[1] 房友盼．基于图像识别的实木板材优选系统研究[D]．南京: 南京林业大学, 2016．

    FANG Y P. Research on optimization system of solid wood based on image recognition [D]. Nanjing: Nanjing Forestry University, 2016. (in Chinese)

[2] 赵鹏, 赵匀, 陈广胜．基于3D扫描技术的木材缺陷定量化分析[J]．农业工程学报, 2017, 33(7): 171-176．

    ZHAO P, ZHAO Y, CHEN G S. Quantitative analysis of wood defect based on 3D scanning technique [J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(7): 171-176. (in Chinese)

[3] 李佳, 王阿川, 马欣然．基于多模型融合的木材表面缺陷图像快速识别[J]．东北林业大学学报, 2014, 42(12): 114-118, 140．

    LI J, WANG A C, MA X R. Fast recognition for wood surface defect image based on multi-model fusion [J]. Journal of Northeast Forestry University, 2014, 42(12): 114-118, 140. (in Chinese)

[4] 张富文, 许清风, 张治宇, 等．钻入阻抗法检测木材缺陷[J]．无损检测, 2016, 38(1): 6-9, 74．

    ZHANG F W, XU Q F, ZHANG Z Y, et al. Wood defect inspection by drilling resistance method [J]. Nondestructive Testing, 2016, 38(1): 6-9, 74. (in Chinese)

[5] 刘嘉新, 吴彤, 王克奇．基于C-V模型的木材缺陷重建图像特征提取[J]．东北林业大学学报, 2015, 43(12): 78-81．

    LIU J X, WU T, WANG K Q. Feature extraction of wood defect reconstructed images based on the C-V model [J]. Journal of Northeast Forestry University, 2015, 43(12): 78-81. (in Chinese)

[6] PACKIANATHER M S, DRAKE P R. Neural networks for classifying images of wood veneer. Part 2 [J]. The International Journal of Advanced Manufacturing Technology, 2000, 16(6): 424-433.

[7] 熊伟俊, 杨绪兵, 云挺, 等．基于快速l1算法和LBP算法的木材缺陷识别[J]．数据采集与处理, 2017, 32(6): 1223-1231．

    XIONG W J, YANG X B, YUN T, et al. Wood defect recognition based on fast l1 algorithm and LBP algorithm [J]. Journal of Data Acquisition and Processing, 2017, 32(6): 1223-1231. (in Chinese)

[8] 王阿川, 仇逊超．木材缺陷识别新方法——改进C-V模型与小波变换[J]．计算机工程与应用, 2011, 47(8): 211-214, 235．

    WANG A C, QIU X C. New method of lumber recognition using improved C-V model and wavelet transform [J]. Computer Engineering and Applications, 2011, 47(8): 211-214, 235. (in Chinese)

[9] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.

[10] BUADES A, COLL B, MORELJ M. A non-local algorithm for image denoising [C]//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE, 2005.

[11] 贾迪, 孟祥福, 孟琭, 等．RGB空间下结合高斯曼哈顿距离图的彩色图像边缘检测[J]．电子学报, 2014, 42(2): 257-263．

    JIA D, MENG X F, MENG L, et al. Color image edge detection combining with gauss Manhattan distance map in RGB space [J]. Acta Electronica Sinica, 2014, 42(2): 257-263. (in Chinese)

[12] GIRSHICK R. Fast R-CNN [C]//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015: 1440-1448.

[13] 王健博, 杨航, 吴笑天．结合图像分割的改进导引滤波[J]．液晶与显示, 2017, 32(5): 380-386．

    WANG J B, YANG H, WU X T. Segmentation based improved guided filter [J]. Chinese Journal of Liquid Crystals and Displays, 2017, 32(5): 380-386. (in Chinese)

[14] 吴晨睿, 张树有, 何再兴．基于梯度分类的复杂背景椭圆快速检测方法[J]．浙江大学学报(工学版), 2018, 52(5): 943-950．

    WU C R, ZHANG S Y, HE Z X. Fast detection method for ellipse in complex background based on gradient grouping [J]. Journal of Zhejiang University (Engineering Science), 2018, 52(5): 943-950. (in Chinese)

[15] 熊风光, 李希, 韩燮．基于整体最小二乘的椭圆拟合方法[J]．微电子学与计算机, 2017, 34(1): 102-105．

    XIONG F G, LI X, HANG X. A method of ellipse fitting based on total least squares [J]. Microelectronics & Computer, 2017, 34(1): 102-105. (in Chinese)

[16] 程玉柱, 顾权, 王众辉, 等．基于深度学习的木材缺陷图像检测方法[J]．林业机械与木工设备, 2018, 46(8): 33-36．

    CHENG Y Z, GU Q, WANG Z H, et al. Wood defect image segmentation based on deep learning [J]. Forestry Machinery & Woodworking Equipment, 2018, 46(8): 33-36. (in Chinese)