  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 binary
matrix. 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 via 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.
