单一特征识别的钨矿石初选准确率低, 稳定性差, 本文提出结合模糊支持向量机和D-S证据理论相的多特征钨矿石识别方法.对矿石图像预处理后, 分别提取矿石的颜色、灰度和纹理等3类视觉特征, 对这3类视觉特征进行模糊分类得到各自的信任度, 再以这3类信任度为独立证据, 采用D-S证据理论对3类证据进行融合, 并依据分类判决规则得到最终的识别结果.试验结果表明, 通过D-S理论对模糊向量机证据的融合, 钨矿石初选的正确识别率达到96%以上, 其准确率和稳定性较单一特征均有大幅度提高, 满足生产过程中初选工艺的要求.

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.