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<p>In the contemporary digital era, the transformation of intangible cultural heritage (ICH) products represents a critical trend in their evolutionary trajectory. This study advocates for the synergistic integration of traditional craftsmanship with advanced technologies within the ICH sector. A standardized electronic image repository was established by scanning these items under uniform lighting conditions, forming the basis for generating non-heritage experimental datasets. Subsequently, a clustering algorithm was applied to extract color features from the images. Owing to its resilience against rotation, scale, and noise variations, the Scale-Invariant Feature Transform (SIFT) algorithm was utilized to delineate pattern features of non-heritage items. The analysis primarily focused on the predominant colors in contemporary ICH products, paving the way for innovative developments that incorporate augmented reality (AR) technology with non-heritage characteristics. Quantitative analyses indicated that the average hue of these products is 118.15°, predominantly within the warmer green spectrum of the color wheel. Moreover, the evaluation of pattern style similarity of non-heritage items, as determined by the SIFT algorithm, yielded an average of 88.36%. Further, regression analysis demonstrated a significant influence of hue, brightness, pattern, and texture on the effectiveness of product development, with regression coefficients of 0.276, 0.236, 0.237, and 0.159, respectively, at a significance level of P&lt;0.001. This research highlights the potential of contemporary science and technology to invigorate traditional arts, thereby contributing significantly to the preservation and revitalization of valued cultural legacies.</p>
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In the contemporary digital era, the transformation of intangible cultural heritage (ICH) products represents a critical trend in their evolutionary trajectory. This study advocates for the synergistic integration of traditional craftsmanship with advanced technologies within the ICH sector. A standardized electronic image repository was established by scanning these items under uniform lighting conditions, forming the basis for generating non-heritage experimental datasets. Subsequently, a clustering algorithm was applied to extract color features from the images. Owing to its resilience against rotation, scale, and noise variations, the Scale-Invariant Feature Transform (SIFT) algorithm was utilized to delineate pattern features of non-heritage items. The analysis primarily focused on the predominant colors in contemporary ICH products, paving the way for innovative developments that incorporate augmented reality (AR) technology with non-heritage characteristics. Quantitative analyses indicated that the average hue of these products is 118.15°, predominantly within the warmer green spectrum of the color wheel. Moreover, the evaluation of pattern style similarity of non-heritage items, as determined by the SIFT algorithm, yielded an average of 88.36%. Further, regression analysis demonstrated a significant influence of hue, brightness, pattern, and texture on the effectiveness of product development, with regression coefficients of 0.276, 0.236, 0.237, and 0.159, respectively, at a significance level of P<0.001. This research highlights the potential of contemporary science and technology to invigorate traditional arts, thereby contributing significantly to the preservation and revitalization of valued cultural legacies.

Product development strategy of non-heritage cultural and creative products under the fusion of traditional crafts and modern technology

School of College of Art and Design Nanning University

Applied Mathematics and Nonlinear Sciences

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

In the contemporary digital era, the transformation of intangible cultural heritage (ICH) products represents a critical trend in their evolutionary...

Product development strategy of non-heritage cultural and creative products under the fusion of traditional crafts and modern technology

Published Online: Jul 05, 2024

Page range: -

Received: Mar 17, 2024

Accepted: May 31, 2024

DOI: https://doi.org/10.2478/amns-2024-1682

Keywords
Clustering algorithm, SIFT algorithm, AR technology, Non-heritage cultural and creative products

© 2024 Haibei Deng., published by Sciendo

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

Product development strategy of non-heritage cultural and creative products under the fusion of traditional crafts and modern technology

Published Online: Jul 05, 2024

Page range: -

Received: Mar 17, 2024

Accepted: May 31, 2024

DOI: https://doi.org/10.2478/amns-2024-1682

KeywordsClustering algorithm, SIFT algorithm, AR technology, Non-heritage cultural and creative products

© 2024 Haibei Deng., published by Sciendo

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

Keywords
Clustering algorithm, SIFT algorithm, AR technology, Non-heritage cultural and creative products