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Related Experiment Video

Updated: Jun 28, 2026

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

Anqi Chen¹, Yicui Peng², Meng Li²

¹Chengdu Technological University, Chengdu, China.

|June 11, 2025

Summary

This study uses artificial intelligence (AI) and machine learning to extract intricate patterns from Qiang embroidery images. The method effectively generates vector graphics for digital preservation and artistic reinterpretation of cultural heritage.

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Area of Science:

Computer Vision
Digital Heritage
Pattern Recognition

Background:

Intangible Cultural Heritage (ICH) images, like Qiang embroidery, often feature fine-grained patterns and complex edges, posing challenges for traditional image processing.
Accurate extraction of these patterns is crucial for digital preservation and artistic reinterpretation.

Purpose of the Study:

To develop an innovative approach using artificial intelligence (AI) for generating vector graphics of fine-grained patterns from ICH images.
To apply and evaluate machine learning algorithms for accurate edge extraction and pattern recognition in Qiang embroidery.

Main Methods:

Pre-processing techniques, including improved adaptive median filtering (IAMF) and non-local mean filtering, were used to reduce noise in Qiang embroidery patterns.
The Xception algorithm, a convolutional neural network (CNN), was employed for edge detection and extraction to create vector graphics.

Main Results:

The proposed method successfully denoised Qiang embroidery images, enabling clear identification of pattern shape characteristics through edge extraction.
Experimental results demonstrate the effectiveness of the Xception algorithm in extracting intricate patterns for vector graphic generation.

Conclusions:

The AI-driven approach provides an effective solution for extracting Qiang embroidery patterns into two-dimensional vector graphics.
This method offers a reliable reference for the digital preservation and artistic reinterpretation of various ICH images.