[1] Zhou Z, Yin J X, Zhou S Y et al. Knot defection on coniferous wood surface by near infrared spectroscopy and successive projections algorithm[J]. Laser & Optoelectronics Progress, 54, 023001(2017).
[2] Norlander R, Grahn J, Maki A. Wooden knot detection using convNet transfer learning[M]. Lecture Notes in Computer Science, 9127, 263-274(2015).
[3] Ma X, Liu Y A, Ye N et al. Application of KPCA and SVM to wood defect recognition[J]. Journal of Changzhou University(Natural Science Edition), 29, 60-68(2017).
[4] Song X Y, Bai F Z, Wu J X et al. Wood knot defects recognition with gray-scale histogram features[J]. Laser & Optoelectronics Progress, 52, 031501(2015).
[5] Cetiner S, Var A A, Cetiner H. Classification of KNOT defect types[C]. Signal Processing and Communications Applications Conference, 1086-1089(2014).
[6] Zhang Z, Ye N, Ye Q L. Automatic wood defects recognition based on texture extraction and support vector machine technology[J]. Computer Engineering and Applications, 45, 219-223(2009).
[8] Cai R T, Zhu P. Face tracking with muli-feature based on Markov random field[J]. Laser & Optoelectronics Progress, 54, 021002(2017).
[10] Wu P. Image segmentation method based on firefly algorithm and maximum entropy method[J]. Computer Engineering and Applications, 50, 115-119(2014).
[12] Gao X J, Zheng X D, Liu Z X et al. Automatic building extraction from high resolution visible images based on shifted shadow analysis[J]. Acta Optica Sinica, 37, 0428002(2017).
[13] Sammut C, Webb G I. Encyclopedia of machine learning[M]. New York: Springer Science & Business Media(2011).
[15] Zhan Y J, Wang H M, Fu X H et al. Identification of steel plate damage position based on particle swarm support vector machine[J]. Chinese Journal of Lasers, 44, 1006006(2017).
[16] Shi F, Wang X C, Yu L et al[M]. MATLAB neural network analysis of 30 cases(2011).