Extracting fabric hand information from visual representations of flared skirts

Abstract

In online transactions of textile products, fabric hand was thought to be inaccessible to consumers. Recently, much effort has been made to study the feasibility of providing consumers with a real sense of fabric through a virtual experience. The current paper proposes to extract fabric hand information from the perspective of visual perception. Two sensory experiments are conducted according to the standardized sensory evaluation procedures on a set of representative textile fabrics by two trained panels. The first experiment is aimed to measure how much fabric hand can be perceived through fabrics’ visual displays. On the basis of the positive results obtained, the second experiment is carried out to further investigate the interactive mechanism between samples’ visual features and their tactile properties. A novel algorithm based on rough set theory and fuzzy set theory is proposed in order to quantitatively measure relations between different sensory information.

Discovering the relationships between yarn and fabric properties using association rule mining

“…Table 5 shows some rules discovered by the algorithm. According to the results, the patterns {YarnElongationAt Break = (15.5-17.5]} and {YarnIrregularity = [7][8][9]} are the most frequent 1-itemsets, which indicates that they are the most influential parameters among the ones considered. Following these two parameters, the important attributes and their range values were determined as cf-items and mf-items and given as, for example, {YarnHairinessH = (3-4]} and {YarnCapillary = (3-4]} , respectively.…”

“…With significant innovations and improvements in the textile domain, it became necessary to analyze textile data, including material properties, machine settings, and process parameters. Thus, several studies on the prediction of yarn [8] and fabric [9] properties have been conducted by using traditional methods such as regression model [10], kernel estimation [11], and correlation analysis [12]. Although all of these traditional methods provide predictions about yarn and fabric properties and influences, they unfortunately remain incapable of analyzing complex relationships among data attributes, estimating unknown attribute values, or investigating hidden patterns among instances.…”

Control and optimization of human perception on virtual garment products by learning from experimental data

“…After this experience being repeated for a sufficient number of times in our daily contact, the memory association established between the tactile and visual perceptions of this fabric can be gradually strengthened. In this way, when we see or even just visualize a similar fabric, the related tactile information will be recalled from the memory association [20].…”

Matching Fabric Based on Wavelet Kernel Signature (WKS) and Drape Indicators

“…In one aspect, drape performance depends on the mechanical properties, structural parameters of fabric as well as the environmental factors [1]. In another aspect, drape performance is also an expression of the structural parameters and mechanical properties of fabrics [2]. That is, drape performance could be though as an implicit function of fabric parameters.…”