Fabric defect segmentation by bidimensional empirical mode ...

Original article

Fabric defect segmentation bybidimensional empirical modedecomposition

Zhengxin Li, Jianli Liu, Jihong Liu and Weidong Gao

Abstract

Fabric defect detection has attracted increasing attention in the fields of computer vision and textile engineering because

it is essential to quality assurance of textile manufacturing. In this paper, we propose a novel defect detection scheme for

fabric inspection based on bidimensional empirical mode decomposition. The stopping criterion for sifting and the

intrinsic mode functions (IMFs) are adapted for this specific application. Appropriate IMFs are selected to eliminate

influences of fabric textures and lighting in defect segmentation. The experiment results on sample images from our

laboratory and from TILDA’s Textile Texture Database demonstrate that the proposed method is a robust and accurate

approach for fabric defect inspection.

Keywords

Defect detection, bidimensional empirical mode decomposition, intrinsic mode functions

A defect is a flaw that occurs on the fabric surfaceduring manufacturing, and is possibly caused by adefective yarns, machine failure and/or manual oper-ational error. Fabric defects undermine product qualityand could decrease the price of fabrics by 45–65%.1

Given that defects inevitably occur, defect inspectionis critical to quality assurance in fabric production.2

Fabric inspection is traditionally carried out by textileexperts, who conduct a completely subjective evalu-ation of fabric products. However, an expert candetect no more than 60% of typical defects on afabric wider than 2m or when its moving speed isfaster than 30m/min.3 These problems prompted thedevelopment of automatic inspection systems basedon computer vision. During the past two decades, vari-ous techniques of automatic fabric defect detectionhave been developed in different approaches, includingstatistical, spectral and model-based ones.4–7

In the statistical approach, the important assump-tions are that the statistics of defect-free regions arestationary, and that these regions extend over a signifi-cant portion of inspected images. Unser and Ade8 con-sidered the Karhunen–Loeve transform as the optimumtool for extracting texture features in the inspection oftextured materials. Zhang and Bresee9 combined an

autocorrelation function and morphological operationsto identify repeating structural units in fabric. Tsaiet al.10 calculated co-occurrence-based features totrain and test a neural network for classifying thefour types of defects. Chetverikov and Hanbury11 cal-culated the regularity and local orientation of texturesto identify the defects in regular and flow-like patterns.Kumar12 extracted a feature vector from each pixel andused a feedforward neural network to detect commondefects, such as slack ends and mispicks. Shi et al.13

defined local contrast deviations to describe contrastdifferences in four directions between analyzed imagesand a defect-free image of the same fabric. Fractalmethods were also applied to model the statistical qua-lities like roughness and self-similarity on fabricsurface.14

Key laboratory of ECO-Textiles, Jiangnan University, China

Corresponding author:

Weidong Gao, Key laboratory of ECO-Textiles, Jiangnan University,

Wuxi, Jiangsu 214122, China.

Email: [email protected]

Textile Research Journal

2014, Vol. 84(7) 704–713

! The Author(s) 2014

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Fabric images can also be transformed into the fre-quency domain for defect detection because of the highdegree of periodicity of textile materials. Tsai andHsieh15 took a one-dimensional Hough transform inthe Fourier domain of texture material images to iden-tify high-energy frequency components (i.e., defectivecomponents). The Fourier transform (FT) is insensitiveto the minor modifications to the frequency spectrumcaused by local fabric defects. To overcome this draw-back, researchers used other approaches, such as Gaborfilters and wavelet transform (WT). Jain andFarrokhnia16 used the Gabor transform to segmentand classify textures with dyadic coverage of theradial spatial frequency range. In the work of Maket al.,17 a finite set of multi-level Gabor wavelets wastuned to match a defect-free fabric image. The WTenables the decomposition of an image into a hierarchyof localized sub-images at different spatial frequencies.Sari-Sarraf and Goddard18 developed a fabric defectdetection system by using the low-pass and high-passDaubechies filters, which can detect small defects at adetection rate of 89%. Kumar and Gupta19 used themean and variance of the Haar wavelet coefficients toidentify surface defects. Chen and Xu20 employed amethod based on a compactly supported biorthogonalWT to inspect fabric defects, including warp lacking,weft lacking, oil stains and holes. Spectral approachesare more advantageous in terms of computational effi-ciency, but less robust in dealing with random textureimages, which cannot be described in terms of primi-tives and displacement rules.

Model-based approaches are particularly suitablefor detecting fabric images with stochastic surface vari-ations or random textures. Cohen et al.21 treated textileweb inspection as a hypothesis-testing problem andimplemented the inspection by characterizing fabrictextures with the Gauss–Markov random field model.Brzakovic et al.22 investigated a theoretical approach,in which a Poisson model for inspecting web materialswas used. Campbell et al.23 designed a Gaussiancloud model superimposed on Poisson clutter todetect production defects with linear patterns.

As summarized in the references, each of the afore-mentioned defect detection methods has limitations.There are still industrial needs to develop efficient androbust algorithms for online fabric inspection. In thepast few years, we developed a prototype machine using4 k-line-scan cameras and linear light-emitting diode(LED) lights for fabric detect inspection.24 In thispaper, we tend to focus on implementation of a novelfabric defect detection method that is based on bidi-mensional empirical mode decomposition (BEMD).A fabric image can be essentially considered as a two-dimensional (2D) oscillating signal that contains fabrictextures, defects and inhomogeneous illumination.

We can use BEMD to separate these componentsinto sub-images, and obtain a sub-image that con-tains only defect information, and therefore possibleinterferences from fabric textures and lighting can beexcluded.

Methodology

Bidimensional empirical mode decomposition

BEMD is a 2D expansion of empirical mode decom-position (EMD), which was originally developed byHuang et al.25 for analysis of non-linear and non-sta-tionary signals. BEMD can decompose a datasetaccording to its spatial instantaneous frequency with-out the need of any base function like sinusoids in FTor wavelets in WT. As a totally data-driven tool, it hasbeen successfully applied in many fields, includingimage segmentation,26,27 image fusion,28 edge detec-tion,29 noise reduction,30 texture synthesis,31 imagecompression32 and image watermarking.33,34 Its multi-scale decomposition ability provides the possibility ofbeing applied to fabric defect detection.

BEMD decomposes a 2D signal based on the localcharacteristic spatial scale, namely local instantaneousfrequency. The generated oscillating signals representthe oscillation modes imbedded in the data, and theyare called intrinsic mode functions (IMFs). An IMF is afunction whose number of extrema and zero crossingsmust either be equal or differ by one, and the meanvalue of the envelope defined by the local maxima(upper envelope) and the envelope defined by thelocal minima (lower envelope) should be zero at anypoint.25

Given an image I, the process of generating an IMFis called sifting. The sifting algorithm is iterative with akth iteration detailed as follows:26

1. identify the extrema of the result of the k-1th iter-ation hk-1 (k� 1, h0 is the original image I) and gettwo sets of maxima and minima, respectively;

2. create an upper envelope from the maxima set and alower envelope from the minima set by non-equidi-stant interpolation;

3. calculate the local mean m by averaging the upperenvelope and lower envelope;

4. subtract out the mean from hk-1: hk-1 – m¼ hk;5. repeat as hk is an IMF.

The iteration of sifting should be applied with care,for carrying the process to an extreme could make theresulting IMF a pure frequency modulated signal ofconstant amplitude.25 The sifting process is usuallystopped by limiting the size of the standard deviation

Li et al. 705



SD, computed from the two consecutive siftingresults as

SD ¼X hk�1 � hkð Þ

2

h2k�1

� �ð1Þ

An IMF c1 is generated when the conditionSD�SDmax is met. The selection of SDmax directlyaffects the calculation time and the information validityof IMFs. The optimal value of SDmax will be discussedin the section Stopping criterion.

The first IMF c1 contains the finest scale componentof the image I. Separating c1 from the rest of thedata by

I� c1 ¼ r1 ð2Þ

a corresponding residue r1 is obtained. Continuously,an IMF cn can be separated from residue rn-1 using asifting process, with rn-1 as input

rn�1 � cn ¼ rn n � 1, r0 ¼ Ið Þ ð3Þ

The sifting process will finally end when rn becomes amonotonic function from which no more IMFs can beextracted. Actually, this can be time-consuming andunsuitable for industrial applications. Thus, the siftingprocess can be terminated manually based on a reason-able observation, and we obtain

I ¼ rn þXni¼1

ci ð4Þ

Delaunay triangulation-basedinterpolation

Creating the envelope surface from a 2D extrema set isa problem of non-equidistant interpolation on scattereddata points. Delaunay triangulation (DT)-based inter-polation is a finite element method (FEM) for 2D enve-lope calculation. A DT for a set P of points in a plane isdenoted as DT(P) in which no point in P is inside thecircumcircle of any triangle.35 It provides a triangulargrid for scattered extreme points, and then the interpol-ation can be piecewise implemented with the vertices oftriangles. Figure 1(a) shows a synthetic fabric image(128� 128 pixels). Its maximum points are shown inFigure 1(b). Figures 1(c) and 1(d) are the triangulargrid generated by DT and the upper envelope.

IMF selection

To demonstrate the multi-scale representation of IMFs,we analyze a defect-free and a defective synthetic fabric

image (512� 512 pixels) for a comparison. The imagespresent a typical woven structure shaped by simulatedyarns on the weft and warp directions, a commondefect mode and an inhomogeneous illumination. Inthe analysis, the images are decomposed into threeIMFs and a residue. Given that some points inthe IMFs are negative, a map function (Equation (5))is employed to visualize the data generated fromBEMD:

f i, jð Þ ¼c i, jð Þ � cmin½ � � 255

cmax � cminð5Þ

where f(i,j) and c(i,j) denote the values at the coordinate(i,j) of the mapped image and IMF, respectively, andcmax and cmin are the maximum and minimum of IMF1,respectively.

As shown in Table 1, the three IMFs represent fine-to-coarse pattern structures. IMF1, IMF2 and IMF3denote the small-, medium- and large-scale compo-nents of the fabric, respectively. IMF1 contains thehighest frequency component that represents themain part of the fabric texture. The IMF2 andIMF3 in the defect-free column have rather loweramplitudes than IMF1 because the original image con-tains little information in medium and large scales.Oppositely, the IMF2 and IMF3 in the defectivecolumn clearly contain a defect profile, as indicatedby the arrows. The residue contains the tendency com-ponent produced by inhomogeneous illumination,which can often be modeled as a low-order polyno-mial without oscillation. Therefore, if we construct asub-image by fusing IMF2 and IMF3, we can obtain asub-image containing possible defect information butexcluding inhomogeneous illumination and the mainfabric texture at the same time.

IMF fusion and segmentation

Since defect information is spread in IMF2 and IMF3,a defect sub-image can be constructed by fusing IMF2and IMF3:

Isub i, jð Þ ¼ c2 i, jð Þ þ c3ði, j Þ ð6Þ

The constructed sub-images of the two syntheticfabric samples are shown in Figure 2. They are visua-lized in a grayscale range of [0, 255] because of the 0mean property inherited from the IMFs.

The last step to recognize defects is to identify athresholding method that generates binary imagesfrom IMF fusion images. Usually, defects appearbrighter than the background due to the oblique illu-mination. Therefore, global binarization using double

706 Textile Research Journal 84(7)



thresholds calculated with the following empiricalequation is adopted:36

t ¼ �� k�ð Þ ð7Þ

where t is the threshold, � and � denote the mean andstandard deviation of the sub-image, respectively, and kis an empirical value usually set around 3. We foundthat k¼ 3.5 gave an optimal result from the trial tests.The pixels at the values between the double thresholdsare considered as defect-free fabric areas, whereas theother pixels are regarded as defect areas. These can beinterpreted as follows:

IBW i, jð Þ ¼

0 Isub i, jð Þ4�þ k�

1 �� k� � Isub i, jð Þ � �þ k�

0 Isub i, jð Þ5�� k�

8>><>>:

ð8Þ

where IBW(i,j) and Isub(i,j) denote the pixels of thebinary result and the constructed sub-image, respect-ively. The resultant binary images of the images inFigure 2 are shown in Figure 3.

The whole process of this defect detection algorithm,including modified BEMD, IMF fusion and defect seg-mentation, is illustrated in Figure 4.

Experimental details

We used two sets of experimental samples: the onecaptured by our image acquisition system with a line-scan digital camera (Dalsa: Spyder2–4 k)24 and theother selected from TILDA’s Textile TextureDatabase.37 The computer used in the experiment hasan Intel E7300 CPU, 2G DDR II memory andWindows 7 operating system. The first six samplesshown in Table 2 are the fabric images captured inour laboratory (256� 256 pixels, 350 dpi), representingdefect-free fabric, broken end, broken pick, hole,

Figure 1. Delaunay triangulation-based interpolation: (a) synthetic fabric image; (b) maxima set; (c) triangular grid; (d) upper

envelope.

Li et al. 707



Table 1. Bidimensional empirical mode decomposition analysis of synthetic fabric images

Figure 2. Intrinsic mode function fusion results of defect-free fabric (a) and defective fabric (b).




foreign fiber and bar on both twill and plain fabric. Thefabric images shown in the last six rows of Table 2 wereselected from TILDA’s database, including a black for-eign yarn (C1R1E4N3.TIF), bruises (C1R1EAIC.TIF),a white foreign yarn (C1R1EAJM.TIF),stitches (C1R1EAJO.TIF) and holes under differentillumination conditions (C1R3EBMU.TIF andC2R3ECWK.TIF). In the experiment and the subse-quent discussions, the sample images are indexed assamples 1–12. We also performed a validation testwith 103 selected images of warp, weft and isolateddefects. These defect images were visually classified by

the experts first and then counted with the implementedBEMD algorithm.

Stopping criterion

An important conclusion that can be induced fromEquation (1) is that iteration times of the sifting processare determined by stopping criterion SDmax. Table 3illustrates the error rates (standard deviation betweentwo continuous iterations) of the first 10 iterations ofsifting for IMF1 performed on the 12 samples.

As shown in Table 3, error rates usually fluctuateand did not quickly converge after several iterations.This can be attributed to a natural drawback of inter-polation on discrete data. Another reason for theresults is the negative effect of the sifting, includingthe smoothing of uneven amplitudes and the boundaryeffect, which cause interference to the computations.The negative effects of sifting do not surpass the posi-tive effects until the error rate is lower than a valuebetween 0.04 and 0.07. Thus, the stopping criterionSDmax should be greater than 0.07. On the otherhand, the error rates rapidly decrease when they reacha value beyond 0.30, prompting us to set suitable SDmax

between 0.07 and 0.30. The conditions for IMF1 arethat it should contain the main part of texture structureand avoid time-consuming iteration in sifting. To sat-isfy these requirements, we set 0.2 as the optimal valueof SDmax after testing different values. During the cal-culation of SD, the computational precision is 10�6.Sifting is commonly terminated after 2–4 cycles,shown as gray cells in Table 1. In particular, sample12 reflects the surface of hemp fabric, which showsa higher irregularity of woven structures than

Figure 3. Defect segmentation results of the defect-free fabric (a) and the defective fabric (b).

Figure 4. Fabric defect detection scheme based on bidimen-

sional empirical mode decomposition (BEMD).

Li et al. 709



Table 2. Defect segmentation results based on bidimensional empirical mode decomposition




Table 2. Continued

Table 3. Error rates of the first 10 iterations of sifting for IMF1 from the sample images

Defect sample

Iteration times

1 2 3 4 5 6 7 8 9 10

1 13.055 0.089 0.053 0.046 0.041 0.043 0.046 0.045 0.043 0.043

2 9.189 0.244 0.059 0.053 0.051 0.049 0.050 0.052 0.051 0.048

3 10.102 0.251 0.105 0.093 0.084 0.080 0.076 0.072 0.076 0.073

4 12.176 0.650 0.098 0.075 0.076 0.073 0.082 0.083 0.075 0.073

5 5.730 0.126 0.079 0.070 0.065 0.066 0.063 0.064 0.066 0.067

6 3.580 0.104 0.067 0.060 0.068 0.058 0.053 0.056 0.057 0.054

7 16.906 0.266 0.147 0.109 0.086 0.079 0.080 0.075 0.073 0.073

8 24.148 0.216 0.134 0.106 0.089 0.081 0.076 0.078 0.075 0.072

9 16.837 0.206 0.154 0.111 0.089 0.079 0.071 0.069 0.064 0.062

10 20.476 0.194 0.108 0.085 0.076 0.068 0.064 0.064 0.064 0.060

11 31.464 0.238 0.113 0.092 0.077 0.074 0.071 0.072 0.069 0.070

12 5.497 0.277 0.283 0.159 0.191 0.131 0.129 0.093 0.118 0.115

Li et al. 711



other samples. Thus, the error rate of this sample moreintensely fluctuates, and more iteration is involved in itssifting. We generated IMFs containing a sound profileof defects using SDmax¼ 0.2, as shown in the ‘IMFfusion’ column of Table 2 .

Defect segmentation

Using the segmentation method described by Equations(7) and (8), we generated the binary results, as shown inthe last column in Table 2. The sample set involvesdifferent fabric structures, for example, twill, plainfabric and hemp, and defect of various styles.Segmentation was well implemented on these samplesand the defect profiles were provided. We can use thesegmentation results to identify the defect location.

Validation test

The 103 sample images of three main groups of fab-ric defects (warp, weft and isolated) were processed totest the performance of the proposed detection scheme.The detection rates are compared with the expertcounting in Table 4. The total detection rate is96.1%, suggesting the proposed method owns a highaccuracy.

Computational complexity

The computational complexity of one iteration of sift-ing is O(n), which is equivalent to O(2n+2n+2),including O(n) for DT interpolation,38 O(n) for extremaidentification and O(1) for computing local mean sur-faces and oscillating signals. For fabric inspection, thenumber of IMFs is fixed to three and the iteration timesof sifting process for each IMF is relatively solid at 2–4.Thus, these parameters are not problem scales thataffect computational complexity. The proposed algo-rithm has a complexity of O(n), which is reasonablefor industrial application. For a 256� 256 image, theaverage computational time with an Intel E7300 CPU is3.4789 s. Improvement in calculation speed will be oneof our focus points in the future development.

Conclusion

In this paper, a novel fabric defect detection methodbased on BEMD has been introduced. Multi-scale rep-resentation by BEMD was carried out for the separ-ation of different components in fabric images. Theamount of information in an IMF was determined bythe stopping criterion of sifting. The fabric texture wasseparated into IMF1, and inhomogeneous illuminationwas retained in the residue. Thus, IMF2 and IMF3contained defect information and can be fused into asub-image with enhanced defect information.Binarization based on double thresholds was carriedout to segment defects out of the sub-image. The pro-posed scheme was tested on various types of defects,such as broken ends, broken picks, holes, bars andstitches, both from our image acquisition system andthe TILDA database. The validation test demonstratedthat the method has a defect detection rate of 96.1%,and is robust to different types of defects and fabrictextures.

Funding

This research received no specific grant from any fundingagency in the public, commercial or not-for-profit sectors.

Acknowledgment

The authors would like to thank the anonymous reviewersand the handling associate editor for their insightful com-

ments. This project was supported by ‘‘National NaturalScience Foundation of China’’ [grant number 61203363 and61202310], ‘‘China Postdoctoral Science Foundation’’ [grant

number 20110490098], ‘‘Special Financial Grant from theChina Postdoctoral Science Foundation’’ [grant number2012T50754], ‘‘Foundation of Engineering Research Centerof Technical Textiles, Ministry of Education’’ [grant number

2011k-03], ‘‘Open project program of key laboratory of ECO-Textiles (Jiangnan University), ministry of education, China’’[grant number KLET1114 and KLET1113], ‘‘Fundamental

Research Funds for the Central Universities’’ [grant numberJUDCF11006], ‘‘Natural Science Foundation of JiangsuProvince’’ [grant number BK2011156], ‘‘Research Project of

Jiangsu Entry-Exit Inspection and Quarantine Bureau’’ [grantnumber 2013KJ28], and ‘‘Program for PostgraduatesResearch Innovation in University of Jiangsu Province’’

[grant number CXZZ11_0472 and CXZZ12_0748].

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Table 4. Detection rates for different fabric images

Defect

style

Expert

counting

BEMD-based

detection

Detection

rate (%)

Warp 37 36 97.3

Weft 36 34 94.4

Isolated 30 29 96.7

Total 103 99 96.1

BEMD: bidimensional empirical mode decomposition.




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