[1]严小红.计算机视觉在条形码缺陷检测中的应用[J].华侨大学学报(自然科学版),2017,38(1):109-112.[doi:10.11830/ISSN.1000-5013.201701021]
 YAN Xiaohong.Application of Computer Vision in Defect Bar Code Detection[J].Journal of Huaqiao University(Natural Science),2017,38(1):109-112.[doi:10.11830/ISSN.1000-5013.201701021]
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计算机视觉在条形码缺陷检测中的应用()
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《华侨大学学报(自然科学版)》[ISSN:1000-5013/CN:35-1079/N]

卷:
第38卷
期数:
2017年第1期
页码:
109-112
栏目:
出版日期:
2017-01-09

文章信息/Info

Title:
Application of Computer Vision in Defect Bar Code Detection
文章编号:
1000-5013(2017)01-0109-04
作者:
严小红12
1. 新疆交通职业技术学院 运输管理学院, 新疆 乌鲁木齐 831401; 2. 南京航空航天大学 航空宇航学院, 江苏 南京 210016
Author(s):
YAN Xiaohong12
1. School of Transportation Management, Xinjiang Vocational and Technical College of Communications, Urumqi 831401, China; 2. College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
关键词:
缺陷条形码 机器视觉 Ostu分割 Hough变换
Keywords:
defect bar code machine vision Ostu segmentation Hough transform
分类号:
TP242.63
DOI:
10.11830/ISSN.1000-5013.201701021
文献标志码:
A
摘要:
提出一种新的基于计算机视觉技术的识别方法.通过各种计算机视觉算法的合理配置,达成对缺陷条形码的修正和识别.在预处理阶段,采取线性灰度化方法和Ostu阈值分割方法,增强黑色条纹和白色背景之间的对比度;在条纹定位阶段,采取Canny边缘检测和Hough变换,有效定位黑色条纹对应的直线特征.实验结果表明:该方法对缺陷条形码的识别是有效的.
Abstract:
A new recognition method based on computer vision technology is put forward. Through the reasonable configuration of various computer vision algorithms, the correction and identification of defective bar code is achieved. In the preprocessing stage, the linear gray level method and the Ostu threshold segmentation method are adopted to enhance the contrast between the black stripes and white background. In the phase of fringe orientation, Canny edge detection and Hough transform are adopted to effectively locate the linear features of black stripes. The experimental results show that this method is effective for the identification of defective bar code.

参考文献/References:

[1] 赖丽旻,洪青阳.声纹识别在开放仪器管理中的应用[J].华侨大学学报(自然科学版),2015,36(5):517-521.
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[4] 黄小英.基于图像的条形码识别的算法研究及设计[J].电子技术,2011,38(5):21-22.
[5] TEKIN E,COUGHLAN J.A Bayesian algorithm for reading 1D barcodes[C]//Canadian Conference on Computer and Robot Vision.Washington D C:IEEE Computer Society,2009:61-67.
[6] 陈萍芸,林春深.一种改进的动脉CT图像去噪方法[J].华侨大学学报(自然科学版),2015,36(4):443-448.
[7] 马超.面对条形码图像缺陷的表面检测系统的研究与实现[D].北京:北京邮电大学,2013:10-21.
[8] 王霞玲,吕岳,文颖.复杂背景和非均匀光照环境下的条码自动定位和识别[J].智能系统学报,2010,5(1):35-40.
[9] SPAGNOLOA G S,COZZELLAA L,De SANTIS M.New 2D barcode solution on computer generated holograms: Holographic barcode[C]//International Symposium on Communications, Control and Signal Processing.Rome:IEEE Press,2012:1-5.
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备注/Memo

备注/Memo:
收稿日期: 2016-11-25
通信作者: 严小红(1987-),女,讲师,博士研究生,主要从事软件工程、测试计量技术及仪器的研究.E-mail:312090008@qq.com.
基金项目: 国家自然科学基金资助项目(12WK02)
更新日期/Last Update: 2017-01-20