defect detection in fabric images using two...

DEFECT DETECTION IN FABRIC IMAGES USING TWO DIMENSIONAL DISCRETE

WAVELET TRANSFORMATION TECHNIQUE

T.D.Venkateswaran1

Research Scholar, Department of Computer Science, Madurai Kamaraj University, Madurai, India.

[email protected]

G.Arumugam2

Senior Professor and Head, Department of Computer Science, Madurai Kamaraj University, Madurai,

India.

[email protected]

Abstract

Defect recognition is one of the problems in image

processing and many different methods based on

texture analysis have been proposed. In this paper, a

method is proposed for recognizing defects in fabric image textures based on two dimensional discrete

wavelet transformation techniques. The proposed

approach applied to real fabric textures. The proposed

algorithm shows good result to detect all types of

defects occurred in fabric images. High detection rate

and low computational complexity are advantages of

this proposed approach.

Keywords: Defect Detection, Image Processing,

Discrete wavelet transformation technique.

1. Introduction Today thanks to advances in machine visions and

hardware, monitoring and classification process of

industrial products can be performed automatically

using intelligent software and high speed hardware.

Visual quality inspection system play an important role

in many industrial and commercial applications such as

tiles, metal, agricultural products, fabric, ceramic, paper

and etc. Any hole, damage, abnormalities and slot in

products surfaces are called defect. Ghazini et al.

proposed a defect detection approach of tiles using

combination of two dimensional wavelet transform and

statistical features. Henry et al. used ellipsoidal region

features and min-max technique on patterned fabric for

detecting defects. Ch. Lin et al., described a texture

defect detection system based on image deflection

compensation. Tolba used a probabilistic neural

network (PNN) for fast defect classification based on the maximum posterior probability of the Log-Gabor

based statistical features. Alimohammadi et al.,

proposed a new method using optimal Gabor filters to

detecting skin defect of fruits which was usable in

agricultural products visual quality inspection systems

(APVQIS). Some of defect detection approaches are

compared by Xie et al.

The computational complexity of most of previous

approaches is too high and some of them don’t guarantee an accurate result for every model of defects.

So in this article, an approach is proposed to defect

detection without these problems.

1.1 Wavelet Transformation Because the frequency contents of signals are very

important, transforms are usually used. The earliest

well known transform is Fourier transform which is a

mathematical technique for transforming our view of

the signal from time domain to frequency domain.

Fourier transform breaks down the signal constituents

into sinusoids of different frequencies. However,

Fourier transform comes with serious shortage that is

the lost of time information which mean it is

impossible to tell when a particular event take place

[20]. This shortage vanishes with using wavelet

transform. A shifted version of the original signal is called mother wavelet which it is a wave form

effectively a limited duration and its average value is

zero. The most well known wavelets are Haar. Figure

(1) depicts some types of these wavelets [21].

1.2 Continuous Wavelet Transform The Continuous Wavelet Transform (CWT) given in

Equation (1), where x(t) is the signal to be analyzed,

and ψ(t) is the mother wavelet or the basis function

which it must be integrated to zero as given in

Equation. All the wavelet functions used in the

transformation are derived from the mother wavelet

T D Venkateswaran et al , International Journal of Computer Science & Communication Networks,Vol 4(1),33-40

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mailto:[email protected]

(Figure 3) through translation (shifting) and scaling (dilation or compression).

Note that τ and S are real numbers representing

translation and scaling parameters respectively. The

translation parameter τ relates to the location of the

wavelet function as it is shifted through the signal.

Thus, it corresponds to the time information in the

Wavelet Transform. The scale parameter S shows either

dilates (expands) or compresses a signal. Scaling

parameters are calculated as the inverse of frequency

[22].

Figure 1. Most popular Wavelets.

1.3 1-D Discrete Wavelet Transform

The CWT calculates coefficients at every scale

which leads to need much time and awful lot amount of

data. If scales and positions are selected based on

powers of two, analysis will be much more efficient

and accurate. This type of selection is called dyadic scales and positions. This analysis can be produced

from the Discrete Wavelet Transform (DWT) [17].

DWT is used to decompose (analyze) the signal into

approximation and detail called coefficients.

Approximation coefficients represent the high scale

(low frequency) components of the signal as if it is a

low pass filter. Detail coefficients represent the low

scale (high frequency) components of the signal as if it

is a high pass filter. Given a signal S of size N,

downsampling the approximation coefficients (cA) is

given by N/2 and the detail coefficients (cD) is given

by N/2 (Fig. 2).

Figure 2 1-D discrete wavelet transforms

The decomposition process of DWT can be

iterated to the first time approximation coefficients cA1

resulting second detail coefficients cD2 and second

approximation coefficients cA2 which can be

decomposed again. This process is known as the

Wavelet decomposition tree (Fig. 3-a) and its inverse

operation of decomposition is called reconstruction, or synthesis. Reconstruction is used to retrieve the signal

back from wavelet coefficients without lose of

information. The reconstruction of the signal is done

using Inverse Discrete Wavelet Transform (IDWT)

operation (Fig. 3-b).

Figure (3). a) 1-D DWT decomposition tree, b) 1-D

DWT reconstruction tree

1.4 2-D Discrete Wavelet Transform

Discrete Wavelet Transform (DWT) is not only

applied to 1-D signals, but also applied to two

dimensional matrixes applied images. Each element in

the matrix represents the intensity of gray color in the

image. The computation of the wavelet transform of

image is applied as a successive convolution by a filter

of row/column followed by a column/row. The results

of DWT on image are four coefficients matrices [15].


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Given image f (x, y), the 2-D wavelet analysis operation consists of filtering and down-sampling horizontally

using a 1-D low pass filter L and a high pass filter to

each row in the image f (x, y), and produces the

coefficient matrices f L (x, y) and f H (x, y). Vertically,

filtering and down-sampling follow using the low pass

and high pass filters L and H to each column in fL(x, y)

and fH (x, y). This produces 4 sub-images fLL (x, y),

fLH (x, y), fHL(x, y) and fHH (x, y) for one level of

decomposition. f LL (x, y) is a smooth sub-image,

which represents the approximation of the image. fLH

(x, y), fHL (x, y), and fHH (x, y) are detail sub-images

which represent the horizontal, vertical and diagonal

directions of the image respectively [14]. As mentioned

before, DWT can be applied again to the approximation

fLL (x, y) where the resulted coefficients matrix of

approximation and details of DWT determined by the

level k of decomposition using the relation 3k+1. Fig.

(4-a) and (4-b) show the first and third level concepts of DWT for image f (x, y).

Figure 4 (a) First level of DWT

Figure 4 (b) Third level of DWT

This paper is organized as follows. In section II, we review the literature in the area of defect detection in

fabric image. In section III, we give the proposed defect

detection algorithm using singular value decomposition

technique. In section IV, we give the results and

discussions and in section V we provide the conclusion

for this paper.

2. LITERATURE REVIEW Methods that are found in literature for the

inspection of patterned texture images include the

traditional image subtraction methods [6-10], the

method of golden image subtraction (GIS) [1], the

method of wavelet-preprocessed golden image

subtraction (WGIS) [1], the method of Direct-

Thresholding (DT) based on wavelet transform [1], the Bollinger Bands method [2], the Regular Bands

method, the Local Binary Pattern (LBP) method [3],

and the motif-based methods [4, 5].

The basic GIS method involves a training stage with

lot of defect-free samples and a testing stage [1]. In the

training stage, the energy of the golden image

subtraction, which is defined as the sum of absolute

difference between the golden image (a template unit of

size that is more than that of the periodic unit) and a

histogram-equalized reference image (defect-free

image) over a given window, is obtained at every pixel

location. Thresholds are obtained from several defect-

free images. In the testing stage, energies obtained from

the golden image and the defective test images are

compared with the thresholds obtained from the

training stage to find the defects after using a median

filter or Weiner filter to perform filtering. The method was tested with 30 defect-free and 30 defective pmm

images. The detection success rates obtained for the

pmm images are 100% for defect-free images and

56.67% for defective images. The overall success rate

was found to be 78.33%. In order to conquer the

sensitivity of this method to noise, the WGIS method

was developed [1]. This is similar to the GIS method

expect that a Haar wavelet transform is applied over all

the images and the sub-images (in level-1

approximation) are utilized instead of the original

image. The overall success rate was improved to

96.7%.

The traditional image subtraction method developed

by Chin and Harlow for the examination of printed

circuit boards involves a direct subtraction of the image

that is under inspection with a defect-free template

image [6]. Since this method involves pixel to pixel comparison, it is sensitive to noises and distortions.

Khalaj et al. developed a method of inspecting

patterned wafers based on periodicity estimation using

a gray value projection and a reference image that is


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constructed from the input image itself using the average gray values of all the periodic units [7].

Pixel-to-pixel comparison between the test image

and the reference or template image, which is based on

an assumed threshold, helps in identifying the defects.

Xie and Guan presented a similar method, wherein the

building block needed for constructing a reference

image is extracted based on linear interpolation [8].

However, when the defect size in the image is too

large, the building block constructed based on the

methods recommended in [7, 8] can never be a good

estimate of the true value.

In the method of DT [1], the Haar wavelet transform

is applied to the reference images and the fourth level

horizontal and vertical details are extracted. Lower and

upper bound values of the three horizontal details in

level-4 and also vertical details are extracted and their

averages are calculated after filtering. Thresholds

obtained using these horizontal and vertical details in the training stage with defect-free images are utilized in

the testing stage for finding the defects in pmm images.

The detection success rates were found to be 86.77%

for defect-free images and 90% for defective images.

The overall detection success rate was found to be

88.3%.

Fabric defect detection using the modified local

binary pattern (LBP) [3] involves two stages, namely,

the training stage and the defect detection stage. In the

training stage, the LBP operator is applied to an image

of defect-free fabric pixel-by-pixel, and a reference

feature vector is computed. The defect-free fabric is

then divided into several windows of size that are

slightly more than that of periodic unit and an LBP

operator is applied to each of these windows to get a

suitable threshold from the defect-free image. In the

detection stage the defective fabric is divided into several windows (as in the training stage) and LBPs are

obtained. Defects are then located in the fabric based

on the threshold. The method was tested on pmm, p2,

and p4m images and the detection success rate was

found to be 96.7%.

Ngan et al. [4, 5] developed motif-based methods

for detecting defective lattices from 16 out of 17

wallpaper groups based on energy and the variance of

the hand-located lattices. Minimum- maximum

decision boundaries (rectangular boundaries) are

obtained in an energy variance space from several

defect-free test images using hand-located defect-free

and defective lattices that are said to be composed of

motifs[4]. The energy of the moving subtraction

between a motif and its circular shift matrices is

derived using a norm-metric measurement and the

variance of the energies for all motifs is obtained. By learning the distribution of these values over a number

of defect-free lattices, boundary conditions for discerning defective and defect free lattices are

obtained. As the 16 wallpaper groups of patterned

fabric can be transformed into three major groups,

namely, pmm, p2, and p4m, the method was evaluated

over these three major wallpaper groups. Decision

boundaries were obtained using 160 defect-free lattices

samples and the method was tested with 140 defect-free

and 113 defective samples. An overall detection

success rate of 93.3% was achieved.

3. PROPOSED ALGORITHM

The steps for proposed Defect Detection Algorithm are

as follows:

Load the Test Texture image in BMP or JPEG

Format.

Reduce the noises in Test Texture image using

median filter.

Convert the Test Texture image to Gray scale

image.

Transform the gray scale image (spatial

domain) into frequency domain using Haar

wavelet. Extract the approximation coefficient

matrix image and compute the otsu’s threshold

and number of regions in the approximation

matrix image.

Compare the Otsu’s threshold value and the number of regions present in the test image with

the reference image.

If the difference is greater than detection

sensitivity level (D), declare that test fabric

image is defective; otherwise test fabric image

is defect free.

The flowchart of the Algorithm is shown in Figure 5.

4. RESULTS AND DISCUSSIONS

Table I shows the values of number of regions,

Otsu’s threshold value, number of regions difference

and Otsu’s threshold difference with respect to defect

free and several defective fabric texture images. The

value of D is 5 and the threshold difference is within 0.0045882. But in transform domain, traditional

inspection result not matched with the proposed

method for the defect miss-pick. The proposed method

shows defect-free fabric texture even though the fabric

having miss-pick defect. The Haar wavelet is used for

this experimentation.

Table II shows the values of number of

regions, Otsu’s threshold value, number of regions

difference and Otsu’s threshold difference with respect

to defect free and several defective slate texture images

in wavelet transformation domain. The value of D is 25

and the threshold difference is within 0.0035686. The


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proposed method result shows that the defect free slate texture as defective one.

5. CONCLUSION

In this paper, two dimensional discrete wavelet

transformation techniques have been effectively used

for the development of the automated defect detection

scheme for fabric texture images. Experiments on real

fabric and slate texture images with defects show that

the proposed method is robust in finding fabric defects and slate defects. Thus, the proposed method can

contribute to the development of computerized defect

detection in fabric industries.

REFERENCES

[1] H.Y.T. Ngan, G.K.H. Pang, S.P. Yung and M.K. Ng, “Wavelet based methods on patterned fabric defect detection,” Pattern Recognit., Vol.38, No.4, 2005, pp.559-576. [2] H.Y.T. Ngan and G.H.K. Pang, “Novel method for patterned fabric inspection using Bollinger bands,” Opt. Eng., Vol.45, No.8, 2006,

pp.087202-1-15. [3] F. Tajeripour, E. Kabir and A. Sheikhi, “Fabric Defect Detection Using Modified Local Binary Patterns,” Proc. of the Int. Conf. on Comput. Intel. and Multimed. Appl., Sivakasi, Tamilnadu, India, December, 2007, pp.261-267. [4] H.Y.T. Ngan, G.H.K. Pang and N.H.C. Yung, “Motif-based defect detection for patterned

fabric,” Pattern Recognit., Vol.41, No.6, 2008, pp.1878-1894. [5] H.Y.T. Ngan and G.H.K. Pang, “Ellipsoidal decision regions for motif-based patterned fabric defect detection,” Pattern Recognit., Vol.43, No.6, 2010, pp.2132-2144. [6] R.T. Chin and C.A. Harlow, “Automated

visual inspection: A survey,” IEEE Trans. on Pattern Anal. and Mach. Intel., Vol.4, No.6, 1982, pp.557-573. [7] B.H. Khalaj and T. Kailath, “Patterned wafer inspection by high resolution spectral estimation techniques,” Mach. Vision and Appl., Vol.7, 1994, pp.178-185.

[8] P. Xie and S.U. Guan, “A golden-template self-generating method for patterned wafer inspection,” Mach. Vision and Appl., Vol.12, 2000, pp.149-156.

[9] Gonzalez, R., R. Woods and S. Eddins, 2004. “Digital Image Processing Using MATLAB”. 1st Edn., Prentice Hall, [10] Jain A K,”Image Analysis and Computer Vision”, PHI, New Delhi, 1997 [11] O. Silv´en, M. Niskanen, and H. Kauppinen, “Wood inspection with non-supervised

clustering”, Machine Vision and Applications, 13:275–285, 2003. [12] I. Rossi, M. Bicego, and V.Murino. “Statistical classification of raw textile defects”, In IEEE Internationa Conference on Pattern Recognition, volume 4, pages 311– 314, 2004. [13] F. Adamo., F. Attivissimo, G. Cavone, N. Giaquinto and AML. Lanzolla “Artificial Vision

Inspection Applied To Leather Quality Control”, 13th International Conference on Pattern Recognition, Volume 2, 25-29; 2006. [14] F. Pernkopf., “Detection of surface defects on raw steel blocks using Bayesian network classifiers”, Pattern Analysis and Applications, 7:333–342, 2004.

[15] Z. Ibrahim, S. Al-Attas, Z. Aspar. “Model-based PCB Inspection Technique Using Wavelet Transform”. Proceedings of the 4th Asian Control Conference (ASCC), 2002. [16] C. Boukouvalas, J. Kittler, R. Marik, M. Mirmehdi, and M. Petrou, “Ceramic tile inspection for colour and structural defects”, Proceedings of AMPT95, ISBN 1 872327 01 X, pp.

390–399, August 1995. [17] H. M. Elbehiery, A. A. Hefnawy, and M. T. Elewa. “Visual Inspection for Fired Ceramic Tile's Surface Defects Using Wavelet Analysis”. Graphics, Vision and Image Processing (GVIP) Vol no 2, pp. 1-8, January 2005. [18] M. Leo, T. D’Orazio, P. Spagnolo and A.

Distante. “Wavelet and ICA Preprocessing for Ball Recognition in Soccer Images” ICGST International Journal on Graphics, Vision and Image Processing (GVIP),Vol no. 1 pp. 11-16, 2007. [19] XianghuaXie. “A ReviewofRecentAdvancesin Surface Defect Detection using Texture analysis

Techniques” Electronic Letters on Computer Vision and Image Analysis vol. (3):1-22, 2008. [20] Matlab Wavelet toolbox documentation. “The language of technical computing” from mathworks. Version 7.0, 2006.


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[21] C.H. Lee, Y.J. Wang and W.L. Huang. “A Literature Survey of Wavelets in Power Engineering Applications”. Proceeding National Science Council. Vol. 24, no. 4, pp. 249-258, 2000. [22] E. Bozzi, G. Cavaccini, M. Chimenti, M. G. Di Bono and O. Salvetti. “Defect detection in C -

scan maps”. Pattern Recognition and Image Analysis, Vol. 17, No. 4, pp. 545–553, 2007. [23] D.M. Tsai and B. Hsiao. “Automatic surface inspection using wavelet reconstruction”, Pattern Recognition. Vol. 34 no. 6, pp. 1285–1305, 2001.

Figure 5. Flowchart of the proposed algorithm

LOAD THE TEST TEXTURE IMAGE

NOISE REDUCTION USING MEDIAN FILTER

COMPARE THE OTSU’S THRESHOLD VALUE AND

NUMBER OF REGIONS IN TEST IMAGE WITH

REFERENCE IMAGE

CONVERT THE RGB IMAGE TO GRAY SCALE IMAGE

CONVERT THE GRAY SCALE IMAGE TO WAVELET

TRANSFORM IMAGE USING HAAR WAVELET AND

EXTRACT THE APPROXIMATION MATRIX IMAGE

IF DEFECT

DETECTED?

DEFECT FREE TEST TEXTURE

IMAGE

DEFECTIVE TEST TEXTURE

IMAGE

END

NO YES


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Table I Feature values related to fabric textures images in transform domain

Pictorial representation of Table I is shown in Figure 6.

0

20

40

60

80

100

120

140

160

180

DEFECT FREE R

EFERENCE

HOLE DEFECT

STAIN

MIS

S-PIC

K

MIS

S-END

DOUBLE-P

ICK

DOUBLE-E

ND

WARP-F

LOAT

COURSE-PIC

K

WEFT D

ENSITY

TEAR

CONTAMIN

ATION

SNARL

DEFECT FREE F

ABRIC

Series1

Series2

Figure 6 Pictorial representation of Table I

FABRIC TEXTURES NO OF

REGIONS

THRESHOLD

VALUE

NO OF

REGIONS

DIFFERENCE

THRESHOLD

DIFFERENCE

RESULT OF

TRADITIONAL

INSPECTION

RESULT OF

PROPOSED

METHOD

DEFECT FREE

REFERENCE

19 0.91765 0 0 DEFECT FREE DEFECT FREE

HOLE DEFECT 43 0.87059 24 0.04706 DEFECTIVE DEFECTIVE

STAIN 25 0.90588 6 0.01177 DEFECTIVE DEFECTIVE

MISS-PICK 17 0.92157 2 0.00392 DEFECTIVE DEFECT FREE

MISS-END 53 0.86667 34 0.05098 DEFECTIVE DEFECTIVE

DOUBLE-PICK 19 0.84314 0 0.07451 DEFECTIVE DEFECTIVE

DOUBLE-END 4 0.92941 15 0.01176 DEFECTIVE DEFECTIVE

WARP-FLOAT 2 0.92549 17 0.00784 DEFECTIVE DEFECTIVE

COURSE-PICK 153 0.91373 134 0.00392 DEFECTIVE DEFECTIVE

WEFT DENSITY 33 0.88627 14 0.03138 DEFECTIVE DEFECTIVE

TEAR 21 0.86275 2 0.0549 DEFECTIVE DEFECTIVE

CONTAMINATION 1 0.9098 18 0.00785 DEFECTIVE DEFECTIVE

SNARL 50 0.8902 31 0.02745 DEFECTIVE DEFECTIVE

DEFECT FREE FABRIC 14 0.91765 5 0 DEFECT FREE DEFECT FREE


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Table II Feature values related to slate textures images in transform domain

Pictorial representation of Table II is shown in Figure 7.

0

5

10

15

20

25

30

DEFEC

T FR

EE REFER

ENCE

DRO

PLET D

EFECT

SPO

TS DEFE

CT

DEB

RIS

DEFEC

T

TEMPLATE D

EFECT

LUM

P DEFEC

T

NO

PAIN

T DEFEC

T

EFFLORESEN

CE D

EFECT

SHADE D

EFEC

T

INSU

FFICIE

NT P

AINT D

EFECT

TEMPLATE M

ARK D

EFEC

T

DEFEC

T FR

EE SLA

TE

Series1

Series2

Figure 7 Pictorial representation of Table II

FABRIC TEXTURES NO OF

REGIONS

THRESHOLD

VALUE

NO OF

REGIONS

DIFFERENCE

THRESHOLD

DIFFERENCE

RESULT OF

TRADITIONAL

INSPECTION

RESULT OF

PROPOSED

METHOD

DEFECT FREE

REFERENCE

19 0.91765 0 0 DEFECT FREE DEFECT FREE

HOLE DEFECT 43 0.87059 24 0.04706 DEFECTIVE DEFECTIVE

STAIN 25 0.90588 6 0.01177 DEFECTIVE DEFECTIVE

MISS-PICK 17 0.92157 2 0.00392 DEFECTIVE DEFECT FREE

MISS-END 53 0.86667 34 0.05098 DEFECTIVE DEFECTIVE

DOUBLE-PICK 19 0.84314 0 0.07451 DEFECTIVE DEFECTIVE

DOUBLE-END 4 0.92941 15 0.01176 DEFECTIVE DEFECTIVE

WARP-FLOAT 2 0.92549 17 0.00784 DEFECTIVE DEFECTIVE

COURSE-PICK 153 0.91373 134 0.00392 DEFECTIVE DEFECTIVE

WEFT DENSITY 33 0.88627 14 0.03138 DEFECTIVE DEFECTIVE

TEAR 21 0.86275 2 0.0549 DEFECTIVE DEFECTIVE

CONTAMINATION 1 0.9098 18 0.00785 DEFECTIVE DEFECTIVE

SNARL 50 0.8902 31 0.02745 DEFECTIVE DEFECTIVE

DEFECT FREE FABRIC 14 0.91765 5 0 DEFECT FREE DEFECT FREE


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