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Classification of Infrared Monitor Images of Coal Using an Feature Texture Statistics and Improved BP Network 被引量:2
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作者 SUN Ji-ping CHEN Wei +3 位作者 MA Feng-ying WANG Fu-zeng TANG Liang LIU Yan-jie 《Journal of China University of Mining and Technology》 EI 2007年第4期489-493,共5页
It is very important to accurately recognize and locate pulverized and block coal seen in a coal mine's infrared image monitoring system. Infrared monitor images of pulverized and block coal were sampled in the ro... It is very important to accurately recognize and locate pulverized and block coal seen in a coal mine's infrared image monitoring system. Infrared monitor images of pulverized and block coal were sampled in the roadway of a coal mine. Texture statistics from the grey level dependence matrix were selected as the criterion for classification. The distributions of the texture statistics were calculated and analysed. A normalizing function was added to the front end of the BP network with one hidden layer. An additional classification layer is joined behind the linear layer. The recognition of pulverized from block coal images was tested using the improved BP network. The results of the experiment show that texture variables from the grey level dependence matrix can act as recognizable features of the image. The innovative improved BP network can then recognize the pulverized and block coal images. 展开更多
关键词 pulverized-coal-image block-coal-image gray level dependence matrix improved BP networks
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