Generating Jacquard Fabric Pattern With Visual Impressions

Abstract

With jacquard fabric, designers can create complex patterns by freely defining the over-under relationships between warp yarns and weft yarns at each grid point or intersections in the fabric. Binary images are one way of representing the over-under relationships of warp and weft yarns at the grid points in a fabric pattern; an image requires an optimal number of warp-weft intersections-not too many, not too few-to produce a weave with both aesthetic and functional merits. This study proposes a method for generating jacquard fabric patterns that reproduce the visual impressions of given input images on jacquard fabric. Our method makes it possible to assign specific fabric dither masks to individual regions. These fabric dither masks can preserve the overall tone of the input image in the dithered image while appropriately controlling the number of warp-weft intersections in the pattern. As the fabric dither masks give users a considerable degree of freedom in defining texture frequencies and directional properties, the proposed method captures the visual impression of a given input image by enabling users to apply fabric dither masks to match textural features-either automatically or interactively. The former approach involves assigning the mask with the closest textural resemblance to each target region, ultimately producing a fabric pattern with a tone and texture that matches the input image. The interactive approach, meanwhile, provides the designer with an interactive interface for assigning masks. This allows designers to experiment with different expressions and track their progress visually, emphasizing or subduing specific areas at their own discretion. Index Terms-Computer graphics, design methodology, intelligent manufacturing systems, textile product. I. INTRODUCTION T HIS paper proposes a method for generating jacquard fabric patterns that reproduce the visual impressions of given input images. The method enables users to apply fabric dither masks on an individual basis. To replicate the unique textural

Image-based textile decoding

“…High-resolution images were divided into small, partial images. The images were made by observing samples represented in reference [5]. The samples were woven with black and white yarns only, and the patterns were generated from natural images.…”

“…Toyoura et al [5] proposed a dithering method for reproducing smoothly changing tones and fine details of natural images on woven fabric, focusing on representing gray scale images by using two colors of warp and weft yarns. The weaving pattern is generated by binarizing the input image using dither masks.…”

Textile defect detection using multilevel and attentional deep learning network (MLMA-Net)

“…The second one is to help gradient flow through the layers by the multiplicative operation with information factor b. Without equation ( 19), the gradient computation of back propagation is expressed in equation (20):…”

“…Compared with equation (20), equation ( 21) can stabilize the training of the whole network as the multiattentional module enables extra gradient flow in the model.…”

“…4 Moreover, deep learning 16,17 based methods, such as convolutional neural networks (CNNs), have been widely applied in single-label textile defect recognition, 18 textile defect detection, 19 and textile pattern generation. 20 For example, Lv et al 21 designed a semisupervised deep learning framework for multilabel feature selection. Zhao et al 22 proposed a multiview multilabel feedback neural network model for multilabel classification.…”