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基于最大池化稀疏编码的煤岩识别方法

伍云霞 田一民

伍云霞, 田一民. 基于最大池化稀疏编码的煤岩识别方法[J]. 工程科学学报, 2017, 39(7): 981-987. doi: 10.13374/j.issn2095-9389.2017.07.002
引用本文: 伍云霞, 田一民. 基于最大池化稀疏编码的煤岩识别方法[J]. 工程科学学报, 2017, 39(7): 981-987. doi: 10.13374/j.issn2095-9389.2017.07.002
WU Yun-xia, TIAN Yi-min. A coal-rock recognition method based on max-pooling sparse coding[J]. Chinese Journal of Engineering, 2017, 39(7): 981-987. doi: 10.13374/j.issn2095-9389.2017.07.002
Citation: WU Yun-xia, TIAN Yi-min. A coal-rock recognition method based on max-pooling sparse coding[J]. Chinese Journal of Engineering, 2017, 39(7): 981-987. doi: 10.13374/j.issn2095-9389.2017.07.002

基于最大池化稀疏编码的煤岩识别方法

doi: 10.13374/j.issn2095-9389.2017.07.002
基金项目: 

国家自然科学基金重点资助项目(51134024)

国家重点研发计划资助项目(2016YFC0801800)

详细信息
  • 中图分类号: TD672;TP391.41

A coal-rock recognition method based on max-pooling sparse coding

  • 摘要: 针对现今煤岩图像识别方法的缺乏与不足,为了挖掘新的煤岩图像识别方法以及更好地处理高维煤岩图像数据,提出了基于最大池化稀疏编码的煤岩识别方法.本方法在提取煤岩图像特征时加入了池化操作,在分类识别时采用了集成分类器,即多个弱分类器组成一个强分类器.实验结果表明:最大池化稀疏编码的特征提取方式能简单有效表达煤岩图像的纹理特征,大大增强煤岩图像的可区分性,获得较高的识别率,并且具有良好的识别稳定性.研究结果可为煤岩界面的自动识别提供新的思路和方法.
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  • 收稿日期:  2017-01-01

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