DEFECT DETECTION IN FABRIC IMAGES USING TWO DIMENSIONAL DISCRETE WAVELET TRANSFORMATION TECHNIQUE

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1 DEFECT DETECTION IN FABRIC IMAGES USING TWO DIMENSIONAL DISCRETE WAVELET TRANSFORMATION TECHNIQUE T.D.Venkateswaran 1 Research Scholar, Department of Computer Science, Madurai Kamaraj University, Madurai, India. thadanvenkateswaran@gmail.com G.Arumugam2 Senior Professor and Head, Department of Computer Science, Madurai Kamaraj University, Madurai, India. gurusamyarumugam@gmail.com 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 33

2 (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 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 1. Most popular Wavelets 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 (3). a) 1-D DWT decomposition tree, b) 1-D DWT reconstruction tree 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]. 34

3 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 defectfree 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 35

4 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 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 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 The 36

5 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 [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 [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 [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 [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 [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 [7] B.H. Khalaj and T. Kailath, Patterned wafer inspection by high resolution spectral estimation techniques, Mach. Vision and Appl., Vol.7, 1994, pp [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 [9] Gonzalez, R., R. Woods and S. Eddins, 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: , [12] I. Rossi, M. Bicego, and V.Murino. Statistical classification of raw textile defects, In IEEE Internationa Conference on Pattern Recognition, volume 4, pages , [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; [14] F. Pernkopf., Detection of surface defects on raw steel blocks using Bayesian network classifiers, Pattern Analysis and Applications, 7: , [15] Z. Ibrahim, S. Al-Attas, Z. Aspar. Modelbased PCB Inspection Technique Using Wavelet Transform. Proceedings of the 4th Asian Control Conference (ASCC), [16] C. Boukouvalas, J. Kittler, R. Marik, M. Mirmehdi, and M. Petrou, Ceramic tile inspection for colour and structural defects, Proceedings of AMPT95, ISBN X, pp , August [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 [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 , [19] XianghuaXie. A ReviewofRecentAdvancesin Surface Defect Detection using Texture analysis Techniques Electronic Letters on Computer Vision and Image Analysis vol. (3):1-22, [20] Matlab Wavelet toolbox documentation. The language of technical computing from mathworks. Version 7.0,

6 [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 , [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 , [23] D.M. Tsai and B. Hsiao. Automatic surface inspection using wavelet reconstruction, Pattern Recognition. Vol. 34 no. 6, pp , LOAD THE TEST TEXTURE IMAGE NOISE REDUCTION USING MEDIAN FILTER 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 COMPARE THE OTSU S THRESHOLD VALUE AND NUMBER OF REGIONS IN TEST IMAGE WITH REFERENCE IMAGE NO DEFECT FREE TEST TEXTURE IMAGE IF DEFECT DETECTED? YES DEFECTIVE TEST TEXTURE IMAGE END Figure 5. Flowchart of the proposed algorithm 38

7 Table I Feature values related to fabric textures images in transform domain FABRIC TEXTURES NO OF THRESHOLD NO OF THRESHOLD RESULT OF RESULT OF REGIONS VALUE REGIONS DIFFERENCE TRADITIONAL PROPOSED DIFFERENCE INSPECTION METHOD DEFECT FREE DEFECT FREE DEFECT FREE REFERENCE HOLE DEFECT DEFECTIVE DEFECTIVE STAIN DEFECTIVE DEFECTIVE MISS-PICK DEFECTIVE DEFECT FREE MISS-END DEFECTIVE DEFECTIVE DOUBLE-PICK DEFECTIVE DEFECTIVE DOUBLE-END DEFECTIVE DEFECTIVE WARP-FLOAT DEFECTIVE DEFECTIVE COURSE-PICK DEFECTIVE DEFECTIVE WEFT DENSITY DEFECTIVE DEFECTIVE TEAR DEFECTIVE DEFECTIVE CONTAMINATION DEFECTIVE DEFECTIVE SNARL DEFECTIVE DEFECTIVE DEFECT FREE FABRIC DEFECT FREE DEFECT FREE Pictorial representation of Table I is shown in Figure Series1 Series DEFECT FREE REFERENCE HOLE DEFECT 0 STAIN MISS-PICK MISS-END DOUBLE-PICK DOUBLE-END WARP-FLOAT COURSE-PICK WEFT DENSITY TEAR CONTAMINATION SNARL DEFECT FREE FABRIC Figure 6 Pictorial representation of Table I 39

8 Table II Feature values related to slate textures images in transform domain FABRIC TEXTURES NO OF THRESHOLD NO OF THRESHOLD RESULT OF RESULT OF REGIONS VALUE REGIONS DIFFERENCE TRADITIONAL PROPOSED DIFFERENCE INSPECTION METHOD DEFECT FREE DEFECT FREE DEFECT FREE REFERENCE HOLE DEFECT DEFECTIVE DEFECTIVE STAIN DEFECTIVE DEFECTIVE MISS-PICK DEFECTIVE DEFECT FREE MISS-END DEFECTIVE DEFECTIVE DOUBLE-PICK DEFECTIVE DEFECTIVE DOUBLE-END DEFECTIVE DEFECTIVE WARP-FLOAT DEFECTIVE DEFECTIVE COURSE-PICK DEFECTIVE DEFECTIVE WEFT DENSITY DEFECTIVE DEFECTIVE TEAR DEFECTIVE DEFECTIVE CONTAMINATION DEFECTIVE DEFECTIVE SNARL DEFECTIVE DEFECTIVE DEFECT FREE FABRIC DEFECT FREE DEFECT FREE Pictorial representation of Table II is shown in Figure Series1 Series2 5 0 DEFECT FREE REFERENCE DROPLET DEFECT SPOTS DEFECT DEBRIS DEFECT TEMPLATE DEFECT LUMP DEFECT NO PAINT DEFECT EFFLORESENCE DEFECT DEFECT FREE SLATE SHADE DEFECT INSUFFICIENT PAINT DEFECT TEMPLATE MARK DEFECT Figure 7 Pictorial representation of Table II 40