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基于磁性纤维的加密纸张图像采集与识别研究
Research on Acquisition and Recognition of Image on Encrypted Paper Made of Magnetic Fibers
收稿日期:2021-01-11  
DOI:10.11980/j.issn.0254-508X.2021.06.010
关键词:  加密纸张  磁性纤维  图像采集  图像识别
Key Words:encrypted paper  magnetic fiber  image collection  image recognition
基金项目:陕西省榆林市2020年科技计划项目(CXY-2020-090)。
作者单位邮编
张开生 陕西科技大学电气与控制工程学院陕西西安710021 710021
王泽 陕西科技大学电气与控制工程学院陕西西安710021 710021
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摘要:针对磁性纤维的加密纸张从肉眼无法识别加密图案的问题,提出一种基于磁性纤维加密纸张的图像采集与识别方法。加密图像采集装置首先对磁性纤维加密纸张进行强磁处理,其次通过向磁性纤维加密纸张喷洒磁粉以使加密图案显现出来,最后利用CCD相机拍摄加密纸张图像。针对拍摄的原始图像提出将改进的机器视觉算法与卷积循环神经网络(CRNN)相结合的方法构建纸张加密图案识别模型。通过组合高斯滤波、Sobel边缘检测算子、改进的最大类间方差法(OTSU)等算法实现对加密图案的检测与分割,然后将分割后的图像输入CRNN网络完成磁性纤维加密图像的特征提取和识别。结果表明,模型识别准确率达到98.37%,能够较好地解决基于磁性纤维加密纸张的加密图案识别问题。
Abstract:A acquisition and recognition method of the image on encrypted paper made of magnetic fiber was proposed aiming at the problem that the encrypted pattern on encrypted paper made of magnetic fibers cannot be recognized by naked eyes. The encrypted image acquisition device first performed strong magnetic treatment on the magnetic-fiber encrypted paper, then sprayed magnetic powder onto the magnetic-fiber encrypted paper to make the encrypted pattern appear,finally a CCD camera was used to obtain the image on the encrypted paper. A method combining an improved machine vision algorithm and Convolutional Recurrent Neural Networks(CRNN)was proposed based on the original images to construct a recognition model for encrypted paper pattern. The detection and segmentation of the encrypted pattern were realized through the combination of Gaussian filtering, Sobel edge detection operator, improved OTSU, etc., the segmented images were then input into the CRNN network to complete the feature extraction and recognition of the magnetic-fiber encrypted image. Experimental results showed that the accuracy of the proposed model reached 98.37%, indicating it could solve the problem of recognition of encrypted pattern on encrypted paper made of magnetic fiber.
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