[1]余乐,郑力新,杜永兆,等.采用部分灰度压缩扩阶共生矩阵的煤和煤矸石图像识别[J].华侨大学学报(自然科学版),2018,39(6):906-912.[doi:10.11830/ISSN.1000-5013.201610012]
　YU Le,ZHENG Lixin,DU Yongzhao,et al.Image Recognition Method of Coal and Coal Gangue Based on Partial Grayscale Compression Extended Coexistence Matrix[J].Journal of Huaqiao University(Natural Science),2018,39(6):906-912.[doi:10.11830/ISSN.1000-5013.201610012]
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采用部分灰度压缩扩阶共生矩阵的煤和煤矸石图像识别()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第39卷
期数:
2018年第6期
页码:
906-912
栏目:
出版日期:
2018-11-20

文章信息/Info

Title:
Image Recognition Method of Coal and Coal Gangue Based on Partial Grayscale Compression Extended Coexistence Matrix
文章编号:
1000-5013(2018)06-0906-07
作者:
余乐1; 2;  郑力新1; 2;  杜永兆1; 2;  黄璇1; 2
1. 华侨大学 工学院, 福建 泉州 362021;2. 华侨大学 工业智能化技术与系统福建省高校工程研究中心, 福建 泉州 362021
Author(s):
YU Le1; 2;  ZHENG Lixin1; 2;  DU Yongzhao1; 2;  HUANG Xuan1; 2
1. College of Engineering, Huaqiao University, Quanzhou 362021, China; 2. Engineering Research Center of Fujian Province Industrial Intelligent Technology and System, Huaqiao University, Quanzhou 362021, China
关键词:
煤;  煤矸石;  图像识别;  特征提取;  灰度压缩;  扩阶共生矩阵
Keywords:
coal;  coal gangue;  image recognition;  feature extraction;  grayscale compression;  extended coexistence matrix
分类号:
TP391
DOI:
10.11830/ISSN.1000-5013.201610012
文献标志码:
A
摘要:
提出一种基于部分灰度压缩扩阶共生矩阵的煤和煤矸石图像识别方法.首先,对煤和煤矸石0~255级灰度图像的前部分灰度信息作灰度级压缩和灰度矩阵扩阶处理,对剩余灰度级部分保持原灰度级不变;然后,根据灰度共生矩阵纹理特征分析理论,分别计算压缩扩阶后的煤和煤矸石灰度图像的能量、熵、惯性矩及相关性.最后,对煤和煤矸石各100张样本采集图像进行处理,并依据特征参数分类识别.结果表明:基于部分灰度压缩扩阶共生矩阵的特征参数能够很好地对煤和煤矸石图像进行有效识别,总的正确率达到93.5%.
Abstract:
A coal and coal gangue image recognition method based on partial grayscale compression extended coexistence matrix is presented. Firstly, the 0-255 grayscale images of coal and coal gangue are compressed partly with the front part grayscale, while the other parts of grayscale are remained the same with the original grayscale. Then, according to texture analysis theory of gray-level co-occurrence matrix(GLCM), the energy, entropy, moment of inertia and the correlation coefficient of the coal and coal gangue after compression and extension-order are calculated,respectively. The experiments are carried out with the test samples of 100 coal images and 100 coal gangue images, and the performances of the proposed recognition method are demonstrated with the calculated characteristic parameters. The experimental results indicated that the coal and coal gangue images can be recognized effectively, and an overall accuracy up to 93.5% is achievable with the proposed expanded-order GLCM method.

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相似文献/References:

[1]陈本沛.煤矸石用于砼骨料的可行性[J].华侨大学学报(自然科学版),1994,15(2):181.[doi:10.11830/ISSN.1000-5013.1994.02.0181]
　Chen Benpei.Feasibility of Applying Coal Gangue to the Aggregate[J].Journal of Huaqiao University(Natural Science),1994,15(6):181.[doi:10.11830/ISSN.1000-5013.1994.02.0181]

备注/Memo

备注/Memo:
收稿日期: 2016-10-09
通信作者: 郑力新(1967-),男,教授,博士,主要从事运动控制、机器视觉、图像处理与模式识别的研究.E-mail:1275373176@qq.com.
基金项目: 福建省科技厅科研计划资助项目(2013H2002); 华侨大学研究生科研创新能力培育计划资助项目(1511422005)
更新日期/Last Update: 2018-11-20