期刊文献+

基于风格迁移算法的敦煌装饰图案创新设计研究

Innovative Design of Dunhuang Decorative Patterns Based on Image Style Transfer Algorithm
下载PDF
导出
摘要 人工智能技术不断推动着艺术设计的发展,将敦煌艺术风格应用到文创产品中,实现了艺术和科技的融合,对传统文化的创新具有重要意义。本文以敦煌装饰图案为研究对象,对敦煌装饰图案的文化特征和风格迁移算法进行总结和调研,利用风格迁移算法将敦煌装饰图案迁移到重组后的敦煌装饰图案和敦煌风景中,应用在文创产品中,实现了传统文化与人工智能技术的融合。 Artificial intelligence technology continues to promote the development of art design,and the application of Dunhuang art style to cultural and creative products realizes the integration of art and technology,which is of great significance to the innovation of traditional culture.This paper takes Dunhuang decorative patterns as the research object,summarizes and investigates the cultural characteristics and style migration algorithm of Dunhuang decorative patterns,and uses the image style transfer algorithm to migrate Dunhuang decorative patterns to the restructured Dunhuang decorative patterns and Dunhuang landscapes,and applies them to the cultural and creative products,which realizes the fusion of traditional culture and artificial intelligence technology.
作者 孙婧 高海军 Sun Jing;Gao Haijun(School of Fine Arts,Beijing Institute of Fashion Technology)
出处 《色彩》 2023年第9期100-103,共4页 Fashion Color
关键词 风格迁移算法 卷积神经网络 敦煌装饰图案 文创设计 藻井图案 Image style transfer convolutional neural network Dunhuang decorative patterns cultural and creative designs caisson pattern
  • 相关文献

参考文献9

二级参考文献143

  • 1Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60 (2) 91 110.
  • 2Dalai N, Triggs B. Histograms of oriented gradients for human detection[C]//Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society Conference on. San Diego, USA: IEEE, 2005, 1 886-893.
  • 3Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786) : 504-507.
  • 4Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the catrs visual cortex[J]. The Journal of Physiology, 1962, 160(1): 106-154.
  • 5Fukushima K, Miyake S. Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in posi- tion[J]. Pattern Recognition, 1982, 15(6): 455-469.
  • 6Ruck D W, Rogers S K, Kabrisky M. Feature selection using a multilayer perceptron[J]. Journal of Neural Network Com- puting, 1990, 2(2): 40-48.
  • 7Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors[J]. Nature, 1986,3231 533 538.
  • 8LeCun Y, Denker J S, Henderson D, et al. Handwritten digit recognition with a back-propagation network[C]//Advances in Neural Information Processing Systems. Colorado, USA Is. n. ], 1990: 396-404.
  • 9LeCun Y, Cortes C. MNIST handwritten digit database[EB/OL], http//yann, lecun, com/exdb/mnist, 2010.
  • 10Waibe[ A, Hanazawa T, Hinton G, et al. Phoneme recognition using time-delay neural networks[J]. Acoustics, Speech and Signal Processing, IEEF. Transactions on, 1989, 37(3): 328-339.

共引文献2561

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部