TL;DR
This paper introduces a novel method for decoding binary textile patterns from woven fabric images using an intermediate representation, achieving 93% accuracy in pattern reconstruction without relying solely on deep learning.
Contribution
The work proposes an intermediate representation framework enabling deep learning-based decoding of textile patterns from images, overcoming challenges of misaligned crossing points.
Findings
Achieved 93% correct pattern decoding accuracy
Demonstrated effectiveness of intermediate representation approach
Enabled pattern reconstruction without direct deep learning on raw images
Abstract
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…
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