The comparison data table for physical image.

Harnessing the power of artificial intelligence(AI) approaches to innovatively generating the vector graphics of fine-grained patterns has become an important task in image edge extraction, particularly on the domain of intangible cultural heritage (ICH) images where they are typically fine-grained and having the complex edges. With higher autonomy, the machine learning algorithms are able to accurately extract the image information, understand and convey the concept contained in it. In this paper, we take Qiang embroidery patterns as an example due to containing fine-grained patterns, which is more suitable for the study of image processing and pattern recognition techniques. We firstly adopt appropriate pre-processing methods, improved adaptive median filtering(IAMF) and non-local mean for the two different types of Qiang embroidery patterns to reduce image noise. Then, the Xception algorithm based on convolutional neural networks(CNNs) is used for edge detection and extraction to generate vector graphics of the patterns. Experimental results show that Qiang embroidery patterns, after denoising and edge extraction, can be clearly identified the shape characteristics of the patterns. Based on this approach, the images can be converted into vector graphics for the digital preservation and further artistic reinterpretation. The use of the Xception algorithm effectively solves the problem of extraction of Qiang embroidery in two-dimensional vectorial images. In addition, our proposed method provides a reliable practical reference for the preservation of other related ICH images.

利用人工智能（AI）技术创新生成细粒度图案的矢量图形，已成为图像边缘提取领域的重要研究方向，尤其在非物质文化遗产（ICH）图像领域——这类图像通常带有细粒度特征且边缘结构复杂。具备更高自主性的机器学习算法，可精准提取图像信息，理解并传递其中蕴含的语义内涵。本文以羌绣图案为例，因其本身蕴含细粒度特征，更适配图像处理与模式识别技术的研究场景。针对两类不同的羌绣图案，我们首先采用适配的预处理方法：改进型自适应中值滤波（IAMF）与非局部均值滤波，以降低图像噪声。随后，基于卷积神经网络（CNNs）的Xception算法被用于边缘检测与提取，以生成图案的矢量图形。实验结果表明，经降噪与边缘提取处理后的羌绣图案，其形状特征可被清晰辨识。依托该方法，可将图像转换为矢量图形，用于数字化保存与后续艺术再创作。Xception算法的应用，有效解决了二维矢量图像中羌绣图案的提取难题。此外，本文提出的方法可为其他相关非物质文化遗产图像的保存工作提供可靠的实践参考。