As a branch of the field of machine learning, deep learning technology is abrupt in various computer vision tasks with its powerful functional learning functions. The deep learning method can extract the required features from the original data and dynamically adjust and update the parameters of the neural network through the backpropagation algorithm so as to achieve the purpose of automatically learning features. Compared with the method of extracting features manually, the recognition accuracy is improved, and it can be used for the segmentation of copperplate printing images. This article mainly introduces the research on the key algorithm of the copperplate printing image segmentation based on deep learning and intends to provide some ideas and directions for improving the copperplate printing image segmentation technology. This paper introduces the related principles, watershed algorithm, and guided filtering algorithm of copperplate printing image synthesis process and establishes an image segmentation model. As a result, a deep learning-based optimization algorithm mechanism for the segmentation of copper engraving printing images is proposed, and experimental steps such as main color extraction in the segmentation of copper engraving printing images, adaptive main color extraction based on fuzzy set 2, and main color extraction based on fuzzy set 2 are proposed. Experimental results show that the average processing time of each image segmentation model in this paper is 0.39 seconds, which is relatively short.

中文翻译：

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基于深度学习的铜版印刷图像分割关键算法

作为机器学习领域的一个分支，深度学习技术以其强大的功能学习功能突然出现在各种计算机视觉任务中。深度学习方法可以从原始数据中提取出所需的特征，并通过反向传播算法动态调整和更新神经网络的参数，从而达到自动学习特征的目的。与手工提取特征的方法相比，提高了识别精度，可用于铜版印刷图像的分割。本文主要介绍基于深度学习的铜版印刷图像分割关键算法的研究，旨在为改进铜版印刷图像分割技术提供一些思路和方向。介绍了铜版印刷图像合成过程的相关原理，分水岭算法和导引滤波算法，建立了图像分割模型。结果，提出了一种基于深度学习的铜版画图像分割优化算法机制，并进行了铜版画图像分割中的主色提取，基于模糊集2的自适应主色提取等实验步骤。提出了基于模糊集2的主色提取方法。实验结果表明，本文每种图像分割模型的平均处理时间为0.39秒，相对较短。并指导铜版印刷图像合成过程的滤波算法，建立图像分割模型。因此，提出了一种基于深度学习的铜版画图像分割优化算法机制，并进行了铜版画图像分割中的主色提取，基于模糊集2的自适应主色提取等实验步骤。提出了基于模糊集2的主色提取方法。实验结果表明，本文每个图像分割模型的平均处理时间为0.39秒，相对较短。并指导铜版印刷图像合成过程的滤波算法，建立图像分割模型。因此，提出了一种基于深度学习的铜版画图像分割优化算法机制，并进行了铜版画图像分割中的主色提取，基于模糊集2的自适应主色提取等实验步骤。提出了基于模糊集2的主色提取方法。实验结果表明，本文每个图像分割模型的平均处理时间为0.39秒，相对较短。提出了铜雕印刷图像分割中的主色提取，基于模糊集2的自适应主色提取，基于模糊集2的主色提取等实验步骤。实验结果表明，本文每个图像分割模型的平均处理时间为0.39秒，相对较短。提出了铜雕印刷图像分割中的主色提取，基于模糊集2的自适应主色提取，基于模糊集2的主色提取等实验步骤。实验结果表明，本文每个图像分割模型的平均处理时间为0.39秒，相对较短。