Abstract
Digital textile printing was appeared in 1980s, which printed directly onto the surface of fabric with digital original by ink-jet printer and got high quality color printing textile. The spectral characterization of printer is the key technology for the textile printing. This paper presented a spectral characterizing model based on RBF neural network, which optimized the RBF neural network by extending the input variables of neural network. Experimental results showed that the 90% spectral errors of testing color samples are less than 0.04, the average spectral error is 0.025, the maximum spectral error is 0.066; while the 90% color differences (ΔE2000) of testing color samples are less than 2.8, the average value is 1.89, the maximum value is 8.5. That means the model could effectively improve the characterization chromaticity and spectral precision.
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Liu, Z., Liang, Y. (2018). The Spectral Characterizing Model Based on Optimized RBF Neural Network for Digital Textile Printing. In: Zhao, P., Ouyang, Y., Xu, M., Yang, L., Ren, Y. (eds) Applied Sciences in Graphic Communication and Packaging. Lecture Notes in Electrical Engineering, vol 477. Springer, Singapore. https://doi.org/10.1007/978-981-10-7629-9_7
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DOI: https://doi.org/10.1007/978-981-10-7629-9_7
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