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Research on the Generative Mechanism of Intelligent Reconstruction for the Integration of Traditional Paper-cutting Art Symbols and Modern Pattern Designs

Authors :
Zhang Zhe
Xu Jiang
Source :
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
Publication Year :
2024
Publisher :
Sciendo, 2024.

Abstract

In the realm of Chinese traditional arts, paper-cutting symbols embody deep-seated aesthetic and cultural values. Within the framework of artificial intelligence, the fusion of these conventional symbols with contemporary pattern design through intelligent reconstruction processes promotes innovation in modern design. This paper introduces a novel pattern feature extraction algorithm specifically designed for identifying and extracting the essence of traditional paper-cutting art. Utilizing Deep Convolutional Generative Adversarial Networks (DCGAN), an automated method for generating woven patterns is developed, integrating conventional motifs with modern design elements. Additionally, a segmentation model based on DeepLabV3+ semantics is constructed to facilitate this integration. The study focuses on the fusion of traditional paper-cutting symbols with fabric patterns, providing a new direction for textile design research. The empirical evaluation involved seven experimenters who predominantly favored patterns that blended abstract and traditional styles, with the average style rating exceeding 3.2. Moreover, the color performance analysis of the reconstructed fabric patterns across five regions showed color difference values greater than 4, indicating superior color fidelity. These findings underscore the potential of combining AI-driven techniques with traditional art forms to enhance and revolutionize modern design practices.

Details

Language :
English
ISSN :
24448656
Volume :
9
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Applied Mathematics and Nonlinear Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.ff25a4405bae4e25b89a3159622258e1
Document Type :
article
Full Text :
https://doi.org/10.2478/amns-2024-1671