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

The innovative generation of vector graphics with fine-grained images using Artificial Intelligence has become an important task in edge extraction. In this paper, we take Qiang embroidery image as an example due to its containing fine-grained edges, which is more suitable for the study of image processing and pattern recognition. We firstly adopt appropriate pre-processing methods, improved adaptive median filtering (IAMF) for the image to reduce image noise. Then, the Xception based on convolutional neural networks is used for edge detection and extraction. Results show that Qiang embroidery images, after denoising and edge extraction, can be clearly identified the shape characteristics of the images. Based on this approach, it can be converted into vector graphics for digital preservation and further artistic reinterpretation. The use of the Xception effectively addresses Qiang embroidery extraction in two-dimensional vector images, offering a practical reference for preserving related intangible cultural heritage.