Wood defects classification using laws texture energy measures and supervised learning approach

H.A. Habib, M.H. Yousaf, M. Mohibullah, Modified laws energy descriptor for inspection of ceramic tiles, in: National conference on emerging technologies, vol. 40, 2004, pp. 137–140. B.S. Bama, S. Ramabai, R. Santhanadevi, Laws based quality inspection of steel products using scanning electron microscopy (SEM) images, in: IJCA Proceedings on National Conference on Advances in Computer Science and Applications (NCACSA), May 2012, pp. 25–29. Longuetaud, 2012, Automatic knot detection and measurements from X-ray CT images of wood: a review and validation of an improved algorithm on softwood samples, Comput. Electron. Agric., 85, 77, 10.1016/j.compag.2012.03.013 Sarigul, 2003, Rule-driven defect detection in CT images of hardwood logs, Comput. Electron. Agric., 41, 101, 10.1016/S0168-1699(03)00046-2 H. Mu, D. Qi, Pattern recognition of wood defects types based on Hu invariant moments, in: 2nd International Congress on Image and Signal Processing (CISP), IEEE, Tianjin, October 17–19, 2009, pp. 1–5. Mohan, 2014, Hybrid optimization for classification of the wood knots, J. Theor. Appl. Inf. Technol., 63, 774 Wang, 2009, Pattern recognition and size determination of internal wood defects based on wavelet neural networks, Comput. Electron. Agric., 69, 142, 10.1016/j.compag.2009.07.019 Yuce, 2014, Neural network design and feature selection using principal component analysis and Taguchi method for identifying wood veneer defects, Prod. Manuf. Res., 2, 291 R.W. Conners, C.W. Mcmillin, K. Lin, R.E. Vasquez-Espinosa, identifying and locating surface defects in wood: part of an automated lumber processing system, IEEE Trans. Pattern Anal. Mach. Intell. PAMI-5(6) (2009) 573–583. C.A. França, A. Gonzaga, Classification of wood plates by neural networks and fuzzy logic, 2010. Kim, 1994, Hierarchical classification of surface defects on dusty wood boards, Pattern Recogn. Lett., 15, 713, 10.1016/0167-8655(94)90076-0 Zhang, 2015, Using computer vision and compressed sensing for wood plate surface detection, Opt. Eng., 54, 10.1117/1.OE.54.10.103102 Ruz, 2009, Automated visual inspection system for wood defect classification using computational intelligence techniques, Int. J. Syst. Sci., 40, 163, 10.1080/00207720802630685 G.A. Ruz, P.A. Estéveza, Image segmentation using fuzzy min-max neural networks for wood defect detection, in: Intelligent Production Machines and Systems, First I* PROMS Virtual Conference, Elsevier, December, 2005, p. 183. P. Cavalin, L.S. Oliveira, A.L. Koerich, A.S. Britto Jr., Wood defect detection using grayscale images and an optimized feature set, in: 32nd Annual Conference on IEEE Industrial Electronics (IECON), November 6, 2006, pp. 3408–3412. Z. Zhang, N. Ye, D. Wu, Q. Ye, Locating the wood defects with typical features and SVM, in: 11th Joint Conference on Information Sciences, 2008. Y. Wang, H. Wang, L. Mo, Research on recognition of wood texture based on integrated neural network classifier, in: International Conference on Intelligent Control and Information Processing, IEEE, Dalian, China, August 13–15, 2010, pp. 512–515. M. Rizwan, K. Kamal, T. Zafar, S. Mathavan, Wood defects classification using GLCM based features and PSO trained neural network, in: 22nd IEEE International Conference on Automation and Computing (ICAC’16), Colchester, UK September 7–8, 2016. YongHua, 2015, Study on the identification of the wood surface defects based on texture features, Int. J. Light Electron Opt., 126, 2231, 10.1016/j.ijleo.2015.05.101 S. Shahnorbanun, S.A. Siti Nurul Huda, A. Haslina, O. Nazlia, H. Rosilah, A computational biological network for wood defect classification, in: Proceedings of the World Congress on Engineering and Computer Science (WCECS), San Francisco, USA, October 20–22, vol. I, 2010. Yu, 2010, A cluster-based wavelet feature extraction method and its application, Eng. Appl. Artif. Intell., 23, 196, 10.1016/j.engappai.2009.11.004 A. Mahram, M.G. Shayesteh, S. Jafarpour, Classification of wood surface defects with hybrid usage of statistical and textural features, in: 35th International Conference on Telecommunications and Signal Processing (TSP), IEEE, July, 2012, pp. 749–752. H. Kauppinen, H. Rautio, O. Silvén, Non-segmenting defect detection and SOM based classification for surface inspection using color vision, in: Conference on Polarization and Color Techniques in Industrial Inspection, Munich, Germany, June 17–18, vol. 3826, 1999, pp. 270–280. M. Niskanen, O. Silvén, H. Kauppinen, Color and texture based wood inspection with non-supervised clustering, in: Proceedings of the Scandinavian Conference on Image Analysis, June, 2001, pp. 336–342. Silven, 2003, Wood inspection with non-supervised clustering, Mach. Vis. Appl., 13, 275, 10.1007/s00138-002-0084-z Todoroki, 2010, Automated knot detection with visual post-processing of Douglas-fir veneer images, Comput. Electron. Agric., 70, 163, 10.1016/j.compag.2009.10.002 Funck, 2003, Image segmentation algorithms applied to wood defect detection, Comput. Electron. Agric., 41, 157, 10.1016/S0168-1699(03)00049-8 S. Radovan, P. George, M. Panagiotis, G. Manos, A. Robert, D. Igor, An approach for automated inspection of wood boards, in: International Conference on Image Processing, IEEE, vol. 1, Thessaloniki, 2001, pp. 798–801. Baradit, 2006, Knots detection in wood using microwaves, Wood Sci. Technol., 40, 118, 10.1007/s00226-005-0027-8 Dheeba, 2014, Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach, J. Biomed. Inform., 49, 45, 10.1016/j.jbi.2014.01.010 A.S. Setiawan, Elysia, J. Wesley, Y. Purnama, Mammogram classification using law’s texture energy measure and neural networks, in: Procedia Computer Science International Conference on Computer Science and Computational Intelligence (ICCSCI), vol. 59, 2015, pp. 92–97. Elnemr, 2013, Statistical analysis of law’s Mask texture features for cancer and water lung detection, Int. J. Comput. Sci. Issues (IJCSI), 10, 196 Rachidi, 2008, Laws’ masks descriptors applied to bone texture analysis: an innovative and discriminant tool in osteoporosis, Skeletal Radiol., 37, 541, 10.1007/s00256-008-0463-2 I. K. Valavanis, S.G. Mougiakakou, A. Nikita, K.S. Nikita, Evaluation of texture features in hepatic tissue characterization from non-enhanced CT images, in: Proceedings of the 29th Annual International Conference of the IEEE EMBS, Lyon, France, August 23–26, 2007. Wu, 1992, Texture features for classification of ultrasonic liver images, IEEE Trans. Med. Imaging, 11 K.J. Fauske, Neural network, December 7, 2006. Available: <http://www.texample.net/tikz/examples/neural-network/>. Visual Inspection of Lumber. Available: <http://www.ee.oulu.fi/~olli/Projects/Lumber.Grading.html>.