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A discrete information coding matrix algorithm based on image features for tracking circulation of commodities

机译:基于图像特征的离散信息编码矩阵算法跟踪商品流通

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摘要

For fast-consumed-commodities industry, it is usually difficult for producers to track the commodities in their circulation process, especially, when their labels have been damaged. In this paper, we develop a novel method based on channel coding theory of forward error correction (FEC) to prevent the flooding of counterfeit products with a very low cost and easy implementation. This anti-interference matrix information coding method has a strong ability to resist damage and high recovery rate, and can produce a large number of nonrepeated codes in terms of different characteristics of demand, yielding a great enhancement of anti-counterfeiting trace capacity. However, the process of reading information strongly relies on artificial means that requires a high cost and reduces the efficiency. For this reason, we propose an efficient approach for image feature extraction based on discrete information coding matrix. It takes the advantage of wearing perspective clipping method to obtain the revised image with printed matrix information and then extract the regions of interest (ROI) that carry the information about the coding matrix by working out its row and column coordinates with the center of gravity method. The matrix code recognition software is implemented. The simulation and experimental results show that the method is effective and robust.
机译:对于快速消费品行业,生产商通常很难在流通过程中追踪商品,尤其是当其标签受损时。在本文中,我们开发了一种基于前向纠错(FEC)信道编码理论的新颖方法,以非常低的成本且易于实现,可以防止假冒产品泛滥。这种抗干扰矩阵信息编码方法具有很强的抗破坏能力和较高的恢复率,并且可以根据需求的不同特征产生大量的非重复编码,从而大大提高了防伪痕迹的能力。但是,读取信息的过程强烈依赖于人工手段,这需要高成本并降低效率。因此,我们提出了一种基于离散信息编码矩阵的图像特征提取有效方法。它利用佩戴透视裁剪法的优势来获得带有打印矩阵信息的修正图像,然后通过使用重心法计算出其行和列坐标来提取包含有关编码矩阵信息的感兴趣区域(ROI) 。实现矩阵码识别软件。仿真和实验结果表明,该方法有效,鲁棒。

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