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Dyed fabric illumination estimation with regularized random vector function link network
Color Research and Application ( IF 1.4 ) Pub Date : 2020-12-04 , DOI: 10.1002/col.22602
Zhiyu Zhou 1 , Dexin Liu 1 , Jiushen Guo 1 , Jianxin Zhang 2 , Zefei Zhu 3 , Chao Wang 1
Affiliation  

The unstable light source in the printing and dyeing environment would cause the change of fabric surface color and lead to serious color difference evaluation error. To solve this problem, a dyed fabric illumination estimation with regularized random vector functional link network (RRVFL) was proposed in this study. First, the Gray‐Edge framework is used to extract the color features of the sample images collected from the actual scene, and the extracted color features and the illumination information of the sample images constitute a data set. Then, considering the ill‐conditioned solution of the output weight of the traditional random vector functional link network (RVFL), regularization is proposed to solve this problem. Thus, we constitute a highly robust RRVFL dyed fabric illumination model. By analyzing the parameters that affect the precision of RRVFL model, the optimal parameters of RRVFL can be selected. Finally, the traditional RVFL, extreme learning machine (ELM), back‐propagation (BP), regularized extreme learning machine (RELM), support vector regression (SVR), and RRVFL estimation algorithm proposed in this study were compared and analyzed using the measurement standards of angle error, colorimetric error, and T‐test, respectively. The experimental results show that RRVFL has the best predictive results and the most stable performance compared with the traditional algorithm. RRVFL is 0.00036, 2.8050, 3.3518, 4.1669, and 2.9289 less than RVFL, ELM, BP, RELM, and SVR, respectively, in terms of average angle error. RRVFL is 0.0131, 0.0763, 0.0232, 0.0241, and 0.0221 lower than RVFL, ELM, BP, RELM, and SVR, respectively, in the average colorimetric error.

中文翻译:

正则化随机向量函数链接网络的染色织物照度估计

印染环境中不稳定的光源会引起织物表面颜色的变化,并导致严重的色差评估误差。为了解决这个问题,本研究提出了一种使用正则化随机矢量功能链接网络(RRVFL)的染色织物照度估计方法。首先,使用Gray-Edge框架提取从实际场景中收集的样本图像的颜色特征,并且提取的颜色特征和样本图像的照明信息构成一个数据集。然后,考虑传统随机矢量功能链接网络(RVFL)输出权重的病态解决方案,提出了正则化方法来解决此问题。因此,我们构建了高度鲁棒的RRVFL染色织物照明模型。通过分析影响RRVFL模型精度的参数,可以选择RRVFL的最佳参数。最后,通过测量比较和分析了本研究中提出的传统RVFL,极限学习机(ELM),反向传播(BP),正则化极限学习机(RELM),支持向量回归(SVR)和RRVFL估计算法。分别是角度误差,比色误差和T检验的标准。实验结果表明,与传统算法相比,RRVFL具有最佳的预测结果和最稳定的性能。就平均角度误差而言,RRVFL分别比RVFL,ELM,BP,RELM和SVR小0.00036、2.8050、3.3518、4.1669和2.9289。RRVFL比RVFL,ELM,BP,RELM和SVR分别低0.0131、0.0763、0.0232、0.0241和0.0221,
更新日期:2021-02-03
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