Abstract
Textile companies usually manufacture fabrics using a mix of pre-colored fibers according to a traditional recipe based on their own experience. Unfortunately, mainly due to the fibers dyeing process, the colorimetric distance between the obtained fabric and the desired one results unsatisfactory with respect to a colorimetric threshold established by the technicians. In such cases, colorists are required to slightly change the original recipe in order to reduce the colorimetric distance. This trial and error process is time-consuming and requires the work of highly skilled operators. Computer-based color recipe assessment methods have been proposed so far in scientific literature to address this issue. Unlikely, many methods are still far to be reliably predictive when the fabric is composed by a high number of components. Accordingly, the present work proposes two alternative methods based on Kubelka-Munk and subtractive mixing able to perform a reliable prediction of the spectrophotometric response of a fabric obtained by means of any variation of a recipe. The assessment performed on a prototypal implementation of the two methods demonstrates that they are suitable for reliable prediction of fabric blends spectral response.
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References
Aspland, R., Shanbhag, P.: Comparison of color difference equations for textiles. CMC (2: 1) and CIEDE2000. AATCC review 4(6), 26–30 (2004)
Furferi, R., Governi, L., Volpe, Y.: Image processing-based method for glass tiles color matching. Imaging Science Journal 61(2), 183–194 (2013)
Amirshahi, S.H., Pailthorpe, M.T.: Applying the Kubelka-Munk equation to explain the color of blends prepared from precolored fibers. Textile research journal 64(6), 357–364 (1994)
Amirshahi, S.H., Pailthorpe, M.T.: An algorithm for the optimization of color prediction in blends. Textile Research Journal 65(11), 632–637 (1995)
Burlone, D.A.: Effect of Fibre Translucency on the Color of Blends of Precolored Fibres. Textile Research Journal 60(3), 162–167 (1990)
Hongying, Y., Zhu, S., Pan, N.: On the Kubelka-Munk Single-Constant/Two-Constant Theory. Textile Research Journal 80(3), 263–270 (2010)
Steams, E.I., Noechel, F.: Spectrophotometric Prediction of Color Wool Blends. Am. Dyest 33(9), 177–180 (1944)
Rong, L.I., Feng, G.U.: Tristimulus algorithm of colour matching for precoloured fibre blends based on the Stearns- Noechel model. Coloration Technology 122(2), 74–81 (2006)
Thompson, B., Hammersley, M.J.: Prediction of the colour of scoured-wool blends. Journal of the Textile Institute 69(1), 1–7 (1978)
Kazmi, S.Z., Grady, P.L., Mock, G.N., Hodge, G.L.: On-line color monitoring in continuous textile dyeing. ISA Transactions 35(1), 33–43 (1996)
Philips-Invernizzi, B., Dupont, D., Jolly-Desodt, A.M., Caze, C.: Color formulation by fiber blending using the Stearns -oechel model. Color Research and Application 27(2), 100–107 (2002)
Furferi, R., Carfagni, M.: Colour mixing modelling and simulation: Optimization of colour recipe for carded fibres. Modelling and Simulation in Engineering, vol. 2010, Article ID 487678, 9 p. (2010)
Furferi, R., Governi, L.: Prediction of the spectrophotometric response of a carded fiber composed by different kinds of coloured raw materials: An artificial neural network-based approach. Color Research and Application 36(3), 179–191 (2011)
Hawkyard, C.J.: Synthetic reflectance curves by subtractive colour mixing. Journal of the Society of Dyers and Colourists 109(78), 246–251 (1993)
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© 2015 Springer International Publishing Switzerland
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Furferi, R., Governi, L., Volpe, Y. (2015). Methods for Predicting Spectral Response of Fibers Blends. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds) New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops. ICIAP 2015. Lecture Notes in Computer Science(), vol 9281. Springer, Cham. https://doi.org/10.1007/978-3-319-23222-5_10
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DOI: https://doi.org/10.1007/978-3-319-23222-5_10
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