Computational fusion of Chinese Gongbi figure painting elements in algorithmic ceramic design methodologies

This research explores the computational integration of traditional Chinese Gongbi figure painting elements with modern ceramic design methodologies. The goal is to preserve and reinterpret cultural heritage by algorithmically fusing Gongbi features—intricate brushwork, calligraphy, seals, and imagery—extracted via image processing techniques like edge detection and SIFT. These features are incorporated into ceramic design templates, which are optimized using the Dynamic Sailfish Optimizer (DSFO) for aesthetic coherence and functional adaptability in production. Digitized designs are securely archived using Advanced Encryption Standard (AES) encryption, ensuring integrity and confidentiality during storage and transmission. Secure access controls and blockchain-based protocols (smart contracts, cryptography) provide verifiable, decentralized, traceable, and non-repudiable access management, enabling controlled licensing and ethical reuse. Results demonstrate that the Gongbi-integrated designs successfully maintain traditional visual richness while being functionally viable. The AES system ensures robust digital rights protection, and user authentication achieves over 90% success. Evaluations show high satisfaction with the designs' aesthetic appeal, cultural accuracy, and production feasibility, confirming the method's effectiveness in bridging heritage and computational creativity.