[1] [1] WANG Ming-yan, JIA Mu-xin, XIAO Yi-wu, et al. The present situation and sustainable development counter measures of tungsten resources in China[J]. Nonferrous Metals Engineering, 2014, 4(2): 76-80.

[2] [2] LONG Kui. Design ofprimary sortingsystemoftungsten orebasedon theDSP[D]. Gan Zhou: Jiangxi University of Science and Technology, 2013: 1-3.

[3] [3] KAARTINEN J, HATONEN J, HYOTYIEMI H, et al. Machine vision based control of zinc flotation a case study[J]. Control Engineering Practice, 2006, 14(12): 1455-1466.

[4] [4] LIN Xia. Research and application of flotation froth image texture extraction[D]. Chang Sha: Central South University, 2013: 23-27.

[5] [5] LIN Ze-tan, MIAO Chang-yun, LI Xian-guo, et al. Conveyor belt breakage detection algorithm based on machine vision[J]. Journal of Tianjin Polytechinc University, 2014, 33(03): 66-68, 79.

[6] [6] XIAO Ling-ling, HUANG Zhi-hui. Research on primary algorithm of wolframite ore based on the physical properties[J]. Mining Research and Development, 2015, 35(9): 36-39.

[7] [7] HU Fa-huan, DONG Zeng-wen, KUANG Yi-shun. Online navel orange grading detection system based on machine vision[J]. Journal of China Agricultural University, 2016, 21(3): 112-118.

[8] [8] MA Shuang, LIU Kun, XU Gang, Illumination analysis of texture image based on color statistics invariance[J]. Computer Applications and Software, 2014, 31(2): 233-237.

[9] [9] PENATTI O A B, VALLE E, TORRES R S. Comparatives study of global and texture descriptors for web image retrieval[J]. Journal Visual Communication and Representation, 2012, 23(2): 359-380.

[10] [10] LI J N, ALLINSON M. A comprehensive review of current local features for computer vision[J]. Neurocomputing, 2008, 71(10-12): 1771-1787.

[11] [11] FATHI A, CI A N N. Noise tolerant local binary pattern operator for efficient texture analysis[J]. Pattern Recogniton Letters, 2012, 33(9): 1093-1100.

[12] [12] LIU li, KUANG Gang-yao. Over view of image textural feature extraction methods[J]. Journal of Image and Graphic, 2009, 14(4): 622-635.

[13] [13] GUO Ming-wei, ZHAO Yu-zhou, XIANG Jun-ping, et al. Review of object detection methods based on SVM[J]. Control and Decision, 2014, 29(2): 193-200.

[14] [14] TONG Xiao-yang, LUO Zhong-yun. Transmission line faults classification method based on fuzzy support vector machine and reducing-dimension display[J]. High Voltage Engineering, 2015, 41(7): 2276-2282.

[15] [15] INOUET T, ABE S. Fuzzy support vector machines for pattern classification[C]. International Joint Conference on Neural Networks. Washington DC: IEEE, 2001, 2: 1449-1454.

[17] [17] BASIR O,YUAN X H. Engine fault diagnosis base on multi-sensor information fusion using Dempster-Shafer evidence theory[J]. Information Fusion, 2007, 8(4): 379-386.

[19] [19] ZHAO Qiu-yue, ZUO Wan-li, TIAN Zhong-sheng, et al. A method for assessment of trust relationship strength based on the improved D-S evidence theory[J]. Chinese Journal of Copputers, 2014, 37(4): 873-883.

[20] [20] OUYANG Ai-guo, TANG Tian-yi, ZHOU Xin, et al. Methanol content determination in methanol diesel with mid infrared spectroscopy analysis using least square support vector machine[J]. Chinese Journal of Luminescence, 2016, 37(10): 1253-1258.

[21] [21] DOU Gang, CHEN Guang-sheng, ZHAO Peng. Wood species recognition using color texture and spectral features[J]. Journal of Tianjin University (Science and Technology): 2015, 48(2): 147-254.