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高斯差分滤波显著性的刀具磨损检测

管声启 洪奔奔 梁洪 王立中

管声启, 洪奔奔, 梁洪, 王立中. 高斯差分滤波显著性的刀具磨损检测[J]. 机械科学与技术, 2018, 37(2): 276-279. doi: 10.13433/j.cnki.1003-8728.2018.0218
引用本文: 管声启, 洪奔奔, 梁洪, 王立中. 高斯差分滤波显著性的刀具磨损检测[J]. 机械科学与技术, 2018, 37(2): 276-279. doi: 10.13433/j.cnki.1003-8728.2018.0218
Guan Shengqi, Hong Benben, Liang Hong, Wang Lizhong. Tool Wear Detection using Gauss Filter Saliency[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(2): 276-279. doi: 10.13433/j.cnki.1003-8728.2018.0218
Citation: Guan Shengqi, Hong Benben, Liang Hong, Wang Lizhong. Tool Wear Detection using Gauss Filter Saliency[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(2): 276-279. doi: 10.13433/j.cnki.1003-8728.2018.0218

高斯差分滤波显著性的刀具磨损检测

doi: 10.13433/j.cnki.1003-8728.2018.0218
基金项目: 

中国纺织工业联合会科技指导性项目(2016065)与陕西省教育厅科研计划项目(16JK1337)资助

详细信息
    作者简介:

    管声启(1971-),副教授,博士,研究方向为图像处理与智能信息处理,sina1300841@163.com

Tool Wear Detection using Gauss Filter Saliency

  • 摘要: 为了提高刀具磨损区域检测准确性,本文在研究刀具磨损区域特点的基础上,提出了一种新的刀具磨损检测方法。首先,对采集的刀具图像进行高斯滤波获得高斯滤波图,消除噪声信息;然后,通过高斯差分滤波获得高斯差分图,提取刀具背景纹理信息;在此基础上,利用高斯滤波图与高斯差分图之间的中央-周边操作获得显著图,以消除高频噪声信息以及光照不均等低频背景信息,提高刀具磨损区域的显著性;最后,根据刀具磨损区域特征进行刀具磨损区域分割和滤波。实验表明,推荐方法能够准确检测刀具磨损区域,具有较高的检测准确率。
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出版历程
  • 收稿日期:  2016-12-06
  • 刊出日期:  2018-02-25

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