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Zernike-CNNs for image preprocessing and classification in printed register detection
Multimedia Tools and Applications ( IF 3.6 ) Pub Date : 2021-07-31 , DOI: 10.1007/s11042-021-10981-2
Wang Sheng 1 , Lv Lin-Tao 1 , Yang Hong-Cai 1 , Lu Di 2
Affiliation  

In the register detection of printing field, a new approach based on Zernike-CNNs is proposed. The edge feature of image is extracted by Zernike moments (ZMs), and a recursive algorithm of ZMs called Kintner method is derived. An improved convolutional neural networks (CNNs) are investigated to improve the accuracy of classification. Based on the classic convolutional neural network (CNN), the improved CNNs adopt parallel CNN to enhance local features, and adopt auxiliary classification part to modify classification layer weights. A printed image is trained with 7 × 400 samples and tested with 7 × 100 samples, and then the method in this paper is compared with other methods. In image processing, Zernike is compared with Sobel method, Laplacian of Gaussian (LoG) method, Smallest Univalue Segment Assimilating Nucleus (SUSAN) method, Finite Impusle Response (FIR) method, Multi-scale Morphological Gradient (MMG) method. In image classification, improved CNNs are compared with classical CNN. The experimental results show that Zernike-CNNs have the best performance, the mean square error (MSE) of the training samples reaches 0.0143, and the detection accuracy of training samples and test samples reached 91.43% and 94.85% respectively. The experiments reveal that Zernike-CNNs are a feasible approach for register detection.



中文翻译:

Zernike-CNNs 用于印刷套准检测中的图像预处理和分类

在印刷领域的套准检测方面,提出了一种基于Zernike-CNNs的新方法。利用Zernike矩(ZMs)提取图像的边缘特征,并推导出ZMs的递归算法Kintner方法。研究了改进的卷积神经网络 (CNN) 以提高分类的准确性。改进的 CNN 在经典卷积神经网络 (CNN) 的基础上,采用并行 CNN 增强局部特征,并采用辅助分类部分修改分类层权重。一个打印的图像用7×400个样本进行训练,用7×100个样本进行测试,然后将本文中的方法与其他方法进行比较。在图像处理中,Zernike 与 Sobel 方法、高斯拉普拉斯算子 (LoG) 方法、最小单值段同化核 (SUSAN) 方法进行了比较,有限脉冲响应 (FIR) 方法、多尺度形态梯度 (MMG) 方法。在图像分类中,将改进的 CNN 与经典 CNN 进行比较。实验结果表明,Zernike-CNNs性能最好,训练样本的均方误差(MSE)达到0.0143,训练样本和测试样本的检测准确率分别达到91.43%和94.85%。实验表明 Zernike-CNN 是一种可行的寄存器检测方法。分别为 43% 和 94.85%。实验表明 Zernike-CNN 是一种可行的寄存器检测方法。分别为 43% 和 94.85%。实验表明 Zernike-CNN 是一种可行的寄存器检测方法。

更新日期:2021-08-01
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