High-quality pattern decoding of large-scale color fabric

This work develops a novel solution for generating binary patterns of large-scale fabrics using deep neural networks. It contributes to textile engineering by enabling the analysis of ancient and modern textile products. There are only two possible over-under relationships between warp and weft yarns at each crossing point, which can be simulated by a binary matrix. Generating a binary pattern from an observed fabric pattern can help designers save time and effort in reproducing fabrics. Deep neural networks have recently been applied in this field and can generate accurate binary patterns that match fabrics; however, these methods still require improvements. This paper introduces a feature point matching-based image stitching method to address the mismatch between the resolution of large fabric images and the network input requirements. Then, we preserve the contrast of color fabric patterns using principal component analysis for grayscale conversion. Finally, we propose a method for deriving pixel-wise confidence values of the label image based on receptive field size and stitching label images by accumulating weights. We show results on Jacquard fabric samples with an average of 266 thousand intersections. The ablation study showed that incorporating the two newly proposed methods achieved the highest accuracy for the textile binary pattern, with an average of 0.952 across samples. • We introduce an image stitching method based on feature point matching to acquire fabric patterns. • We employ a method based on PCA for converting color patterns into grayscale images considering warp and weft colors. • We propose a weight accumulation method to derive the confidence level of each pixel label based on the receptive field.