A textile fabric consists of countless parallel vertical yarns (warps) and horizontal yarns (wefts). While common looms can weave repetitive patterns, Jacquard looms can weave the patterns without repetition restrictions. A pattern in which the warps and wefts cross on a grid is defined in a binarymatrix. The binary matrix can define which warp and weft is on top at each grid point of the Jacquard fabric. The process can be regarded as encoding from pattern to textile. In this work, we propose a decoding method that generates a binary pattern from a textile fabric that has been already woven. We could not use a deep neural network to learn the process based solely on the training set of patterns and observed fabric images. The crossing points in the observed image were not completely located on the grid points, so it was difficult to take a direct correspondence between the fabric images and the pattern represented by the matrix in the framework of deep learning. Therefore, we propose a method that can apply the framework of deep learning viau the intermediate representation of patterns and images. We show how to convert a pattern into an intermediate representation and how to reconvert the output into a pattern and confirm its effectiveness. In this experiment, we confirmed that 93% of correct pattern was obtained by decoding the pattern from the actual fabric images and weaving them again.

中文翻译：

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基于图像的纺织品解码

纺织品由无数平行的垂直纱线（经线）和水平纱线（纬线）组成。普通织机可以编织重复的图案，提花织机可以编织图案而没有重复限制。在二进制矩阵中定义了经线和纬线在网格上交叉的模式。二进制矩阵可以定义在提花织物的每个网格点上哪个经纱和纬纱在顶部。该过程可以视为从图案到纺织品的编码。在这项工作中，我们提出了一种解码方法，该方法可以从已经编织的织物中生成二进制图案。我们不能使用深度神经网络仅基于训练的图案和观察到的织物图像来学习过程。观察图像中的交叉点未完全位于网格点上，因此在深度学习的框架中很难在织物图像和矩阵表示的图案之间取得直接的对应关系。因此，我们提出了一种可以在模型和图像的中间表示中应用深度学习框架的方法。我们展示了如何将模式转换为中间表示，以及如何将输出转换为模式并确认其有效性。在本实验中，我们确认通过从实际的织物图像中解码出图案并再次进行编织，可以获得93％的正确图案。我们展示了如何将模式转换为中间表示，以及如何将输出转换为模式并确认其有效性。在本实验中，我们确认通过从实际的织物图像中解码出图案并再次进行编织，可以获得93％的正确图案。我们展示了如何将模式转换为中间表示，以及如何将输出转换为模式并确认其有效性。在本实验中，我们确认通过从实际的织物图像中解码出图案并再次进行编织，可以获得93％的正确图案。