计算机应用 ›› 2013, Vol. 33 ›› Issue (01): 179-181.DOI: 10.3724/SP.J.1087.2013.00179

• 人工智能 • 上一篇    下一篇

基于稀疏表示的QR码识别

孙道达,赵健,王瑞,冯宁,胡江华   

  1. 西北大学 信息科学与技术学院, 西安 710127
  • 收稿日期:2012-07-12 修回日期:2012-08-19 出版日期:2013-01-01 发布日期:2013-01-09
  • 通讯作者: 赵健
  • 作者简介:孙道达(1984-),男,云南宣威人,硕士研究生,主要研究方向:稀疏表示、二维码识别;赵健(1973-),男,河北河间人,教授,博士,主要研究方向:图像检索、仿射传播、稀疏表示、信息安全、物联网;王瑞(1986-),女,陕西咸阳人,硕士研究生,主要研究方向:超完备稀疏表示、图像修复。
  • 基金资助:

    陕西省教育厅科技立项项目(2010JK847)

QR code recognition based on sparse representation

SUN Daoda,ZHAO Jian,WANG Rui,FENG Ning,HU Jianghua   

  1. School of Information Science and Technology, Northwest University, Xi'an Shaanxi 710127, China
  • Received:2012-07-12 Revised:2012-08-19 Online:2013-01-01 Published:2013-01-09
  • Contact: ZHAO Jian

摘要: 针对QR码图像受污染、破损、遮挡时识别软件无法识别的问题,提出一种基于稀疏表示的QR码识别方法。以40类QR码图像作为研究对象,每类13幅,其中每类随机选取3幅共120幅作为训练样本,余下400幅作为测试样本。所有训练样本组成稀疏表示字典,测试样本为训练样本的稀疏线性组合,表示系数是稀疏的,对每一个测试样本,计算其在字典上的投影,具有最小残差值的类别,即为分类所属类别。最后将提出的方法与QR码识读软件PsQREdit的识别结果做了对比和分析。实验结果表明:提出的方法对于部分受污染、破损、遮挡的图像仍能正确识别,具有很好的鲁棒性,为QR码的识别提供了一种新的有效方案。

关键词: QR码, 识别, 稀疏表示, 压缩感知, 鲁棒性

Abstract: With regard to the problem that recognition software does not work when the Quick Response (QR) code image is contaminated, damaged or obscured, a QR code recognition method based on sparse representation was proposed. Forty categories QR code images were used as research subjects and each category has 13 images. Three images were randomly selected from each category and thus a total of 120 images were got as the training sample and the remaining 400 as test sample. Sparse representation dictionary was composed of all training samples. The test samples were a sparse linear combination of the training samples and the coefficients were sparse. The projection of each test sample in the dictionary was calculated, so category with the smallest residual was classification category. Finally, comparison and analysis were done between the recognition results of the proposed method and the QR code recognition software PsQREdit. The experimental results show that, the proposed method is able to correctly identify for partially contaminated, damaged and obscured image, and it has good robustness. It is a new effective means for the recognition of QR code.

Key words: Quick Response (QR) code, recognition, sparse representation, compressed sensing, robustness

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