Image-based Textile Decoding

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 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.