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基于PSO优选特征的实木板材缺陷的压缩感知分选方法

李超 刘思佳 曹军 于慧伶 张怡卓

李超, 刘思佳, 曹军, 于慧伶, 张怡卓. 基于PSO优选特征的实木板材缺陷的压缩感知分选方法[J]. 北京林业大学学报, 2015, 37(7): 117-122. doi: 10.13332/j.1000-1522.20140385
引用本文: 李超, 刘思佳, 曹军, 于慧伶, 张怡卓. 基于PSO优选特征的实木板材缺陷的压缩感知分选方法[J]. 北京林业大学学报, 2015, 37(7): 117-122. doi: 10.13332/j.1000-1522.20140385
LI Chao, LIU Si-jia, CAO Jun, YU Hui-ling, ZHANG Yi-zhuo. The method of wood defect recognition based on PSO feature selection and compressed sensing[J]. Journal of Beijing Forestry University, 2015, 37(7): 117-122. doi: 10.13332/j.1000-1522.20140385
Citation: LI Chao, LIU Si-jia, CAO Jun, YU Hui-ling, ZHANG Yi-zhuo. The method of wood defect recognition based on PSO feature selection and compressed sensing[J]. Journal of Beijing Forestry University, 2015, 37(7): 117-122. doi: 10.13332/j.1000-1522.20140385

基于PSO优选特征的实木板材缺陷的压缩感知分选方法

doi: 10.13332/j.1000-1522.20140385
基金项目: 

林业公益性行业科研专项(201304510)、黑龙江省自然基金项目(C201405)、中央高校基本科研业务费专项(DL13CB02、DL13BB21)

详细信息
    作者简介:

    李超,博士,讲师。主要研究方向:信号处理与图像处理。Email:lchao820225@163.com 地址:150040黑龙江省哈尔滨市和兴路26号东北林业大学机电工程学院。

    责任作者:

    张怡卓,博士,副教授。主要研究方向:图像处理与模式识别。Email:nefuzyz@163.com 地址:同上。

The method of wood defect recognition based on PSO feature selection and compressed sensing

  • 摘要: 针对实木板材表面缺陷的复杂性与随机性,提出了一种快速、准确的识别方法。首先,对实木板材表面图像进行3级双树复小波分解,提取低频子带、高频子带、原图像的均值、标准差和熵,共40维特征向量;然后,运用粒子群算法(PSO)优选出20个关键特征;最后,采用压缩感知理论将优选后的特征向量作为样本矩阵列,构建训练样本数据字典,通过最小残差完成缺陷识别。对4类柞木样本进行了仿真实验,活结、死结、虫眼、裂纹的分类正确率分别为93.3%、86.7%、100%和93.3%,结果表明:双树复小波良好的方向性能够表达实木板材表面复杂的信息;基于粒子群算法的特征选择能够提高分类效率;压缩感知分类器与传统分类器相比,具有结构简单、分类精度高的特点。

     

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出版历程
  • 收稿日期:  2014-10-23
  • 修回日期:  2014-10-23
  • 刊出日期:  2015-07-31

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