X-ray image illegal object classification based on computer vision

Yu WANG; Wen-hui ZHOU; Xiao-min YANG; Wei JIANG; Wei WU

doi:10.3788/YJYXS20173204.0287

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X-ray image illegal object classification based on computer vision

Display and Imag | 更新时间：2020-07-07

- X-ray image illegal object classification based on computer vision
- Chinese Journal of Liquid Crystals and Displays Vol. 32, Issue 4, Pages: 287-293(2017)
- 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：
- 基金信息：
- DOI：10.3788/YJYXS20173204.0287
  CLC： TP391.4
- Received：12 September 2016，
  
  Accepted：07 December 2016，
  
  Published：2017-04
- 稿件说明：
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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：

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.

关键词

Keywords

references

郑金州, 鲁绍栋.X射线技术在安检领域的应用[J].CT理论与应用研究, 2012, 21(2):357-364.

ZHENG J Z, LU S D. The application of X-ray technology in sectuirty inspection domain[J]. Computerized Tomography Theory and Applications, 2012, 21(2):357-364. (in Chinese)

MERY D, RIFFO V. Automated object recognition using multiple X-ray views[J]. Materials Evaluation, 2014, 72(11):1362-1372.

龙钧宇, 余爱民, 余红.一种基于模糊MSR的医学X光图像增强算法[J].计算机技术与发展, 2014, 24(1):235-241.

LONG J Y, YU A M, YU H. Medical X-ray image enhancement algorithm based on fuzzy multi-scale Retinex[J]. Computer Technology and Development, 2014, 24(1):235-241. (in Chinese)

韩萍, 刘则徐, 何炜琨.一种有效的机场安检X光手提行李图像两级增强方法[J].光电工程, 2011, 38(7):99-105.

HAN P, LIU Z X, HE W K. An efficient two-stage enhancement algorithm of X-ray carry-on luggage images[J]. Opto-Electronic Engineering, 2011, 38(7):99-105. (in Chinese)

陈树越, 刘金星, 丁艺, 等.红外与X光图像融合方法研究[J].激光与红外, 2014, 44(11):1286-1289.

CHEN S Y, LIU J X, DING Y, et al. Research on infrared and X-ray image fusion technique[J]. Laser & Infrared, 2014, 44(11):1286-1289. (in Chinese)

SKORUPSKI J, UCHRO SKI P. A fuzzy system to support the configuration of baggage screening devices at an airport[J]. Expert Systems with Applications , 2016, 44:114-125.

UNAY D, SOLDEA O, EKIN A, et al. Automatic annotation of X-ray images:a study on attribute selection[C]// Proceedings of the First MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support , Berlin Heidelberg:Springer, 2010:97-109.

ZHANG J, ZHANG L, ZHAO Z R, et al. Joint shape and texture based X-ray cargo image classification[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops , Columbus, OH:IEEE, 2014:266-273.

DO M, VETTERLI M. The contourlet transform:an efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing , 2005, 14(12):2091-2106.

王相海, 魏婷婷, 周志光, 等.Contourlet四叉树系数方向相关性的遥感图像融合算法[J].计算机研究与发展, 2013, 50(8):1778-1786.

WANG X H, WEI T T, ZHOU Z G, et al. Research of remote sensing image fusion method based on the Contourlet coefficients' correlativity[J]. Journal of Computer Research and Development , 2013, 50(8):1778-1786. (in Chinese)

TAMURA H, MORI S, YAMAWAKI T. Textural features corresponding to visual perception[J]. IEEE Transactions on Systems, Man, and Cybernetics , 1978, 8(6):460-473.

LIU Y, LI Z, GAO ZM. An improved texture feature extraction method for tyre tread patterns[C]// Proceedings of the 4th International Conference on Intelligence Science and Big Data Engineering , Berlin Heidelberg:Springer, 2013:705-713.

业巧林, 业宁, 张训华, 等.一种新的支持向量分类算法ACNN-SVM[J].郑州大学学报:理学版, 2008, 40(3):56-58.

YE Q L, YE N, ZHANG X H, et al. A new algorithm of classification based on support vector ACNN-SVM[J]. Journal of Zhengzhou University:Natural Science Edition , 2008, 40(3):56-58. (in Chinese)

李欣海.随机森林模型在分类与回归分析中的应用[J].应用昆虫学报, 2013, 50(4):1190-1197.

LI X H. Using "random forest" for classification and regression[J]. Chinese Journal of Applied Entomology, 2013, 50(4):1190-1197. (in Chinese)

GISLASON P O, BENEDIKTSSON J A, SVEINSSON JR. Random Forests for land cover classification[J]. Pattern Recognition Letters , 2006, 27(4):294-300.

郭东锋, 胡海洲, 汪季涛, 等.基于随机森林的烤烟香型分类研究[J].中国农学通报, 2015, 31(6):241-246.

GUO D F, HU H Z, WANG J T, et al. Study on the classification of flue-cured tobacco based on the random forest algorithm[J]. Chinese Agricultural Science Bulletin, 2015, 31(6):241-246. (in Chinese)

WU W, LIU Z, HE Y. Classification of defects with ensemble methods in the automated visual inspection of sewer pipes[J]. Pattern Analysis and Applications, 2015, 18(2):263-276.

葛俊锋, 罗予频.非对称AdaBaost算法及其在目标检测中的应用[J].自动化学报, 2009, 35(11):1403-1409.

GE J F, LUO Y P. A comprehensive study for asymmetric AdaBoost and its application in object detection[J]. Acta Automatica Sinica, 2009, 35(11):1403-1409. (in Chinese)

王磊, 郑任儿.决策树算法的比较研究[J].科技信息, 2012(30):156-157.

WANG L, ZHENG R E. A comparative study on algorithms of decision trees[J]. Science & Technology Information, 2012(30):156-157. (in Chinese)

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