A novel method for weft and warp yarn segmentation in multicolour yarn‐dyed fabric images

This paper proposes a novel method for segmentation of weft and warp yarns in multicolour yarn‐dyed fabric images. A multicolour yarn‐dyed fabric is cross‐woven by weft and warp yarns with different colours. When a multispectral imaging system is used to measure the colour of a multicolour yarn‐dyed fabric image, its weft and warp yarns need to be detected before analysing their colours. Detection of interstices between weft and warp yarns is firstly conducted. A modified K‐means clustering approach is then utilised to separate weft and warp yarns. The number of clusters is fixed to 2. The metric to measure the distance between a pixel and the mean of a cluster is the CIELAB colour difference. The initial means are determined by the expected values of fitted Gaussian distributions to CIExyY colour histograms. Experimental results show that the proposed method is promising for the segmentation of weft and warp yarns in multicolour yarn‐dyed fabrics, with an improved segmentation accuracy and much faster processing speed than K‐means clustering in CIEXYZ and CIELAB spaces.     

An efficient method for solid‐colour and multicolour region segmentation in real yarn‐dyed fabric images

This paper presents an efficient approach to solid‐colour and multicolour region segmentation in real yarn‐dyed fabric images. The approach is based on a novel model describing the spectral response of a multispectral imaging system to yarn‐dyed fabrics. The model indicates that solid‐colour regions cannot be distinguished from multicolour regions in terms of reflectance, tristimulus, or CIELAB values owing to a geometric term representing the influence of fabric surface condition on measured colours. The geometric term makes it difficult to determine the segmentation thresholds of CIEXYZ and CIELAB colour histograms. However, solid‐colour and multicolour regions can be detected in CIExyY space because chromaticity coordinates are impervious to the geometric term. The CIExyY histograms of a solid‐colour region accord with one Gaussian distribution, but those of a multicolour region accord with a combination of two Gaussian distributions. The CIEXYZ, CIELAB, and CIExyY colour distributions of both solid‐colour and multicolour yarn‐dyed fabrics were analysed in detail in simulation and real experiments. Experimental results show that solid‐colour yarn‐dyed regions can be distinguished from multicolour yarn‐dyed fabric regions by the shapes of CIExyY histograms, but cannot be distinguished by the shapes of CIEXYZ or CIELAB histograms.     

Automatic detection of layout of color yarns of yarn‐dyed fabric. Part 3: Double‐system‐Mélange color fabrics

Automat layout detection of color yarns is necessary for weaving and producing processes of yarn‐dyed fabrics. This study presents a novel approach to inspect the layout of color yarns of double‐system‐mélange color fabrics automatically, which is Part III of the series of studies to develop a computer vision‐based system for automatic inspection of color yarn layout for yarn‐dyed fabrics. The inspection of single‐system‐mélange color fabrics has been realized in Part I of the series of studies. Integrating the projection‐based region segmentation method proposed in Part I and the FCM‐based stepwise classification method proposed in Part II, the proposed approach is composed of three steps: (1) fabric region segmentation, (2) fabric region selection, and (3) layout of color yarns recognition. In the first step, the fabric regions are segmented by the projection‐based region segmentation method. In the second step, the reasonable fabric regions are selected by analyzing their color histograms and comparing their weft color's frequency. In the third step, the layout of color yarn is recognized by the FCM‐based stepwise classification method, and the precise layouts of color warps and wefts are produced. The experimental analysis proved that the proposed method can recognize the layout of color yarns of double‐system‐mélange color fabrics correctly by testing four different color fabrics and three pieces of same yarn‐dyed fabrics. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 250–260, 2017     

Color pattern recognition for yarn‐dyed fabrics

In the process of designing and analyzing the yarn‐dyed fabric, the yarn color pattern has an important effect on the appearance of the fabric. An automatic color pattern recognition method for yarn‐dyed fabric is proposed in this study. The proposed method uses the fabric images obtained from a high‐resolution digital camera image acquisition system. The local statistical texture features are used for yarn texture segmentation. The yarn color classification problem is then formulated in a research framework of multiregion fuzzy segmentation, which can be added auxiliary variables and solved efficiently by the fast dual projection algorithm. The color values of the yarn crossing points are calculated by the yarn color classification results. The locations of the yarn crossing points are detected by a lightness gradient projection method. Different kinds of fabrics are tested in the experiments. Experiments on 14 actual fabrics show that the approach proposed in this study is effective for classifying yarn color and extracting the yarn color pattern in yarn‐dyed fabric.     

A multispectral imaging approach to colour measurement and colour matching of single yarns without winding

This paper investigates a multispectral imaging approach to colour measurement and colour matching of single yarns. The small size of a single yarn makes it impossible for spectrophotometers directly to acquire its spectral reflectance. Multispectral imaging systems, on the other hand, have the potential to measure the reflectance of single yarns as they can record both the spectral and the spatial information of a sample. A multispectral imaging system, namely imaging colour measurement, has been developed to conduct colour measurement of single yarns. A single yarn is first detected from backgrounds by a modified K‐means clustering method. The reflectance of the single yarn is then specified by an averaging method. Comparative experiments based on 100 pairs of single yarns and corresponding yarn windings show that the reflectance magnitude of a single yarn acquired by imaging colour measurement is smaller than that of corresponding yarn winding measured by a Datacolor 650 spectrophotometer. Experiments on 16 single yarns show that the repeatability and spatial reproducibility of the imaging colour measurement system in measuring a single yarn colour are 0.1185 and 0.2827 CMC(2:1) units. A colour matching comparison experiment (pass or fail), using 24 pairs of single yarns and corresponding pairs of solid‐colour yarn dyed fabrics, shows that single yarns measured by imaging colour measurement can achieve similar colour matching results to solid‐colour yarn dyed fabrics measured by the Datacolor 650 spectrophotometer, with degrees of similarity of 87.5 and 83.3% when the CMC(2:1) and CIE2000(2:1:1) colour difference formulas are employed.     

Comparative study of colour yield of cotton knitted fabric made by torque‐free ring‐spun yarns

Torque‐free ring spinning is a new spinning technology that has produced yarns with low twist and balanced torque. In this study, a commercially torque‐free ring‐spun yarn, namely Estex yarn, with three types of cotton fibre, i.e. Pima, upland and organic cotton, were used. Cotton fabric samples were knitted with Estex yarns and conventional ring‐spun yarns. The fabric samples were then dyed with two reactive dyes, Remazol Black B and Remazol Brilliant Blue R Spec., and the fabric dyeability was measured in terms of reflectance and colour yield. Finally, the results were analysed using the statistical software package SPSS and the results revealed that fabric samples manufactured by Estex yarns could achieve a better colour yield than conventional ring‐spun fabric samples. In addition, the Pima cotton gave the best colour yield, followed by upland cotton and organic cotton.     

Automatic detection of layout of color yarns of yarn‐dyed fabric. Part 1: Single‐system‐mélange color fabrics

To recognize the layout of color yarns of single‐system‐mélange color fabric automatically, a novel FCM‐based stepwise classification method is proposed in this article. This method consists of three main steps: (1) warp yarn segmentation, (2) weft color recognition, and (3) the layout of color warps recognition. In the first step, the yarn segmentation method based on mathematical statistics of subimages is adopted to localize warp yarns preliminarily; and then the segmentation results of warp yarn are corrected by misrecognized‐boundary remove and missing‐boundary interpolation. In the second step, the weft color is extracted based on RGB color histograms of whole fabric image. In the third step, the pixels in each warp yarn are classified into two clusters by fuzzy C‐means clustering (FCM) algorithm in CIELAB color model separately, and the preliminary recognized layout of color warps is obtained. All warp colors are clustered by FCM algorithm in CIELAB color model again and the precise layout of color warps is output. The experimental and theoretical analysis proved that the proposed method can recognize the layout of color yarns of single‐system‐ mélange color fabrics with satisfactory accuracy and good robustness. © 2015 Wiley Periodicals, Inc. Col Res Appl, 40, 626–636, 2015     

Measuring linear density of threads in single‐system‐mélange color fabrics with FCM algorithm

According to the color yarns in the fabric, the fabrics can be divided into three categories: solid color fabrics, single‐system‐mélange color fabrics, and double‐system‐mélange color fabrics. The density of solid fabrics can be inspected with gray‐projection method or Fourier analysis method. But the methods cannot be applied to yarn‐dyed fabrics directly. A method for detecting the density of single‐system‐mélange color fabrics will be discussed in this article. By analyzing the pattern and color characters of single‐system‐mélange color fabrics, fuzzy C‐means algorithm is proposed to classify the colors in the fabric image based on CIELAB color space first. With the color segmentation results, the fabric can be divided into different blocks. The yarns can be located in different blocks with different average gray‐levels, and then the number of yarns can be counted in each block. The linear density of threads can be obtained by counting the yarns in a unit length finally. The experiment proved that the algorithm proposed in this study can inspect the density of single‐system‐mélange color fabric successfully. © 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 456–462, 2013     

Automatic detection of layout of color yarns of yarn‐dyed fabric. Part 2: Region segmentation of double‐system‐Mélange color fabric

To detect the layout of color yarns automatically, a novel projection‐based fabric segmentation method is proposed to segment the double‐system‐mélange color fabric into several regions, which can be seen as single‐system‐mélange color fabrics. This method consists of five main steps: (1) yarn skew detection, (2) fabric image projecting, (3) projection curve smoothing, (4) variance curve calculating, and (5) curve peak confirmation. Based on the acquisition fabric image, the skew angles of warp and weft yarns are detected by Hough transform first. The projection curves of L, a, and b channels in Lab color model are generated and smoothed by Savitzky–Golay filter. The variance curves of L, a, and b are then calculated, and the peaks corresponding to the regional boundaries in each curve are detected. The regional boundaries are confirmed by synthesizing the curve peaks of L, a, and b. The experimental and theoretical analysis proves that the proposed method can segment the double‐system‐mélange color fabric into regions with satisfactory accuracy and good robustness. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 626–635, 2016     

Recoloring textile fabric images based on improved fuzzy clustering

This article proposes a new recoloring method for textile fabric images based on improved fuzzy local information c‐means (FLICM) clustering. In the clustering algorithm, the fuzzy factor was modified so that it can produce reliable segmentation in areas with rich details. With the obtained cluster labels and pixel‐wise memberships, the color of each pixel is modeled as the linear combination of the two most dominant colors. The recoloring process was then conducted by replacing the specified dominant color with user‐provided target colors. Experimental results showed that the proposed method can produce natural and faithful color appearance on both printed and yarn‐dyed fabric images, and outperforms the state‐of‐the‐art. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 115–123, 2017     

Determining the dependence of colour values on yarn structure

The colour values of knitted cotton fabrics made from single and plied ring and compact yarns were investigated before and after dyeing. The fabric samples were knitted under the same constructional properties and then dyed with direct and reactive dyes. It was found that fabrics with ring yarns had high lightness and low chroma and colour strength values compared with fabrics with compact yarns. Also colour strength and colour difference values of dyed fabrics were assessed after increasing abrasion cycles (2500, 5000, 7500 and 10 000). The main changes in colour strength values were observed at 2500 abrasion cycles. The effect of abrasion on colour difference values of fabrics having ring yarns was more obvious than fabrics having compact yarns.     

Improving the colour fastness of dyed nylon‐6 fabric by graft copolymerisation and curing of acrylic acid

The influence of grafting and grafting–curing of acrylic acid on the colour fastness of nylon‐6 fabric dyed with an acid dye of low wash fastness was investigated. The variables involved in grafting were initially optimised for pristine nylon‐6 fabric prior to grafting the same monomer onto the dyed fabrics. The highest graft yield achieved for the pristine and dyed nylon‐6 fabrics was 44 and 14% respectively. Grey scale testing and colorimetric analysis revealed that the highest colour fastness and the smallest drop in colour strength belonged to the dyed–grafted–cured nylon‐6 fabric. The colour components were measured, and the total colour difference of each sample after five washing cycles was computed. The specific colour difference showed that the implementation of either grafting or grafting–curing processes will alter the reference colour of the dyed fabric. The tensile strength of the grafted and grafted–cured fabrics was respectively 2.7 and 6.3% lower than that of dyed nylon‐6.     

Optimal yarn colour combination for full‐colour fabric design and mixed‐colour chromaticity coordinates based on CIE chromaticity diagram analysis

It is challenging for textile designers to achieve full‐colour effects in woven fabric using a limited set of coloured yarns. The common problems encountered during full‐colour fabric design include an insufficient number of colours and a failure to match the fabric colour with the desired colour. Using the theories of primary colours and optical colour mixing, we examine the mixed‐colour distribution of primary colour yarns on the basis of the CIE 1976 chromaticity diagram (CIE u′v′). In our experiment, dope‐dyed polyester filament yarns were selected as raw materials. Eight kinds of gradually varied weave structures and four types of primary colour combination were adopted in order to make different types of full‐colour fabric colour chart. Spectrophotometer and DigiEye colour measurement systems were selected to measure the reflectance and colour value of the fabric samples. By comparing the colour distribution of mixed fabrics in the CIE u′v′ diagram, the relationship between the primary colour combinations and the colour distribution of mixed fabrics is discussed. Of RGB, CMY, NCS, and RGBCMY combinations, only RGBCMY resulted in a relatively complete and large colour gamut. Moreover, the colour positions of mixed fabrics in the CIE u′v′ diagram were almost all distributed on or near the connecting line of the primary colour coordinates. The specific colour position of mixed fabrics in the CIE u′v′ diagram were mainly determined by the proportion of primary colours on the fabric surface. In this way, a new method for computing colour position in the CIE u′v′ diagram is introduced.     

Use of a predictive colour model for managing the colour appearance of two‐colour woven fabrics

The aim of this study was to implement a two‐dimensional colour appearance model for prediction of the colour values of weft threads when the optical mixing of a two‐colour woven structure had to match the colour appearance of a single‐colour reference woven fabric. Five single‐colour woven fabrics were woven from five threads of similar hue. One of the samples was chosen as a reference, for which the colour appearance was the goal to be achieved in the two‐colour woven fabrics prepared with the other available warp threads and newly dyed weft threads. The colour values of dyed weft threads were predicted by a two‐dimensional colour appearance model. With dyed weft threads, managing the colour appearance of the two‐colour woven fabric was enabled to achieve the colour values of the reference. In the results, colour deviations between the predicted and measured colour values of weft threads revealed some limitations to the colour appearance model and performance of the dyeing process. After the production of the two‐colour woven fabric, the colour appearance matched the appearance of the reference, resulting in deviations of ΔE_CMC(2:1) = 1.2‐7.8. Moreover, the differences between theoretically predicted and measured colour values of the two‐colour woven fabric were evaluated as small, ranging from ΔE_CMC(2:1) = 1.5‐1.9. The results demonstrated the efficiency of implementing the colour appearance model and the dyeing process of weft threads as an approach to achieve the defined colour appearance of two‐colour woven fabrics, which with small colour deviations matches the colour of a single‐colour reference.     

Graphene oxide as dyestuffs for the creation of electrically conductive fabrics

Graphene oxide (GO) was immobilized on the surfaces of acrylic yarns through a conventional dyeing approach. The GO dyed yarns and/or the fabric were immersed in an aqueous sodium hydrosulfite solution at around 363 K for 30 min, which converted the GO into graphene. The graphene created a graphitic-coloured and electrically conductive thin layer over each yarn in the fabric. Data on the electrical conductance of the yarns versus temperature (30-300 K) fit well with the so-called fluctuation-induced tunneling model, which suggests that the graphene layer belongs to a continuously interconnected network. Values of the electrical resistivity ranged from 10² to 10¹⁰ Ohm/cm, as verified by the content of graphene in the conductive layer.     

Pattern‐driven color pattern recognition for printed fabric motif design

Pattern‐driven design method is an important data‐driven design method for printed fabric motif design in textiles and clothing industry. We introduce a novel framework for automatic design of color patterns in real‐world fabric motif images. The novelty of our work is to formulate the recognition of an underlying color pattern element as a spatial, multi‐target tracking, classification, segmentation and similarity association process using a new and efficient color feature encoding method. The proposed design method is based on pattern‐driven color pattern recognition and indexing from the element image database. A series of color pattern recognition algorithms are used for color and pattern feature extraction. The local statistical corner features and Markov random field model are used for motif unit tiling detection and conversion. The color feature encoding problem is modeled in a gray‐scale color difference optimization problem, which can be solved quickly by existing algorithms. Color pattern feature matching, segmentation and indexing techniques are then used to locate and replace the elements in the motif unit image with similar elements in the database. Experiments show that the approach proposed in this study is effective for color pattern recognition and printed fabric motif design.     

The quasi‐yarn‐dyed effect: triple dyeing of woven polyester/cationic dyeable polyester/viscose rayon blend fabrics by chemical treatments in the laboratory and on a pilot and an industrial scale

The aim of this study was to carry out triple dyeing of woven polyester/cationic dyeable polyester/viscose rayon blend fabrics with the required colour fastness and physical properties. The feel and final appearance of the fabric were achieved by partial removal of the viscose rayon moiety of the fabric through optimised causticisation treatments. The results of the triple dyeings obtained from laboratory and small‐scale experiments were successfully scaled up in authentic processing equipment. The final product, which has a yarn‐dyed effect, readily satisfied the requirements related to the colour fastness and physical properties.     

Assessing colour values of polyester fabrics produced from fibres having different cross-sectional shapes after abrasion

This study assesses the colour, colour difference and colour strength values obtained for eight disperse-dyed polyester fabric samples produced from full and hollow round and trilobal fibres after four sets of abrasion cycle. Each fabric was produced from the same yarns in warp and weft directions. The L *, C *ΔE^*_ab and K/S values of the undyed fabrics and dyed fabrics before and after increasing cycles of abrasion are presented and discussed. Fabrics produced from full and hollow fibres showed great differences regarding colour values in abrasion behaviour. Fabrics produced from hollow fibres were found to be very sensitive to increasing abrasion cycles.     

Fading of Cotton Yarn Colored with C. I. Vat Blue I (Indigo Dye) via Ozone Application

In this experimental study, the possibility of obtaining of fading effect to indigo dyed denim yarns via ozonation process was searched. Therefore, a novel approach was attempted for fading the denim materials in the form of yarn before weaving or garment processes. The effect of the ozone gas on the CIELab values of indigo dyed yarn specimens was statistically investigated depending on some physical properties of the yarns and application parameters of ozone gas. In addition, the effect of ozone gas on the strength performance of the yarns was investigated. The results showed that different yarn and ozone application parameters affected the fading results. Besides, the ozonation process did not have a crucial negative effect on yarn strength.     

Analysis of spinning process parameters on development of spun‐dyed PET yarn using the Taguchi method

The yarn tenacity of a spun‐dyed yarn is predicted from the spinning conditions and other properties of the yarn are analyzed through the defined parameters using the Taguchi method. To develop a spun‐dyed yarn using the Taguchi method, four factors that can largely influence the yarn properties are selected. From the experimental design based on four factors, the processes are executed to produce the specific yarns whose properties are measured to analyze the relationship between the process conditions and their results. The target properties of a spun‐dyed yarn may be obtained through adjusting the spinning parameters that are related to the yarn properties by the Taguchi tool. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 102:1419–1427, 2006