| 浮选泡沫图像主成分的分类与识别 |
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| 引用本文： | 刘小波.浮选泡沫图像主成分的分类与识别[J].云南冶金,2014(3):76-79. |
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| 作者姓名： | 刘小波 |
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| 作者单位： | 昆明冶金高等专科学校电气学院,云南昆明650033 |
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| 基金项目： | 云南省省院省校合作项目.项目编号2000YXJO. |
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| 摘 要： | 通过对大量浮选泡沫图像的分析，获得能够表示泡沫层的特征参数。采用灰度共生矩阵法提取参数特征并对特征参数进行正交变换处理，然后用BP神经网络进行分类，获得不同类别的浮选效果。研究结果表明，对特征参数进行正交变换修正后，大大提高了分类识别的正确率。
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| 关 键 词： | 浮洗泡沫图像 纹理 神经网络 |
Classification and Identification of the Main Components of Flotation Froth Image |
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| LIU Xiao-bo.Classification and Identification of the Main Components of Flotation Froth Image[J].Yunnan Metallurgy,2014(3):76-79. |
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| Authors: | LIU Xiao-bo |
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| Affiliation: | LIU Xiao-bo (College of Electrical Engineering, Kunming Metallurgy College, Kunming, Yunnan 650033, China) |
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| Abstract: | The characteristic parameter of flotation froth can be got through the quantitative analysis of its image. The characteristic parameter is extracted by Grey-level co - occurrence matrix, and the characteristic parameter is treated by orthogonal transformation, and then it shall be classified by the BP neural network to get the different flotation effects. The research results show that the accuracy of the classification and identification is increased greatly by the orthogonal transformation of the characteristic parameter. |
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| Keywords: | flotation froth image texture neural network |
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