Ungsten Ore Primary Selection Based on Fuzzy Support Vector Machine and D-S Evidence Theory

被引：0
|
作者：
Hu F.-H. [1 ,2 ]
Liu G.-P. [1 ]
Hu R.-H. [1 ]
Dong Z.-W. [1 ]
机构：
[1] School of Mechanical & Electrical Engineering, Nanchang University, Nanchang
[2] School of Mechanical & Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, Jiangxi
来源：
Liu, Guo-Ping (liuguoping@vip.163com) | 1600年 / Chinese Optical Society卷 / 46期
基金：
中国国家自然科学基金;
关键词：
D-S evidence theory; Decision fusion; Feature extraction; Fuzzy support vector machine; Image processing; Machine vision; Tungsten ore; Tungsten ore primary selection;
D O I：
10.3788/gzxb20174607.0710003
中图分类号：
学科分类号：
摘要：
According to the low accuracy and low stability of the single feature-based method for tungsten ore primary selection, a multi-feature fusion based on fuzzy support vector machine and D-S evidence theory was proposed. Firstly, the three types of vision feature that is color, gray and texture were extracted from the ore image after a series of image processing. Their probability function were acquired according to each type of feature utilizing fuzzy support vector machine and the results were used to D-S evidence theory as evidence. Finally, using D-S combination rule of evidence to achieve the decision fusion and giving final recognition result by classification rules. The experimental results show that the accuracy of multi-feature fusion methods is over 96% and it has good performance on accuracy and stability compared to the single feature-based method in tungsten ore primary selection. The accuracy and stability can meet the requirement of production process. © 2017, Science Press. All right reserved.
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