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Application of computer vision in the automatic identification and classification of woven fabric weave patterns

机译：计算机视觉在机织物组织形态自动识别与分类中的应用

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Traditionally, fabric texture identification is based on visual inspection. Recent studies have proposed automatic recognition, which utilizes computer vision to recognize the texture of different fabrics. In the recognition process, the fabric weave patterns are identified by the warp and weft floats. However, due to the optical environments and the appearance differences of fabrics and yarns, the stability and fault-tolerance of the computer vision method are yet to be improved. By using the fabric weave patterns image identification system, this study analyzed the fabric image to find out the warp and weft by the pixel gray-level cumulative values. It then cut out the image of the warp and weft floats to obtain the texture feature values, and used the Fuzzy C-Means (FCM) algorithm to identify the warp and weft floats. The identification results can derive the black-white digital image and the digital matrix of the fabric weave patterns. Finally, weaves classification is conducted based on the successfully trained two-stage Back-Propagation Neural Network. This twostage neural network can be used to construct the computer vision system to recognize fabric texture, and to increase the system reliability and accuracy. This study used the first-order and second-order co-occurrence matrix, and confirmed that fabric patterns can be identified and classified accurately with this method.
机译：传统上，织物纹理识别是基于视觉检查的。最近的研究提出了自动识别，其利用计算机视觉来识别不同织物的质地。在识别过程中，织物的织造图案由经纱和纬纱浮标识别。然而，由于光学环境以及织物和纱线的外观差异，计算机视觉方法的稳定性和容错性尚待提高。通过使用织物编织图案图像识别系统，该研究分析了织物图像，以通过像素灰度级累积值找出经纱和纬纱。然后裁剪出经纱和纬纱的图像以获得纹理特征值，并使用模糊C均值（FCM）算法识别经纱和纬纱。识别结果可以得出黑白数字图像和织物编织图案的数字矩阵。最后，基于成功训练的两阶段反向传播神经网络进行编织分类。该两阶段神经网络可用于构建计算机视觉系统，以识别织物纹理，并提高系统的可靠性和准确性。这项研究使用了一阶和二阶共现矩阵，并确认可以使用此方法准确识别和分类织物图案。

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