Development of a color matching algorithm for digital transfer textile printing using an artificial neural network and multiple regression

Hwang, Jong Pil; Kim, Sung Min; Park, Chang Gyu

doi:10.1177/0040517515569525

Cited by 21 publications

(13 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Experiments and Resultsmentioning

confidence: 99%

“…To find the color differences, the spot color should be covered to standard red, blue and green (sRGB) color values. 37 Then the CIE XYZ value can be calculated by using the sRGB values according to the following equation X Y Z Finally, the CIE Lab color value can be obtained by a nonlinear transformation of the CIE XYZ color value, as shown in the following equation…”

Section: Recipes With Three and More Primary Colorsmentioning

confidence: 99%

See 1 more Smart Citation

An enhanced quantum-inspired gravitational search algorithm for color prediction based on the absorption spectrum

Gao

Zhang

Zhou

et al. 2020

Textile Research Journal

View full text Add to dashboard Cite

Spot color is widely applied to printing and packing in modern industry, which can satisfy the individualization requirements and express the emotion of products. Color prediction is the core technique for spot color restoration. In this paper, a method that combines the least squares method and gravitation search algorithm is proposed to address the color prediction by using the absorption spectrum. Firstly, the spectral transmittance of the thin film with high transmission and low reflectance characteristics is researched to find the absorbance. Secondly, the least squares method is used to ascertain the primary colors of the spot color. Thirdly, an enhanced quantum gravitation search algorithm is designed to predict the spot color. The predicted results on the 30 spot colors show that the proposed method has higher accuracy in comparison with the three existed methods. The color differences between the prepared spot colors and the reproduced spot colors are all less than 3, in which 75% of the color differences are less 1 and 35% of the color differences are less 0.1. All the results confirm that the proposed method can predict the spot color accurately.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Recipes With Three and More Primary Colorsmentioning

confidence: 99%

An enhanced quantum-inspired gravitational search algorithm for color prediction based on the absorption spectrum

Gao

Zhang

Zhou

et al. 2020

Textile Research Journal

View full text Add to dashboard Cite

show abstract

“…As is widely recognized, 16 ANNs are computational systems that simulate the microstructure of a biological nervous system. ANNs can be trained to perform a particular function, either from information from outside the network or by the neurons themselves in response to the input.…”

Section: Artificial Neural Network Training and Prediction Of The Blementioning

confidence: 99%

Color matching of fabric blends: hybrid Kubelka-Munk + artificial neural network based method

Furferi

Governi

Volpe

2016

J. Electron. Imaging

View full text Add to dashboard Cite

Abstract. Color matching of fabric blends is a key issue for the textile industry, mainly due to the rising need to create high-quality products for the fashion market. The process of mixing together differently colored fibers to match a desired color is usually performed by using some historical recipes, skillfully managed by company colorists. More often than desired, the first attempt in creating a blend is not satisfactory, thus requiring the experts to spend efforts in changing the recipe with a trial-and-error process. To confront this issue, a number of computer-based methods have been proposed in the last decades, roughly classified into theoretical and artificial neural network (ANN)-based approaches. Inspired by the above literature, the present paper provides a method for accurate estimation of spectrophotometric response of a textile blend composed of differently colored fibers made of different materials. In particular, the performance of the Kubelka-Munk (K-M) theory is enhanced by introducing an artificial intelligence approach to determine a more consistent value of the nonlinear function relationship between the blend and its components. Therefore, a hybrid K-M+ANN-based method capable of modeling the color mixing mechanism is devised to predict the reflectance values of a blend.

show abstract

“…The obtained results were shown that the reverse characterization model based on neural network has more accuracy. Hwang et al introduced a color matching algorithm for digital transfer textile printing using an artificial neural network and multiple regression. For this purpose, sRGB values of printed fabric were obtained by a flatbed scanner.…”

Section: Introductionmentioning

confidence: 99%

Improve neural network‐based color matching of inkjet textile printing by classification with competitive neural network

Hajipour

Shams‐Nateri

2018

Color Research & Application

View full text Add to dashboard Cite

Nowadays, with increasing use of digital printing in the textile industry, characterization and color matching are very much considered. There is a very complicated relationship between pixel values of input digital image and colorimetric parameters of printed textile samples. One of the most important used methods for inverse characterization of printer and prediction of CMYK digital values is neural network. In this study, the prediction accuracy of CMYK digital values were improved by dividing the training samples into 2, 4, 6, 8, and 10 subgroups using creating a competitive neural network. For classification of samples, L*a*b* or XYZ were introduced to a competitive neural network as input parameters. Then, the classification of test samples was performed by trained competitive neural network. To predict the of CMYK digital values of input digital image, a cascade‐forward back propagation neural network is trained by L*a*b* of each subgroup. The results obtained show that the prediction accuracy of CMYK digital values were improved by suggested method. The best result was obtained by classification of samples with L*a*b* into eight subgroups and using a cascade‐forward back propagation neural network with 4, 4, and 4 neurons in hidden layers.

show abstract

Development of a color matching algorithm for digital transfer textile printing using an artificial neural network and multiple regression

Cited by 21 publications

References 6 publications

An enhanced quantum-inspired gravitational search algorithm for color prediction based on the absorption spectrum

An enhanced quantum-inspired gravitational search algorithm for color prediction based on the absorption spectrum

Color matching of fabric blends: hybrid Kubelka-Munk + artificial neural network based method

Improve neural network‐based color matching of inkjet textile printing by classification with competitive neural network

Contact Info

Product

Resources

About