首页 | 本学科首页   官方微博 | 高级检索  
     

概率神经网络在矿井红外监控图像识别中的应用
引用本文:孙继平,陈伟,王福增,唐亮,李郴.概率神经网络在矿井红外监控图像识别中的应用[J].煤炭学报,2007,32(11):1206-1210.
作者姓名:孙继平  陈伟  王福增  唐亮  李郴
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
基金项目:教育部博士点基金资助项目(20050290010),北京市教育委员会共建经费研究生教育资助项目
摘    要:对粉煤图像和块煤图像灰度相关矩阵各统计量的数值进行了极差正规化处理,并分析了其统计量的分布特征.使用概率神经网络对粉煤图像和块煤图像进行了识别仿真.实验结果表明,用灰度相关矩阵各统计量作为粉煤图像和块煤图像的识别特征,成功地识别出了粉煤和块煤的图像.

关 键 词:概率神经网络  粉煤图像  块煤图像  灰度相关矩阵  
文章编号:0253-9993(2007)11-1206-05
收稿时间:2007-09-24
修稿时间:2007年9月24日

Application of probabilistic neural network in recognizing coalmine infrared monitoring images
SUN Ji-ping,CHEN Wei,WANG Fu-zeng,TANG Liang,LI Chen.Application of probabilistic neural network in recognizing coalmine infrared monitoring images[J].Journal of China Coal Society,2007,32(11):1206-1210.
Authors:SUN Ji-ping  CHEN Wei  WANG Fu-zeng  TANG Liang  LI Chen
Affiliation:The State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology (Beijing
Abstract:The normalization values of texture statistics of gray level correlative matrix were given,which were taken from the smashed-coal-images and block-coal-images.The distribution feather of statistical variables was analyzed.Recognizing the smashed-coal-images and block-coal-images was simulated with a probabilistic neural network.The experiment results show that the statistical variables of the gray level correlative matrix act as the recognizable feather,and the algorithm can recognize the smashed-coal-image and block-coal-image successfully.
Keywords:probabilistic neural network  smashed-coal-image  block-coal-image  gray level correlative matrix
本文献已被 CNKI 维普 万方数据 等数据库收录!
点击此处可从《煤炭学报》浏览原始摘要信息
点击此处可从《煤炭学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号