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。
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.
出处
《计算机应用》
CSCD
北大核心
2013年第1期179-181,185,共4页
journal of Computer Applications
基金
陕西省教育厅科技立项项目(2010JK847)
关键词
QR码
识别
稀疏表示
压缩感知
鲁棒性
Quick Response （QR） code
recognition
sparse representation
compressed sensing
robustness
