Abstract
Deep learning technology has developed significantly in the field of pattern recognition in recent years. As an important research achievement in the field of artificial intelligence, multi-layer artificial neural network has achieved impressive practical results in visual processing. In particular, the style migration algorithm based on the convolution neural network CNN provides a new opportunity for the design of textile patterns by mixing visual quality from multiple source images to create new output images. This paper combines the fractal pattern with the style migration algorithm based on deep learning, and uses the deep learning framework Tensorflow to realize the intelligent aided design system of textile pattern based on deep learning. The integrity of the section is more in line with the textile printing requirements.
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Acknowledgments
This work was funded by Beijing Science and Technology Program (Z171100005017004).
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Wang, Y., Liu, Z. (2019). Intelligent Aided Design of Textile Patterns Based on Deep Learning. In: Deng, K., Yu, Z., Patnaik, S., Wang, J. (eds) Recent Developments in Mechatronics and Intelligent Robotics. ICMIR 2018. Advances in Intelligent Systems and Computing, vol 856. Springer, Cham. https://doi.org/10.1007/978-3-030-00214-5_139
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DOI: https://doi.org/10.1007/978-3-030-00214-5_139
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