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首页> 外文期刊>The Journal of the Textile Institute >A novel hybrid genetic and imperialist competitive algorithm for structure extraction of woven fabric images
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A novel hybrid genetic and imperialist competitive algorithm for structure extraction of woven fabric images

机译：机织织物图像结构提取的新型遗传与帝国混合竞争算法

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摘要

The woven fabric is a flexible object and to specify its parameters, applying inflexible and ordinary methods of image processing ever have considerable errors. In this regards, proposing an adaptable method to fabric image properties is concentrated to detect the yarns position. In this research, a flexible algorithm is proposed containing two stages: first, the inexact ranges of fabric parameters are determined by preprocessing colored fabric images using wavelet transform and clustering methods. Then, the hybrid genetic and imperialist competitive algorithm is applied to optimize the obtained ranges and detect the yarns position. To achieve better results, the parameters of the hybrid ICA-GA are calibrated using the Taguchi method. Results indicate that in this new method, the error value of detecting structural fabric parameters has considerably decreased to 5% as compared with common gray-scale projection method. The proposed method is capable of detecting the exact yarns position in colored fabric images with uneven color intensity and low-density weave with mean precision value of 96.2%. In the fabric images with high density weaves, the mean precision value is more than 94.72%.
机译：机织织物是一种柔性物体，要指定其参数，应用刚性和普通的图像处理方法会产生很大的误差。在这方面，集中提出一种对织物图像特性的适应性方法以检测纱线位置。在这项研究中，提出了一种灵活的算法，该算法包括两个阶段：首先，通过使用小波变换和聚类方法对彩色织物图像进行预处理，确定织物参数的不精确范围。然后，应用遗传和帝国混合竞争算法来优化获得的范围并检测纱线位置。为了获得更好的结果，使用Taguchi方法校准了混合ICA-GA的参数。结果表明，与常规灰度投影法相比，该新方法检测结构织物参数的误差值已大大降低至5％。所提出的方法能够检测颜色强度不均和低密度编织的彩色织物图像中的精确纱线位置，平均精度值为96.2％。在高密度编织的织物图像中，平均精度值大于94.72％。

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