Journal Article

Generate vector graphics of fine-grained pattern based on the Xception edge detection

Peng, Yicui, Chen, Anqi, Li, Meng, Wen, Xiang, Huang, Guo, Chen, Hao, Hu, Jinrong, Liu, Chang

2025

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Algorithms / Artificial Intelligence / Artificial neural networks / Biology and Life Sciences / Computer and Information Sciences / Computer Graphics / Cultural heritage / Cultural resources / Culture / Deep learning / Digital imaging / Digital preservation / Edge detection / Engineering and Technology / Humans / Image processing / Image Processing, Computer-Assisted - methods / Information processing / Inheritances / Innovations / Machine Learning / Methods / Multiculturalism & pluralism / Needlework / Neural networks / Neural Networks, Computer / Noise reduction / Pattern recognition / Pattern Recognition, Automated - methods / Physical Sciences / Preservation / Research and Analysis Methods / Social Sciences