基于机器视觉的煤岩界面识别研究

田子建; 彭霞; 苏波

基于机器视觉的煤岩界面识别研究

田子建¹,
彭霞²,
苏波^{1, 3}

1.
中国矿业大学（北京）机电与信息工程学院，北京100083
2.
中煤科工集团常州自动化研究院，江苏常州213015
3.
河南理工大学电气工程与自动化学院，河南焦作454003

基金项目:

国家自然科学基金资助重点项目（51134024）

国家自然基金资助项目(U1261125)

河南省高等学校控制工程重点学科开放课题(KG2011-07)

详细信息

中图分类号: TD67
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- 被引次数: 0
出版历程
- 刊出日期: 2013-05-10

Research of coal-rock interface identification based on machine visio

TIAN Zi-jian¹,
PENG Xia²,
SU Bo^{1, 3}

1.
School of Mechanical Electronic and Information Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China；2.Changzhou Automation Research Institute of CCTEG, Changzhou

摘要

摘要: 针对现有煤岩识别方法存在煤岩界面传感器结构复杂、可靠性差、普适性差等问题，提出了一种基于机器视觉的煤岩界面识别系统设计方案，给出了系统总体结构，分析了系统识别煤岩界面的工作原理，重点讨论了图像特征选取和分类器的设计。该系统根据灰度共生矩阵理论提取煤岩图像的22种纹理特征，采用增l减r法搜索出优选特征，最后运用线性函数判别法构建煤岩分类器模型。实验结果表明，该系统的煤岩分类器模型性能稳定，具有较强的识别能力。
- 煤岩界面识别 /
- 机器视觉 /
- 特征选取 /
- 灰度共生矩阵 /
- 煤岩分类器
Abstract: In view of problems that coal-rock interface sensor has complicated structure, low reliability and universality in existing coal-rock identification method, the paper proposed a solution of coal-rock interface identification system based on machine vision. It gave general structure of the system, analyzed its working principle and focused on discussion of image feature selection and classifier design. The system extracts 22 textures of coal-rock images according to the theory of gray-level co-occurrence matrix, employs method of increasing l and decreasing r to search the optimized features, and builds coal-rock classifier model by linear function recognition. The experiment results show that the coal-rock classifier model of the system has stable performances and strong identification ability.
- coal-rock interface identification /
- machine vision /
- feature selection /
- gray-level co-occurrence matrix /
- coal-rock classifier