RETRACTED: Artificial neural network for defect detection in CT images of wood

Achille, 2018, Information dropout: Learning optimal representations through noisy computation, IEEE Trans. Pattern Anal. Mach. Intell., 40, 2897, 10.1109/TPAMI.2017.2784440 Andreu, J.P., Rinnhofer, A., 2003. Modeling of internal defects in logs for value optimization based on industrial CT scanning. In: Fifth International Conference on Image Processing and Scanning of Wood. Bad Waltersdorf Austria, pp. 23-26. Archibald, 2016, Image reconstruction from undersampled Fourier data using the polynomial annihilation transform, J. Sci. Comput., 67, 432, 10.1007/s10915-015-0088-2 Beaulieu, 2019, Applications of computed tomography (CT) scanning technology in forest research: A timely update and review, Can. J. For. Res., 49, 1173, 10.1139/cjfr-2018-0537 Boukadida, 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, 10.1016/j.compag.2012.03.012 Chang, 2018, A novel image segmentation approach for wood plate surface defect classification through convex optimization, J. For. Res., 29, 1789, 10.1007/s11676-017-0572-7 Couceiro, J., Hansson, L., Sehlstedt-Persson, M., Sandberg, D., 2016. The use of X-ray computed tomography in timber construction research. In: Forest Products Society International Convention. Forest Products Society, pp. 1-8. Cristhian, 2008, Detection of knots using X-ray tomographies and deformable contours with simulated annealing, Wood Res., 53, 57 Davis, 1992, Computed tomographymeasurements on wood, Industrial Metrology, 2, 195, 10.1016/0921-5956(92)80004-D Fredriksson, 2017, Knot detection in computed tomography images of partially dried jack pine (Pinus banksiana) and white spruce (Picea glauca) logs from a Nelder type plantation, Can. J. For. Res., 47, 910, 10.1139/cjfr-2016-0423 Freyburger, 2009, Measuring wood density by means of X-ray computer tomography, Ann. For. Sci., 66, 804, 10.1051/forest/2009071 Gergeľ, 2019, Computed tomography log scanning–high technology for forestry and forest based industry, Cent. Eur. For. J., 65, 51 Gergeľ, 2020, Prediction model of wooden logs cutting patterns and its efficiency in practice, Appl. Sci., 10, 3003, 10.3390/app10093003 Goel, 2016, Convolution and correlation theorems for the offset fractional Fourier transform and its application, AEU-Int. J. Electron. C., 70, 138, 10.1016/j.aeue.2015.10.009 Halabe, 2009, Nondestructive evaluation of wooden logs using ground penetrating radar, Nondestruct. Test. Eva., 24, 329, 10.1080/10589750802474344 Hassani, 2018, Studying and detecting log-related issues, Empir. Softw. Eng., 23, 3248, 10.1007/s10664-018-9603-z Ignjic, 2019, Computer Tomography Tube Voltage and Phantom Dimensions Influence on the Number of Hounsfield Units, 111 Kamal, 2017, Wood defects classification using laws texture energy measures and supervised learning approach, Adv. Eng. Inform., 34, 125, 10.1016/j.aei.2017.09.007 Khan, 2018, A methodological review of 3D reconstruction techniques in tomographic imaging, J. Med. Syst., 42, 190, 10.1007/s10916-018-1042-2 Khatami, 2018, A radon-based convolutional neural network for medical image retrieval, International Journal of Engineering-Transactions C: Aspects, 31, 910 Krähenbühl, 2012, Knot detection in x-ray ct images of wood, 209 Li, 2015, The method of wood defect recognition based on PSO feature selection and compressed sensing, J. Beijing Forestry Univ., 37, 117 Li, 2017, Soft measurement of wood defects based on LDA feature fusion and compressed sensor images, J. For. Res., 28, 1285, 10.1007/s11676-017-0395-6 Liu, D., Yu, J., 2009. Otsu method and K-means. In: 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, Vol. 1, pp. 344-349. 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 Mu, 2015, The application of RBF neural network in the wood defect detection, Int. J. Hybrid Inf. Technol., 8, 41 Nguyen, 2016, Segmentation of defects on log surface from terrestrial lidar data, 3168 Nordmark, 2002, Knot identification from CT images of young Pinus sylvestris sawlogs using artificial neural networks, Scand. J. For. Res., 17, 72, 10.1080/028275802317221109 Olofsson, 2019, New possibilities with CT scanning in the forest value chain, 1 Osborne, 2016, Modeling knot geometry from branch angles in Douglas-fir (Pseudotsuga menziesii), Can. J. For. Res., 46, 215, 10.1139/cjfr-2015-0145 Parajuli, 2016, Price linkages between spot and futures markets for softwood lumber, For. Sci., 62, 482, 10.5849/forsci.16-019 Park, 2017, Retrieval of sentence sequences for an image stream via coherence recurrent convolutional networks, IEEE Trans. Pattern Anal. Mach. Intell., 40, 945, 10.1109/TPAMI.2017.2700381 Rais, 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, 28, 10.1007/s13595-017-0630-5 Rojas, 2013 Roussel, 2014, Automatic knot segmentation in CT images of wet softwood logs using a tangential approach, Comput. Electron. Agric., 104, 46, 10.1016/j.compag.2014.03.004 Rummukainen, 2019, Economic value of optical and X-ray CT scanning in bucking of Scots pine, Wood Mater. Sci. Eng., 1, 1 Ruz, 2005, A neurofuzzy color image segmentation method for wood surface defect detection, Forest Prod. J., 55, 52 Schafer, M.E., 2000. Ultrasound for defect detection and grading in wood and lumber. In: 2000 IEEE Ultrasonics Symposium. Proceedings. An International Symposium (Cat. No. 00CH37121). IEEE, Vol. 1, pp. 771-778. Schmoldt, D.L., He, J., Abbott, A.L., 1998. Comparison of several artificial neural network classifiers for CT images of hardwood logs. In: Machine vision applications in industrial inspection VI. International Society for Optics and Photonics, Vol. 3306, pp. 34-43. Schmoldt, 1997, Machine vision using artificial neural networks with local 3D neighborhoods, Comput. Electron. Agric., 16, 255, 10.1016/S0168-1699(97)00002-1 Thomas, 2017, An artificial neural network for real-time hardwood lumber grading, Comput. Electron. Agric., 132, 71, 10.1016/j.compag.2016.11.018 Thomas, 2007, Defect detection on hardwood logs using laser scanning, Wood Fiber Sci., 38, 682 Thomas, 2006, Automated detection of severe surface defects on barked hardwood logs, Forest Prod. J., 57, 50 Wei, 2009, Identification of selected internal wood characteristics in computed tomography images of black spruce: a comparison study, J. Wood Sci., 55, 175, 10.1007/s10086-008-1013-1 Wu, 2010, Wood defect recognition based on affinity propagation clustering, 1 Yao, 2017, Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application, IEEE Trans. Ind. Electron., 65, 1490, 10.1109/TIE.2017.2733448 Zhang, 2015, Wood defect detection method with PCA feature fusion and compressed sensing, J. For. Res., 26, 745, 10.1007/s11676-015-0066-4 Zhang, 2018, Artificial neural network, 1