基于计算机视觉的X射线图像异物分类研究

王宇; 邹文辉; 杨晓敏; 姜维; 吴炜

doi:10.3788/YJYXS20173204.0287

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基于计算机视觉的X射线图像异物分类研究

显示与图像 | 更新时间：2020-07-07

- 基于计算机视觉的X射线图像异物分类研究
- X-ray image illegal object classification based on computer vision
- 液晶与显示 2017年32卷第4期页码：287-293
- 作者机构：
  
  1.中国民航飞行学院航空工程学院, 四川广汉 618307
  2.西南师范大学电子信息工程学院, 四川南充 637009
  3.四川大学电子信息学院, 四川成都 610064
- 作者简介：
  
  王宇 (1975-), 男, 浙江兰溪人, 副教授, 2001年于四川大学获得硕士学位, 2008年于四川大学获得博士学位, 主要从事数字图像处理、DSP应用方面的研究。E-mail:scuwangyu@163.com
- 基金信息：
  
  国家自然科学基金资助项目(No.61271330);Supported by National Natural Science Foundation of China(No.61271330)
- DOI：10.3788/YJYXS20173204.0287
  中图分类号： TP391.4
- 收稿日期：2016-09-12，
  
  录用日期：2016-12-7，
  
  纸质出版日期：2017-04
- 稿件说明：
移动端阅览
王宇, 邹文辉, 杨晓敏, 等. 基于计算机视觉的X射线图像异物分类研究[J]. 液晶与显示, 2017,32(4):287-293.

Yu WANG, Wen-hui ZHOU, Xiao-min YANG, et al. X-ray image illegal object classification based on computer vision[J]. Chinese journal of liquid crystals and displays, 2017, 32(4): 287-293.
王宇, 邹文辉, 杨晓敏, 等. 基于计算机视觉的X射线图像异物分类研究[J]. 液晶与显示, 2017,32(4):287-293. DOI： 10.3788/YJYXS20173204.0287.

Yu WANG, Wen-hui ZHOU, Xiao-min YANG, et al. X-ray image illegal object classification based on computer vision[J]. Chinese journal of liquid crystals and displays, 2017, 32(4): 287-293. DOI： 10.3788/YJYXS20173204.0287.

摘要

在安检领域，目前最主要的手段是人工分析X光图像，以检测是否隐藏的违禁品。由于人工检测存在较强的主观性，并且在安检员疲劳时容易造成漏判、错判。针对这一问题，对X光异物图像进行自动识别研究，提出了基于Tamura纹理特征和随机森林的X射线异物分类方法。介绍了基于计算机视觉的X光违禁品自动检测识别系统；提出一种基于Contourlet变换的Taruma纹理特征提取方法，通过该方法得到Taruma纹理特征向量；最后采用随机森林分类器对违禁品图像进行分类判断。实验结果表明，基于Tamura纹理特征和随机森林的X射线异物分类能够有效地区分不同种类的违禁品。

Abstract

In the field of security

manual X-ray images analysis is a common method to detect illegal object. Due to manual analysis' strong subjectivity

it is easy to cause false detection. The illegal object classification by Tamura texture feature and random forest with X ray is proposed in this paper. Firstly

an automatic illegal object detection and identification system based on computer vision is introduced. Then a method based on Contourlet transform is proposed to extract Taruma texture. Finally

the random forest classifier is adopted to classify illegal objects. Experimental results show that the illegal object classification by Tamura texture feature and random forest with X ray can effectively distinguish different kinds of illegal object.

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references

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