Combining CAD and Deep Learning Models for Style Recognition and Transformation of Arts and Crafts
Abstrak
. This article provides new insights into art and design methods for protecting styles in deep learning by preprocessing and technical identification of comprehensive datasets of different arts. This article plans a craftsman recognition style for artistic processing. The training set process of feature recognition uses algorithm recognition to accurately recognize the expected style of the model classifier of the source image. It uses a classifier to train a recognition model for precise style transfer based on the framework of handmade works. At the same time, it utilized the transfer algorithm of the target style to perform simple training model style optimization on the classifier. The research results are presented in the table. With the combination of cultural and artistic model image content, the DL model in this paper has a relatively high advantage in image accuracy recognition. This not only enriches the target style conversion of handicrafts but also protects and overlaps the artistic style of the model's image with digital craftsmanship. The foundation has been laid for the recognition of image styles to protect traditional art.
Penulis (1)
Chenhan Huang
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Sumber Database
- Semantic Scholar
- DOI
- 10.14733/cadaps.2025.s1.74-89
- Akses
- Open Access ✓