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     

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     

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     

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.     

Establishment of a color tolerance for yarn-dyed fabrics from different color-depth yarns

Yarn-dyed fabric is often woven from warp and weft yarns in the same color depth to ensure a uniform color appearance. The difference in color depth between warp and weft tends to result in the uneven color of the yarn-dyed fabric. This article aims to establish a color tolerance for yarn-dyed fabric that can be woven with a qualified color appearance but from the warp and weft yarns in different color depths. A total of 27 yarn-dyed fabric samples in three color series (red, yellow, and blue) were evaluated by using the yarn-dyed fabric from warp and weft yarns in the same color depth of 2% (on weight of fabric, owf) as the standard. Visual assessment and instrumental measurement of color were carried out to establish the color tolerance ellipse that was defined as CMC (Color Measurement Committee) color differences (2:1) of no more than 1.00. It was found that the color strengths (K/S) and color differences (ΔE_CMC(2:1)) of these fabric samples for each color series had linear relationships with the color depths of warp and weft yarns. The color tolerance ellipses indicated that, even though the warp and weft yarns had an apparent color difference, they could be woven in fabrics with relatively uniform color appearance and meet the requirements for yarn-dyed fabric. This work provided valuable insight into the production of qualified yarn-dyed fabrics from unqualified dyed yarns.     

K‐M theory of fabric knitted by three‐channel rotor spun wool yarn

Two‐component and three‐component color blended yarns were spun by red, yellow, and blue wool slivers using a three‐channel rotor spun machine, and the corresponding plain fabrics were knitted. The color‐matching models of K‐M theory were built with the relative method and the least squares method, respectively. Colors and blending ratios of the fabrics were predicted by the model. The results showed that the average color differences of the samples predicted by the two methods are both about 1.0 and the mean value of the proportional error is below 3%. The least squares method has a better color‐matching effect for the three‐component sample, and the relative value method has better color‐matching results for the two‐component sample. When the tolerance range is 2.0, the pass rates of the samples predicted by either the relative value method or the least squares method reach 100%.     

Research on the detection of fabric color difference based on T‐S fuzzy neural network

T‐S fuzzy neural network algorithm is used to establish the mapping relationship from the RGB space to the L*a*b* space, which avoids the complex process of color space conversion. Meanwhile, the block method is adopted to detect color difference of dyed fabric that is wide format and wide viewing angle. Color differences in different regions can be calculated with Color Measurement Committee color difference formula based on T‐S fuzzy neural network. Experimental results are in accordance with the spectrophotometer measurement, which proves that T‐S fuzzy neural network algorithm used in real‐time color detection process is effective and feasible. Workers can make corresponding adjustment on‐line according to the deviation to ensure the quality of fabric color and reduce the loss.     

Towards automation of color/weave selection in Jacquard design: Model verification

Jacquard woven fabrics are made from colored yarns and different weaves for designing complex pictorial and other patterning effects. The final visualized color effect is the result of assigning weave designs to different areas of the pattern to be created. The current practice in creating Jacquard woven fabric designs is to produce many samples in a trial‐and‐error attempt to match artwork colors. An ability to simulate accurately the appearance of a design prior to manufacture is highly desirable to reduce trial‐and‐error sample production. No automated accurate digital color methodology is yet available to assist designers in matching the patterned woven fabric to the desired artwork. To achieve this, we developed a geometrical model to predict the color contribution of each yarn on the face of the fabric. The geometrical model combined with a Kubelka‐Munk based color mixing model allowed the prediction of the reflectance properties of the final color for a given design. We compared the predicted and experimental values of the reflectance properties for a range of fabrics using the same geometric model with three separate color mixing models. The geometrical model combined with a log‐based color mixing model produced reasonable agreement between predicted and measured ΔE_ab, with an average ΔE_ab of approximately five. © 2009 Wiley Periodicals, Inc. Col Res Appl, 34, 225–232, 2009     

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.     

Use of gray system theory in product‐color planning

A quantitative evaluation method for the CIE color‐planning activity within the product design cycle is proposed in this article. The questionnaire‐based process that is traditionally employed to obtain objective color psychology tends to be time‐consuming. Accordingly, this study proposes the use of gray system theory to overcome this problem. In the CIE color system, colors are defined by three primary colors, R (red), G (green), and B (blue). Using these three principal hues with fixed equigap sequences to simulate specific basic color samples is an efficient means of investigating unicolor images on a personal computer. However, a gray relational generating operation can be used to simulate colors beyond these basic samples and to predict the corresponding membership values for semantic words. In addition, the gray clustering operation is introduced to predict the overall color image evaluation of multicolored products. The predicted evaluation results of the gray system theory and a back‐propagation neural network are both compared with experimentally verified results. The results indicate that the gray forecasting model is the more effective means of predicting the image evaluation, and therefore, the method is adopted within the color‐planning activity. Although this study takes the example of the Internet‐aided color planning of a baby walker as a case study, the proposed method can also be used on other products. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 222–231, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20009     

Color segmentation in multicolor images using node‐growing self‐organizing map

This article presents a clustering algorithm based on node‐growing self‐organizing map (NGSOM) to classify colors on color images automatically and accurately partition the regions of different colors for color measurement. Based on the CIEDE2000 criterion, pixels in a multicolor image are grouped into a number of visually distinguishable color regions in which pixel distribution information is provided as the input of the NGSOM network for further segmentation. As an unsupervised clustering algorithm, the NGSOM randomly selects two initial nodes from the input source without a predefined network structure and grows nodes according to color differences between the first best matching unit (FBMU) and the second best matching unit (SBMU) of the current input data. Unlike a traditional SOM, the NGSOM utilizes learning rates varying with the pixel distributions of major color regions. The node‐growing procedure is terminated when all the input data are examined. Compared with some commonly used color clustering algorithms, the proposed algorithm possesses a better peak signal‐to‐noise ratio (PSNR) and higher time efficiency. The NGSOM can be used for a wide range of applications, including fabric colorfastness assessment, painting conservation, and scenic identification in aerial images.     

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.     

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.     

Three‐dimensional color prediction modeling of single‐ and double‐layered woven fabrics

In this research, the three‐dimensional structural and colorimetric modeling of three‐dimensional woven fabrics was conducted for accurate color predictions. One‐hundred forty single‐ and double‐layered woven samples in a wide range of colors were produced. With the consideration of their three‐dimensional structural parameters, three‐dimensional color prediction models, K/S‐, R‐, and L*a*b*‐based models, were developed through the optimization of previous two‐dimensional models which have been reported to be the three most accurate models for single‐layered woven structures. The accuracy of the new three‐dimensional models was evaluated by calculating the color differences ΔL*, ΔC*, Δh°, and ΔE_CMC(2:1) between the measured and the predicted colors of the samples, and then the error values were compared to those of the two‐dimensional models. As a result, there has been an overall improvement in color predictions of all models with a decrease in ΔE_CMC(2:1) from 10.30 to 5.25 units on average after the three‐dimensional modeling.     

Cationization of cellulose/polyamide on UV protection,bio‐activity,and electro‐conductivity of graphene oxide‐treated fabric

In this work, cationized cotton/nylon fabric was treated with reduced graphene oxide (rGO) to produce highly conductive fabric. The fabric was cationized with 3‐chloro‐2‐hydroxy propyl trimethyl ammonium chloride to attract more anionic GO. The fabric was then treated with GO followed by reduction with sodium dithionite. The results of energy‐dispersive X‐ray spectroscopy, X‐ray diffraction, and X‐ray photoelectron spectroscopy indicated entire coverage of the fabric surface with rGO. The color of fabrics changed to gray‐black and the electrical resistance decreased to 0.6 × 103 Ω sq^?1. The washing fastness was measured according to ISO 105‐CO5 for color change and also electrical resistance of the samples demonstrated well stability of rGO on the fabric surface. The antibacterial activities of the treated fabrics improved against Gram‐negative bacteria including Escherichia coli (84.8%) and Pseudomonas aeruginosa (96.4%) and also Gram‐positive bacteria consisting Staphylococcus aureus (100%) and Enterococcus faecalis (98.4%). Further, the treated fabrics indicated an excellent UV reflectance of 100%. Finally heating of the cationized rGO fabric at 220 °C displayed a lower electrical resistance of 0.5 × 103 Ω sq^?1. The thermogravimetric analysis showed that heating has a slight effect on the dimensional thermal stability of the treated fabric as shrunk 2.43%. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134 , 45493.     

Studies on acrylic acid–grafted polyester fabrics by electron beam preirradiation method. II. Novel intelligent immersion‐resistant and moisture‐permeable fabrics

Moisture regain, water vapor permeability, and water‐impermeable ability of polyester fabrics before and after grafting by acrylic acid (AA) were investigated. The results showed that the AA‐grafted fabric presents novel properties. When it is dry it possesses a higher water vapor transmission rate. Once it comes into contact with water, the grafted poly(acrylic acid) layer on the fibers' surface begins to swell and seals the intervals between fibers and yarns to prevent water from penetrating, a phenomenon known as immersion resistance. This kind of immersion‐resistant and moisture‐permeable fabrics might be a suitable candidate for the immersion‐resistant layer fabrics of Type B immersion‐resistant suits. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 3939–3943, 2003     

Temperature‐sensitive poly(N‐tert‐butylacrylamide‐co‐acrylamide) hydrogels bonded on cotton fabrics by coating technique

Different from the conventional method of developing stimuli‐sensitive textiles by graft copolymerization of environmental responsive polymers onto the fabric, the coating technique was applied to bond temperature‐sensitive hydrogels with cotton fabric through chemical covalent in our work. A temperature‐sensitive linear copolymer of N‐tert‐butylacrylamide (NTBA) and acrylamide (AAm) was prepared in methanol. Then, the cotton fabrics were coated using an aqueous solution of this copolymer containing 1,2,3,4‐butanetertracarboxylic acid as a crosslinker and sodium hypophosphite (SHP) as a catalyst, followed by drying and curing. The surface of the cotton fabrics was bonded on more or less coatings of poly (NTBA‐co‐AAm) hydrogels, as verified by Fourier transform infrared spectroscopy and scanning electron microscopy images. The poly(NTBA‐co‐AAm) hydrogels‐coated fabrics exhibited temperature sensitive, and the temperature interval of the deswelling transition was higher than lower critical solution temperature of linear copolymer solution. The coated fabrics presented good water‐impermeable ability because of the swelling of hydrogels bonded, especially when the add‐on was as high as 14.14%. Environmental scanning electron microscopy images revealed that coating hydrogels swelled and covered on the surface as a barrier to prevent water from penetrating once the coated fabric came into contact with water. The findings demonstrate that the temperature‐sensitive hydrogels can be covalently bonded on the cotton fabrics by coating technique and the coated fabrics have potential on immersion fabrics. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009     

用于轮胎帘布层的芳纶帘子布的开发

采用芳纶1414纱线和聚酰胺66包芯纱织造芳纶帘子布,通过调整帘子布的密度,设计出了4种不同紧度的帘子布,并对织造过程中遇到的影响帘子布质量的相关问题进行分析,提出了相应的解决方案。同时,对帘子布进行力学及阻燃性能测试分析,结果表明:所设计的帘子布的抗冲击性和阻燃性能良好,可较好地应用于轮胎帘布层。     

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.     

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.