Digital Protection and Display of Cantonese Embroidery Patterns Based on CAD and Human-computer Interaction

Xiaoli Qi; Hongming Li

doi:10.47297/taposatWSP2633-456914.20250601

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Digital Protection and Display of Cantonese Embroidery Patterns Based on CAD and Human-computer Interaction

Volume 6, Issue 1

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Digital Protection and Display of Cantonese Embroidery Patterns Based on CAD and Human-computer Interaction
Digital Protection and Display of Cantonese Embroidery Patterns Based on CAD and Human-computer Interaction
Dongguan Polytechnic, Dongguan, Guangdong 523808, China
Vol. 6, Issue 1, Pages: 68-78(2025)
Published：21 January 2025
DOI：10.47297/taposatWSP2633-456914.20250601
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Qi Xiaoli,Li Hongming.Digital Protection and Display of Cantonese Embroidery Patterns Based on CAD and Human-computer Interaction[J].Theory and Practice of Science and Technology,2025,06(01):68-78.
DOI：

Qi Xiaoli,Li Hongming.Digital Protection and Display of Cantonese Embroidery Patterns Based on CAD and Human-computer Interaction[J].Theory and Practice of Science and Technology,2025,06(01):68-78. DOI： 10.47297/taposatWSP2633-456914.20250601.

摘要

Abstract

In the digital preservation and display of Cantonese embroidery patterns

computer-human-computer interaction technology is particularly important. By utilizing computer-assisted human-machine interaction technology

this article presents a cutting-edge digital preservation and display of the image algorithm model for Xiangyun yarn patterns. The research method scanned high-resolution data by capturing the complex texture of auspicious cloud colours in Cantonese embroidery patterns. The use of image processing algorithms eliminates a series of noise generated during the process of capturing pattern tones and sharpens details. Following this

the refined image undergoes vectorization and precise modelling via CAD software

yielding an editable and scalable digital representation. Additionally

we've crafted an interactive exhibition system that immerses viewers in the cultural allure of Cantonese Embroidery through HCI. Experimental results demonstrate that our enhanced model outperforms the original AlexNet in embroidery classification

notably improving accuracy

recall

precision

and the F1 score. This underscores the significance of tailoring deep learning models to specific applications.

关键词

Keywords

references

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