| 基于纹理和改进径向基网络的煤监控图像识别 |
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| 引用本文： | 孙继平,陈伟,李毅,王福增,唐亮,张帆.基于纹理和改进径向基网络的煤监控图像识别[J].煤炭科学技术,2008,36(1):78-81. |
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| 作者姓名： | 孙继平 陈伟 李毅 王福增 唐亮 张帆 |
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| 作者单位： | 中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京,100083 |
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| 基金项目： | 高等学校博士学科点专项科研项目 |
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| 摘 要： | 煤的红外监控图像的正确识别对矿井自动监控有重要的意义.计算煤监控图像的灰度相关矩阵各纹理统计量,分析其分布特征.在径向基函数神经网络(RBFNN)的输入层增加了正规化函数,用改进的RBFNN对图像进行了识别.结果表明,图像的纹理统计量在统计上有很好的分离性,改进的RBFNN能成功地识别出煤矿井下红外监控系统中面煤和块煤的图像.
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| 关 键 词： | 煤炭 红外监控图像 灰度相关矩阵 改进RBFNN |
| 文章编号： | 0253-2336（2008）01-0078-04 |
| 收稿时间： | 2007-09-18 |
| 修稿时间： | 2007年9月18日 |
Identification of monitoring and control coal image base on texture features and improved radial basis function neural network |
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| SUN Ji-ping,CHEN Wei,LI Yi,WANG Fu-zeng,TANG Liang,ZHANG Fan.Identification of monitoring and control coal image base on texture features and improved radial basis function neural network[J].Coal Science and Technology,2008,36(1):78-81. |
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| Authors: | SUN Ji-ping CHEN Wei LI Yi WANG Fu-zeng TANG Liang ZHANG Fan |
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| Affiliation: | SUN Ji-ping,CHEN Wei,LI Yi,WANG Fu-zeng,TANG Liang,ZHANG Fan(National Key Lab of Coal Resources , Safety Mining,China University of Mining , Technology,Beijing 100083,China) |
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| Abstract: | A correct identification of an infrared monitoring coal image is an important to the mine auto monitoring and control. Calculated the matrix and texture statistic value related to the grey scale of the monitoring and control coal image and analyzed the distribution features. A normalization function was added to the input of the RBFNN. The images were identified with the improved RBFNN. The results showed that the texture statistics value of the images had a good separability in the statistics. The improved RBFNN could successfully identify the fine coal and lump coal images in the coal mine infrared monitoring and control system. |
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| Keywords: | coal infrared monitoring and control image grey scale related matrix improved RBFNN |
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