白雪冰,王科俊,邹丽晖. 2008. 基于灰度共生矩阵的木材表面缺陷图像的纹理分割方法. 东北林业大学学报,36(12):23-25. (Bai X B,Wang K J,Zou L H. 2008. Approach to texture segmentation of wood surface defects based on gray level co-occurrence matrix. Journal of Northeast Forestry University,36(12):23-25.[in Chinese]) 蒋盛益,李 霞. 2009. 一种改进的BIRCH聚类算法. 计算机应用,29(1):293-296. (Jiang S Y,Li X. 2009. Improved BIRCH clustering algorithm. Journal of Computer Applications,29(1):293-296.[in Chinese]) 任 宁,于海鹏,刘一星,等. 2007. 木材纹理的分形特征及计算. 东北林业大学学报,35(2):9-12. (Ren N,Yu H P,Liu Y X,et al. 2007. Fractal character and calculation of wood texture. Journal of Northeast Forestry University,35(2):9-12.[in Chinese]) 吴东洋,业 宁,苏小青. 2010. 基于灰度共生矩阵和聚类方法的木材缺陷识别.计算机与数字工程,38(11):38-41. (Wu D Y,Ye N,Su X Q. 2010. Wood defect recognition based on GLCM and clustering algorithm. Computer & Digital Engineering,38(11):38-41.[in Chinese]) 谢娟英,高红超. 2014. 基于统计相关性与K-means的区分基因子集选择算法.软件学报,25(9):2050-2075. (Xie J Y,Gao H C. 2014. Statistical correlation and K-means based distinguishable gene subset selection algorithms. Journal of Software,25(9):2050-2075.[in Chinese]) 杨 旭. 2016. 木材加工自动化中的板材缺陷检测技术研究.南京:南京林业大学硕士学位论文. (Yang X. 2016. Research on defect detection technology in wood processing automation. Nanjing:MS thesis of Nanjing Forestry University.[in Chinese]) 业 宁,丁建文,王 迪,等. 2007. 基于 LBP 特征提取的木材纹理缺陷检测. 计算机研究与发展,44(suppl):383-387. (Ye N, Ding J W, Wang D, et al. 2007. Wood texture defect detection with LBP features. Journal of Computer Research and Development, 44(suppl):383-387.[in Chinese]) 尹建新,祁亨年,冯海林,等. 2011.一种基于混合纹理特征的木板材表面缺陷检测方法.浙江农林大学学报, 28(6):937-942. (Yin J X,Qi H N,Feng H L,et al. 2011. A method for wood surface defect detection based on mixed texture features. Journal of Zhejiang A & F University,28(6):937-942.[in Chinese]) 于海鹏,刘一星,孙建平. 2005. 基于小波的木材纹理分频信息提取与分析. 林业科学,41(2):100-105. (Yu H P,Liu Y X,Sun J P. 2005.Separated frequency features extraction and analysis of wood texture based on wavelet. Scientia Silvae Sinicae,41(2):100-105.[in Chinese]) 余丽萍,黎 明,杨小芹,等.2010.基于灰度共生矩阵的断口图像识别.计算机仿真,27(4):224-227. (Yu L P,Li M,Yang X Q, et al.2010. Recognition of fracture image based on gray level co-occurrence matrix.Computer Simulation,27(4):224-227.[in Chinese]) 赵玉艳,郭景峰,郑丽珍. 2008.一种改进的BIRCH分层聚类算法.计算机科学,35(3):180-182. (Zhao Y Y,Guo J F,Zheng L Z. 2008. Improved BIRCH hierarchical clustering algorithm.Computer Science,35(3):180-182.[in Chinese]) 朱 蕾. 2011. 木材表面缺陷图像识别的算法研究. 南京:南京林业大学硕士学位论文. (Zhu L. 2011. Research on recognition algorithm of the wood surface defect image. Nanjing:MS thesis of Nanjing Forestry University.[in Chinese]) 邹丽晖. 2007.基于纹理特征的木材表面缺陷识别方法的研究. 哈尔滨:东北林业大学硕士学位论文. (Zou L H. 2007. Research on identification methods of wood surface defects based on texture features. Harbin:MS thesis of Northeast Forestry University.[in Chinese]) Boss R S C,Thangavel K,Daniel D A P,et al. 2012. Mammogram image segmentation using fuzzy clustering. International Conference on Pattern Recognition,Informatics and Medical Engineering (PRIME-2012), 290-295. Haralick R M,Shanmugam K.1973. Textural features for image classification.IEEE Transactions on Systems Man & Cybernetics,SMC-3(6):610-621. Haralick R M. 1979. Statistical and structural approaches to texture. Proceedings of the IEEE,67(5):786-804. Lu J C,Liu F L,Luo X Y. 2014. Selection of image features for steganalysis based on the Fisher criterion. Digital Investigation,11(1):57-66. Ulaby F T,Kouyate F,Brisco B,et al. 1986. Textural information in SAR Images. IEEE Transactions on Geoscience and Remote Sensing,24(2):235-245. Wang K Q,Bai X B. 2006. Classification of wood surface texture based on Gauss-MRF model. Journal of Forestry Research,17(1):57-61. Yedla M, Pathakota S R,Srinivasa T M. 2010. Enhancing K-Means clustering algorithm with improved initial center. International Journal of Computer Science and Information Technologies,1(2):121-125. Zhang T, Ramakrishnan R, Livny M. 1996. BIRCH an efficient data clustering method for very large databases.ACM SIGMOD Record,25(2):103-114. |