| 基于灰度共生三角阵的炮膛图像分析 |
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| 引用本文： | 李玉兰,郑海起,栾军英,唐力伟,濮俊艳.基于灰度共生三角阵的炮膛图像分析[J].装甲兵工程学院学报,2009,23(2):48-52. |
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| 作者姓名： | 李玉兰 郑海起 栾军英 唐力伟 濮俊艳 |
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| 作者单位： | 1. 军械工程学院,火炮工程系,河北,石家庄,050003
2. 73906部队,江苏,南京,210028 |
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| 摘 要： | 为解决炮膛疵病图像粗分类时缺少聚类和分类特征的问题，引入了灰度共生三角阵（Grey Level Co-occurrence Triangular Matrix，GLCTM）的方法。通过对炮膛样本图像GLCTM特征参量的计算与分析，确定了GLCTM的参数；根据特征参量的相关性分析结果，选择了其中7个作为炮膛图像的特征参量。样本图像的实验结果表明，炮膛图像的GLCTM特征参量取值与图像灰度的直观特点之间存在对应关系。
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| 关 键 词： | 灰度共生三角阵 炮膛 疵病 机器视觉 粗分类 |
Analysis of Gun Bore Image Based on Grey Level Co-occurrence Triangular Matrix |
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| LI Yu-lan,ZHENG Hai-qi,LUAN Jun-ying,TANG Li-wei,PU Jun-yan.Analysis of Gun Bore Image Based on Grey Level Co-occurrence Triangular Matrix[J].Journal of Armored Force Engineering Institute,2009,23(2):48-52. |
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| Authors: | LI Yu-lan ZHENG Hai-qi LUAN Jun-ying TANG Li-wei PU Jun-yan |
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| Institution: | 1. Department of Artillery Engineering, Ordnance Engineering College, Shijiazhuang 050003, China ; 2. Troop No. 73906 of PLA, Nanjing 210028, China) |
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| Abstract: | The method of Grey Level Co-occurrence Triangular Matrix （GLCTM） is introduced in flaw image coarse classification of gun bore, in order to solve the problem of lacking clustering and classifying features. By computing and analyzing characteristic parameters of GLCTM for sample images, parameters of gun bore images ＇GLCTM are decided. According to correlation analysis results of characteristic parameters of GLCTM, seven of them are chosen as characteristic parameters of gun bore image. The experimental results from sample images analysis show that values of characteristic parameters of GLCTM are corresponding to visual characters of image grey. |
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| Keywords: | GLCTM gun bore flaw machine vision coarse classification |
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