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<p>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.</p>
</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.

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

Shanxi Provincial Academy of Educational Sciences

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the realm of Chinese traditional arts, paper-cutting symbols embody deep-seated aesthetic and cultural values. Within the framework of artificial...

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

Publicado en línea: 05 jul 2024

Páginas: -

Recibido: 23 feb 2024

Aceptado: 23 may 2024

DOI: https://doi.org/10.2478/amns-2024-1671

Palabras clave
Design fusion, Intelligent reconstruction generation, DCGAN, DeepLabV3+ semantic segmentation model

© 2024 Zhe Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Publicado en línea: 05 jul 2024

Páginas: -

Recibido: 23 feb 2024

Aceptado: 23 may 2024

DOI: https://doi.org/10.2478/amns-2024-1671

Palabras claveDesign fusion, Intelligent reconstruction generation, DCGAN, DeepLabV3+ semantic segmentation model

© 2024 Zhe Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Design fusion, Intelligent reconstruction generation, DCGAN, DeepLabV3+ semantic segmentation model