.

[32] T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, 2002, doi: 10.1109/TPAMI.2002.1017623.




Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by Informatics Department - Universitas Ahmad Dahlan ,  UTM Big Data Centre - Universiti Teknologi Malaysia, and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W : http://ijain.org
E : info@ijain.org, andri.pranolo@tif.uad.ac.id (paper handling issues)
     ijain@uad.ac.id, andri.pranolo.id@ieee.org (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0