Texture Defect Detection System with Image Deflection Compensation

Transcription

1 Texture Defect Detection System with Image Deflection Compensation CHUN-CHENG LIN CHENG-YU YEH Department of Electrical Engineering National Chin-Yi University of Technology 35, Lane 15, Sec. 1, Chungshan Roa, Taiping, Taichung, Taiwan Abstract: Image textural analysis technology has been wiely use in the esign of automate efect etection systems. Because the presence of efects may change the textural features of an image, a reference image without efects can be compare with the test image to etect whether there are any efects. However, besies efects, the eflection of the input test image coul also change its textural features. When there is any angular ifference between the reference an test images, their textural features woul also be ifferent, even if there is no efect in the test image. As a result, misjugment of the efect etection system may occur. Most of the previous stuies have focuse on the evelopment of textural analysis technology which coul ecrease the effect of test image eflection. This stuy aime to estimate the eflection angle of test images through polar Fourier transform an phase correlation analysis, an rotate the reference image by the same angle to compensate for the eflection of the test image. After the angles of the reference an test images were brought into line, the textural analysis base on the gray level co-occurrence matrix was applie to analyze an compare the textural features of the two images. The results of actual texture efect etection emonstrate that the angular ifferences between the reference an test images coul be estimate correctly, with an estimation error of only 0 to 0.5. By compensating for the eflection of the test image, the accuracy of the texture efect etection coul be effectively enhance. Key-Wors: - Texture efect etection, Image eflection compensation, Polar Fourier transform, Phase correlation analysis, Gray level co-occurrence matrix 1 Introuction In automate prouction systems, efects are likely to occur in many proucts, such as textiles, steel proucts, iscs, an glass an paper proucts, ue to the abnormality of processing tools uring processing. If the efect is too small to be ientifie by visual inspection, it woul be time-consuming an strenuous to carry out manual inspection, an thus inspection may be neglecte. Since the textural features of efects are ifferent from those of the proucts, many stuies have propose image textural analysis technology to esign an automate efect etection system. The main aim of textural analysis is to simulate human capability of istinguishing textures, an ientify appropriate textural features to escribe the image, so as to analyze textural ifferences. The main methos for textural analysis inclue the statistical metho [1-9], the structural metho [10] an the spectral metho [11-15]. The statistical metho can provie all sorts of statistical ata about the relative positions among pixels, an then ecrease the effects of textural image translation, rotation an noise, thus having inherent avantages for efect etection. It can be ivie into first orer, secon orer an higher orer levels. Julesz [16] pointe out that human s vision system is most appropriate for secon-orer statistics; thus, most researchers have aopte this kin of statistics. The secon-orer statistical metho inclues the gray level co-occurrence matrix () [1-5], the neighboring gray level epenence matrix [6-8] an the neighborhoo gray tone ifference matrix [9]. The structural metho consiers that images are forme by textural primaries arrange base on specific rules. Accoringly, it ientifies the arrangement rules by obtaining textural primaries in the image [10]. However, to analyze the images with inconspicuous textural rules, the structural metho cannot effectively obtain the textural primaries an escribe their arrangement rules. The spectral metho uses Fourier transform to analyze the obtaine spectrums an istinguish the features of perioic texture, such as main irection, spatial perio an spectral energy [11-15]. Moreover, there are some other textural analysis methos base on the Markov ranom fiel moel [17,18], the irectional Walsh-Haamar transform [19], the ranom subspace neural classifier [0], the wavelet transform [1], an the optimize filter [,3]. Chang et al. [17] observe that certain spatial an temporal features of the Markov ranom ISSN: Issue 9, Volume 8, September 009

2 fiel ensity function can be erive to moel temporal textures. They propose a texture escriptor base on spatial an temporal preiction resiual of mean filtering to stuy the ynamic nature of textures for vieo inexing. Zimeras [18] further evelope an auto-logistic moel base on Markov ranom fiels to simulate texture patterns. The metho base on the irectional Walsh- Haamar transform [19] can insert the basic avantages of multi-scale an multi-irectional texture analysis into a fast moifie Walsh- Haamar transform. The ranom subspace neural classifier [0] was evelope an use for image recognition in micromechanics, an can recognize ifferent types of metal surfaces after mechanical processing. Ngan et al [1] propose the metho base on wavelet transform an golen image subtraction for efect etection on patterne fabric or repetitive patterne texture. The golen image subtraction can effectively segment out the efective regions on patterne fabric. The optimize filters using linear finite-impulse-response filters [] an Gabor filters [3] were esigne to automatically extract the efects in texture materials. Since the presence of efects can change the textural features of an image, a reference image without efects can be use to compare with the test image to etect whether there are any efects. However, besies efects, the image eflection can also change textural features an affect the accuracy of efect etection. When there is any angular ifference between the reference image an the test image, their textural features are accoringly ifferent, even if there is no efect in the test image. As a result, misjugment of the etection system may occur. Although the current methos of textural analysis can extract various useful features in texture materials, they are all sensitive to image eflection. This stuy aime to compensate for the eflection angle of the test image, so as to enhance the accuracy of texture efect etection. It transforme the rotation relationship between the test an reference images into a isplacement relationship in the frequency omain through polar Fourier transform [4-7], an estimate the isplacement vector in the frequency omain through phase correlation analysis [8] to obtain the angular ifference between the two images. Then, the reference image was rotate by the same angle to compensate for the eflection of the test image. When the angles of the test an reference images were brought into line, the -base statistical metho was applie to analyze an compare the textural features of the two images. The remainer of this paper is organize as follows. Sections an 3 introuce the estimation methos for the eflection angle of the test image an the -base texture analysis, respectively. Section 4 establishes the texture efect etection rules, an efines the performance inexes of the texture efect etection system. Section 5 explains the etection results of the texture efect etection system for all efect images. Section 6 gives the conclusions. Estimation methos for the eflection angle of the test image Accoring to the properties of Fourier transform, spatial signal isplacement or rotation has specific corresponing relationships in the frequency omain. Therefore, the spatial isplacement vector or rotation angle can be calculate from the frequency omain. Kuglin an Hines [8] use the translation property of Fourier transform to propose a phase correlation analysis to calculate the isplacement vector between two images, an applie this analysis in image registration. Other research on image registration [4-7] combine polar Fourier transform an phase correlation analysis to further calculate the ifference of rotation angles between two images. Through polar Fourier transform, the original spatial rotation relationship of these two images before an after rotating coul be expresse as a isplacement relationship in the frequency omain. Therefore, phase correlation analysis can be further use to calculate the isplacement vector in the frequency omain, an to obtain the angle ifference of these two images. In this stuy, the estimation metho for the eflection angle of the test image is escribe in etail as follows. Suppose that f 1 an f are the images before an after a isplacement of ( x 0, y0 ), the relationship between which is as follows: f ( 1 0 y0 x, y) = f ( x x, y ) (1) Accoring to the translation property of Fourier transform, the relationship between Fourier transforms F1 ( u, an F ( u, of image f 1 an f is as follows: jπ ( ux + vy ) 0 F ( u, = e 0 F1 ( u, v ) Moreover, () ISSN: Issue 9, Volume 8, September 009

3 F ( u, jπ ( ux0 + vy0 ) = e F ( u, 1 (3) Accoringly, image isplacement in frequency omain can only change the phase correlation jπ ( ux 0 ) of 0 + vy e. After Eq. (3) taken on inverse Fourier transform, δ ( x x0, y y0 ) can be obtaine. The position of maximum δ ( x x0, y y0 ) is then the isplacement vector ( x 0, y 0 ). The inverse Fourier transform of Eq. (3) is efine as a phase correlation function as follows [8]: 1 F ( u, Corr( x, y) = I ( ) F1 ( u, ( x, y ) = arg max( Corr( x, )) (4) 0 0 y ( x, y) 1 where I is the inverse Fourier transform, an x 0 an y 0 are the positions of the maximum phase correlation function. Therefore, in phase correlation analysis, two images with a isplacement relationship unergo Fourier transform first, an then their phase correlation function is calculate, an the position of the maximum phase correlation function is obtaine, in orer to fin the isplacement vector between the two images. Figures 1 an are the images of a rectangle before an after a isplacement of (30, 10). The corresponing phase correlation function is shown in Figure. As seen, the maximum value of this function appears at (30, 10). Therefore, the spatial isplacement vector of the images is (30, 10). Image rotation also has a specific corresponing relationship in the frequency omain [4-7]. Supposing that f can be obtaine through rotating f 1 by θ 0, then the relationship between f 1 an f can be escribe as follows: f ( x, y) = f1( xcosθ0 + y sinθ0, x cosθ0 + y sinθ0 ) (5) Accoring to the rotation properties of Fourier transform, the relationship between spectrums F 1( u, an F ( u, after f 1 an f unergo Fourier transform is as follows: F ( u, = F1 ( ucosθ0 + vsinθ0, u cosθ0 + vsinθ0 ) (6) Fig. 1 A rectangle image, before isplacement; after a isplacement of (30, 10). Magnitue Fig. The phase correlation function corresponing to a isplacement of (30, 10). Therefore, when the image rotates by θ 0, the spectrum also rotates by θ 0. Figures 3 an are the images of a rectangle before an after rotating by 45. Figures 3 an () show the spectrums of the image before an after rotating. The rotation angles of the spectrum in the frequency omain an the image in the spatial omain are both 45. Although the rotation angles in the frequency an spatial omains are the same, they cannot be calculate irectly in the frequency omain. The spatial rotation relationship nees to be transforme into a isplacement relationship in the frequency omain. Polar Fourier transform transforms the spectrum represente by the rectangular coorinate after Fourier transform into that represente by polar coorinate. Supposing that f is obtaine through translating f 1 by ( x0, y0 ) an rotating it by θ 0, then the relationship between f 1 an f can be escribe as follows: f x, y) = f1( xcosθ 0 + ysinθ0, xcosθ 0 + y sinθ0 y0 ) (7) ( x0 ISSN: Issue 9, Volume 8, September 009

4 Reference Image f 1 Test Image f Polar Fourier Transform Polar Fourier Transform Magnitue Spectrum Calculation Magnitue Spectrum Calculation M ( r, ) M ( r, ) 1 θ θ () Fig. 3 Image an spectrum of a rectangle: an : images before an after rotating by 45 ; an (): spectrums before an after rotating. Through Fourier transform, the following equation can be obtaine: jπ ( ux + vy ) 0 F ( u, = e 0 F1 ( ucosθ0 + vsin θ 0, u cosθ0 + vsinθ0 ) (8) If consiering the magnitue spectrum only, the relationship among them is as follows: M ( u, = M1( ucosθ0 + vsinθ0, u cosθ0 + vsinθ0 ) (9) where M 1 an M are the magnitue spectrums of images f 1 an f ; parameters r an θ that transform the rectangular coorinate into the polar coorinate system are efine as follows: r = u + v (10) tan 1 v θ = ( ) (11) u Then the relationship between magnitue spectrums can be transforme as: Deflection Angle θ 0 Fig. 4 Flow chart for estimating the eflection angle of the test image. M r, θ ) = M ( r, θ ) (1) ( 1 θ0 Phase Correlation Analysis As a result, the spatial rotation relationship between images can be transforme as the translation relationship in the frequency omain through polar Fourier transform. Then, phase correlation analysis is use to calculate the translation vector in this frequency omain. This stuy applie polar Fourier transform an phase correlation analysis to calculate the eflection angle of the test image. Figure 4 is the calculation flow chart of the eflection angle of this test image. After applying polar Fourier transform to the reference an test images, an calculating the magnitue spectrum, if there is any angular ifference between the images, the magnitue spectrums woul have a isplacement relationship. Phase correlation analysis is then applie to calculate the isplacement vector between magnitue spectrums, in orer to obtain the eflection angle of the test image. 3 -base texture analysis -base texture analysis is a secon-orer statistical metho that can be use to calculate the emergency frequency of pixels conforming to the relative angle an istance in a specific position [1- ISSN: Issue 9, Volume 8, September 009

5 5]. The corresponing is ifferent ue to the ifference of the relative position angle of the etecte pixels. Each image has four s uner ifferent angles, 0, 45, 90 an 135. I ( l) an I( m, are two pixels in the two-imension image I, then s uner ifferent angles can be escribe as follows [1]: ( l),( m, I P( i, j,,0) = # k m = 0, l n = (13) I ( l) = i, I( m, = j ( l),( m, I ( k m =, l n = ) P( i, j,,45) = # (14) or ( k m =, l n = ) I( l) = i, I ( m, = j ( l),( m, I P( i, j,,90) = # k m =, l n = 0 (15) I( l) = i, I ( m, = j ( l),( m, I ( k m =, l n = ) P( i, j,,135) = # (16) or ( k m =, l n = ) I( l) = i, I( m, = j where, is the istance between pixels, # is the quantity of collection elements, an i an j are the pixel values of I ( l) an I ( m,. Figure 5 is an 8 8 image example, the gray level values of which are j = 0,1,. Figure 6 is the schematic iagram of this 8 8 image example. Supposing that = 1, then in four irections can be calculate accoring to Eqs. (13), (14), (15) an (16), as shown in Figure 7. Figure 8 is an image example with vertical texture. Figures 9,, an () are the s at 0, 45, 90 an 135, respectively, where a larger inicates higher brightness. As shown in Figure 9, the numerical value istribution is relate to textural structure. The value of structures with fewer gray level interfaces tens to be istribute near the iagonal line, while that of structures with more gray level interfaces spreas from the iagonal line an istributes uniformly Fig. 5 An 8 8 image example. Gray level #(0,0) #(0,1) #(0,) 1 #(1,0) #(1,1) #(1,) #(,0) #(,1) #(,) Fig. 6 schematic iagram for an 8 8 image example () 135 Fig. 7 The co-occurrence matrices of an 8 8 image example. Fig. 8 An image example with vertical textures ISSN: Issue 9, Volume 8, September 009

6 upper right or lower left corners, the contrast is larger. A smaller contrast inicates closer gray-level values among pixels, while a larger contrast inicates larger ifferences of gray-level values among pixels. ) Angular secon moment ASM = [ P ( i j) ] i j, (19) () Fig. 9 s of an image example with vertical textures at 0, 45, 90 an () 135. Since the image texture in Figure 8 presents a vertical tren, at 90 istributes significantly near the iagonal line, as shown in Figure 9. At the same time, s in the other irections istribute uniformly, as shown in Figures 9,, an (). Conventional textural features in texture analysis inclue contrast, angular secon moment (ASM), homogeneity, entropy an correlation, which are efine as follows [-5]: 1) Contrast = i j ( i j) P ( i j) CON, (17) where P is the normalize, efine as follows: P = i= o j= 0 P ( i, j) P (18) Contrast is use to measure the sharp egree of the contrast an reflect the situation of co-occurrence matrices elements near the main iagonal line. When larger values center on the main iagonal line of co-occurrence matrices, the image contrast is smaller. Otherwise, when larger values centre at the The ASM value is equal to the quaratic sum of all elements in an also the energy of. ASM is use to measure the consistency an uniformity of image gray level istribution, which means the repeate emergence probability of a certain pixel: higher probability inicates higher consistency. When an image has consistent texture, few values of normalize off the iagonal line are approximate to 1, while others are approximate to 0. Therefore, when the image texture is more consistent an uniform, values ten to centre on the few elements, thus making the ASM value larger. Otherwise, when values istribute more uniformly, the ASM value is smaller. 3) Homogeneity 1 HOM = P ( i, j), i j (0) i j i j When larger values are concentrate aroun the main iagonal line, homogeneity woul be larger. Opposite to contrast, homogeneity is use to analyze the homogeneous egree of the image: the more homogeneous the image (contrast less sharp), the larger the homogeneity. An image with a sharper contrast is etaile, while one with higher homogeneity is coarse. 4) Entropy = i j ( i j) log P ( i j) EN P,, (1) Entropy is mainly use to measure textural complexity. When the texture of a textural image is unorerly, gray level values istribute uniformly, making the values of all elements in the cooccurrence matrix smaller. Therefore, larger entropy inicates higher textural complexity. Entropy is in inverse proportion to energy. The co-occurrence matrix values of an image with higher entropy istribute uniformly. ISSN: Issue 9, Volume 8, September 009

7 5) Correlation i j P ( i, j) μ xμ y i j COR = σ xσ y () where, μ μ σ x y = i i P ( i, j) j = j i i P ( j, i) ( x = i j ( y = i j i μ ) P ( i, j) x y σ j μ ) P ( i, j) (3) where μ x, μ y, σ x an σ y are the mean values of rows an stanar eviations of columns. Correlation values can reflect image irectivity. When images arrange along a certain irection, the correlation of textural features in this irection is higher than that in other irections. A feature vector is then efine combining Eqs. (17), (19), (0), (1) an () as follows: [ ASM HOM EN COR] CON (4) The feature vector can be use to calculate the Euler istance between the reference image an the test image, which is efine as follows: xy = m i= 1 ( x y ) (5) i i where xi an yi are the feature vectors of the reference an test images, respectively. The Euler istance can be use to calculate the istances among vectors in orthogonal space. However, in this stuy, it is use to estimate the nearness of feature vectors. When the Euler istance is lower than the efault critical value, there is no efect in the test image. Otherwise, when the Euler istance is higher than the efault critical value, there are some efects in the image. The texture efect etection algorithm propose in this stuy is as follows: Step 1: Reference image an test image with pixels are input. Step : Polar Fourier transform an phase correlation analysis are applie to calculate angular ifference θ 0 between the reference an test images. Step 3: The reference image with pixels is rotate by θ 0. After the angles of the reference an test images are consistent, a block of 3 3 pixels in a central position is extracte as a reference. Step 4: The test image of pixels is ivie into 64 test blocks of 3 3 pixels. Step 5: Feature matrixes an the Euler Distance of the reference block an all test blocks are calculate. Step 6: When the Euler Distance is larger than the efault threshol value, the value is set accoring to actual texture kins. In this stuy, performance inexes such as specificity, sensitivity, positive preictive accuracy (PPA), negative estimative accuracy (NPA), an total estimative accuracy (TPA) [9] were applie to estimate the performance of the efect etection system. Specificity is use to estimate the correctness of etecting zero efects in perfect images. Sensitivity is use to estimate the correctness of etecting the existence efects in actually efective images. PPA is use to estimate the etection correctness of the existence of efects. NPA is use to estimate the etection correctness of zero efects. TPA is use to estimate the total etection correctness. Supposing that the efect etection results are as in Table 1, all performance inexes are efine as follows: Specificity = 100% ( b + ) a Sensitivity = 100% ( a + c) a PPA = 100% ( a + b) NPA = 100% ( c + ) (6) (7) (8) (9) 4 Texture efect etection algorithm a + TPA = 100% ( a + b + c + ) (30) ISSN: Issue 9, Volume 8, September 009

8 5 Experiment results In this stuy, four kins of test images with bifilar efects, panel efects, greasy efects an scratch efects uner ifferent eflection angles were applie to evaluate the performance of the texture efect etection system. As seen from Figure 10, when there is no eflection in the test image, applying -base texture analysis only can etect the efective blocks correctly. However, given any eflection in the test image angle, the etection correctness of applying -base texture analysis only ecreases greatly. Figures 11, 1, 13 an 14 show the efect etection results of -base texture analysis without an with image eflection compensation when the efective test images eflect ifferent angles. Because the ifferences between the perfect an efective blocks for greasy an scratch efects are less conspicuous than those for bifilar an panel efects, the Euler istances an the threshol value of ientifying efective blocks for greasy an scratch efects are also lower than those for bifilar an panel efects. The threshol values for ientifying bifilar, panel, greasy an scratch efects are 300, 300, 15 an 5, respectively. The smaller Euler istances also cause more misjugments in the etection of greasy an scratch efects when the input test image eflects, if only applying -base texture analysis without image eflection compensation. Moreover, the larger the eflection angle, the lower the accuracy of the efect etection. After aing phase correlation analysis an compensating for the eflection angle of the test image, the etection accuracy of the efective blocks can revert to that when the test image has no angular eflection. This stuy further analyze the performance inexes of the texture efect etection system for ifferent eflection angles an test images with various efects. Tables, 3, 4 an 5 list the performance inexes for bifilar efects, panel efects, greasy efects an scratch efects, respectively, using -base texture analysis without an with image eflection compensation uner ifferent eflection angles of the input test image. The quantity of actual perfect an efective blocks is epenent on the eflecte test image. The results emonstrate that image eflection compensation can increase TPA by 11% (97%-86%), 1% (100%-99%), 5% (97%-45%) an 37% (98%- 61%) in the etection of bifilar efects, panel efects, greasy efects an scratch efects, respectively. As seen, the eflection of the test image has the smallest effect on the textural features of the test image with panel efects, an the largest effect on the test image with greasy efects. Table 1 Distribution of efect etection results Detecte efective blocks Detecte perfect blocks Actual efective blocks Actual perfect blocks Total a b a+b c c+ Total a+c b+ () Fig. 10 Defect etection results of texture analysis when there is no eflection in the test image bifilar efects panel efects, greasy efects an () scratch efects. Tables, 3, 4 an 5 also inicate the actual eflection angles of the test image an the estimate eflection angles by polar Fourier transform an phase correlation analysis. The estimation error of the eflection angle is only from 0 to 0.5. Hence the eflection angle of the test image can be compensate for quite correctly. ISSN: Issue 9, Volume 8, September 009

9 () () Fig. 11 Bifilar efects etection results when the input test image eflects 45 ( an ) an 150 ( an ()) using -base texture analysis without ( an ) an with ( an ()) image eflection compensations. Fig. 13 Greasy efects etection results when the input test image eflects 45 ( an ) an 150 ( an ()) using -base texture analysis without ( an ) an with ( an ()) image eflection compensations. () () Fig. 1 Panel efects etection results when the input test image eflects 45 ( an ) an 150 ( an ()) using -base texture analysis without ( an ) an with ( an ()) image eflection compensations. Fig. 14 Scratch efects etection results when the input test image eflects 45 ( an ) an 150 ( an ()) using -base texture analysis without ( an ) an with ( an ()) image eflection compensations. ISSN: Issue 9, Volume 8, September 009

10 Table Performance inexes for the etection of bifilar efects. Specificity Sensitivity PPA NPA TPA + Image Deflection Compensation Actual eflection angles are 30, 45, 60 an 150, while estimate eflection angles are 9.5, 45, 60.5 an The threshol value is 300. Table 4 Performance inexes for the etection of greasy efects. Specificity Sensitivity PPA NPA TPA + Image Deflection Compensation Actual eflection angles are 11, 19, 9 an 36, while estimate eflection angles are 11.3, 19.5, 9.5 an The threshol value is 15. Table 3 Performance inexes for the etection of panel efects. Specificity Sensitivity PPA NPA TPA + Image Deflection Compensation Actual eflection angles are 9, 14, an 33, while estimate eflection angles are 8.5, 14.1,.5 an 3.5. The threshol value is 300. Table 5 Performance inexes for the etection of scratch efects. Specificity Sensitivity PPA NPA TPA + Image Deflection Compensation Actual eflection angles are 5, 11, 7 an 38, while estimate eflection angles are 5.4, 11., 6.7 an The threshol value is 5. 6 Conclusions This stuy estimate angular ifferences between the reference an test images using polar Fourier transform an phase correlation analysis, an rotate the reference image by the same angle to compensate for the eflection of the test image. The estimation error of the image eflection angle was only from 0 to 0.5. Hence the estimate eflection angle can be further applie to compensate for eflection effects. The experimental results of the texture efect etection showe that, when there is some eflection in the test image, the ISSN: Issue 9, Volume 8, September 009

11 efect etection accuracy of using -base texture analysis only ecreases greatly, while that of compensating for the angular eflection of the test image can be effectively enhance. In conclusion, the texture efect etection system with image eflection compensation propose in this stuy can effectively solve the problem of test image eflection an enhance the performance of the texture efect etection system. 7 Acknowlegement This research was financially supporte by the National Science Council uner Grant No B References: [1] R.M. Haralic K. Shanmugan, I.H. Dinstein, Textural features for image classification, IEEE Transaction on Systems, Man, an Cybernetics, Vol.SMC-3, 1973, pp [] L.K. Soh, C. Tsatsoulis, Texture Analysis of SAR Sea Ice Imagery Using Gray Level Co- Occurrence Matrices, IEEE Transactions on Geoscience an Remote Sensing, Vol.37, 1999, pp [3] D. Mery, M.A. Berti, Automatic Detection of Weling Defects Using Texture Features, Insight, Vol.45, 003, pp [4] U. Kanaswamy, D.A. Ajeroh, M.C. Lee, Efficient Texture Analysis of SAR Imagery, IEEE Transactions on Geoscience an Remote Sensing, Vol.43, 005,pp [5] C.E. Honeycutt, R. Plotnic Image analysis techniques an gray-level co-occurrence matrices () for calculating bioturbation inices an characterizing biogenic seimentary structures, Computers an Geosciences, Vol.34, 008, pp [6] C. Sun, W.G. Wee, Neighboring Gray Level Depenence Matrix for Texture Classification, Computer Vision, Graphics an Image Processing, Vol.3, 198, pp [7] L.H. Siew, R.M. Hogson, E.J. Woo, Texture Measures for Carpet Wear Assessment, IEEE Transaction on Pattern Analysis an Machine Intelligence, Vol.10, 1988, pp [8] A. Murali, W.V. Stoecker, R.H. Moss, Detection of Soli Pigment in Dermatoscopy Images Using Texture Analysis, Skin Research an Technology, Vol.6, 000, pp [9] C.I. Christooulou, S.C. Michaelies, C.S. Pattichis, Multifeature Texture Analysis for the Classification of Clous in Satellite Imagery, IEEE Transactions on Geoscience an Remote Sensing, Vol.41, 003, pp [10] C.D. Ruberto, G. Roriguez, S. Vitulano, Image Segmentation by Texture Analysis, Proceeings of International Conference on Image Analysis an Processing, 1999, pp [11] C.H. Chen, G.K.H. Pang, Fabric Defect Detection by Fourier Analysis, IEEE Transactions on Inustry Application, Vol.36, 000, pp [1] Z. Peng, T.B. Kir Two-imensional Fast Fourier Transform an Power Spectrum for Wear Particle Analysis, Tribology International, Vol.30,1997, pp [13] D. Zhang, G. Lu, Stuy an Evaluation of Different Fourier Methos for Image Retrieval, Image an Vision Computing, Vol.3, 005, pp [14] D.M. Tsai, C.Y. Hsieh, Automate Surface Inspection for Directional Textures, Image an Vision Computing, Vol.18, 1999, pp [15] D.M. Tsai, T.Y. Huang, Automate Surface Inspection for Statistical Textures, Image an Vision Computing, Vol.1, 003, pp [16] T. Caelli, B. Julesz, Psychophysical Evience for Global Feature Processing in Visual Texture Discrimination, Journal of the Optical Society of America, Vol.69, 1979, pp [17] W.H. Chang, N.C. Yang, C.M. Kuo, Y.J. Chen, A Novel Temporal Texture Descriptor Using Spatial-Temporal Local Distribution for Vieo Retrieving, WSEAS Transactions on Information Science an Applications, Vol. 3, 006, pp [18] S. Zimeras, Simulating texture patterns using auto-logistic moels, WSEAS Transactions on Systems, Vol. 5, 006, pp [19] S.A. Monajemi, P. Moallem, A fast multiscale/multi-irectional texture feature extraction scheme, WSEAS Transactions on Computers, Vol. 5, 006, pp [0] T. Baiy E. Kussul, O. Makeyev, Texture recognition with ranom subspace neural classifier, WSEAS Transactions on Circuits an Systems, Vol. 4, 005, pp [1] H.Y.T. Ngan, G.K.H. Pang, S.P. Yung, M.K. Ng, Wavelet base methos on patterne fabric efect etection, Pattern Recognition, Vol.38, 005, pp [] A. Kumar, G.K.H. Pang, Defect Detection in Texture Materials Using Optimize Filters, IEEE Transaction on Systems, Man, an Cybernetics, Vol.3, 00, pp ISSN: Issue 9, Volume 8, September 009

12 [3] A. Kumar, G.K.H. Pang, Defect Detection in Texture Materials Using Gabor Filters, IEEE Transactions on Inustry Applications, Vol.38, 00, pp [4] B.S. Rey, B.N. Chatterji, An FFT-Base Technique for Translation, Rotation, an Scale-Invariant Image Registration, IEEE Transactions on Image Processing, Vol.5, 1991, pp [5] A. Averbuch, Y. Keller, FFT Base Image Registration, Proceeings of IEEE International Conference on Acoustics, Speech an Signal Processing, Vol.4, 00, pp.iv/3608-iv/3611. [6] Y. Keller, A. Averbuch, M. Israeli, Pseuopolar-Base Estimation of Large Translations, Rotations, an Scalings in Images, IEEE Transactions on Image Processing, Vol.14, 005, pp.1-. [7] Y. Keller, Y. Shkolnisky, A. Averbuch, The Angular Difference Function an Its Application to Image Registration, IEEE Transactions on Pattern Analysis an Machine Intelligence, Vol.7, 005, pp [8] C.D. Kuglin, D.C. Hines, The phase correlation image alignment metho, Proceeings of IEEE International Conference on Cybernetics an Society, 1975pp [9] P.F. Griner, R.J. Mayewski, A.I. Mushlin, P. Greenlan, Selection an interpretation of iagnostic tests an proceures. Principles an applications, Annals of internal meicine, Vol.94, 1981, pp ISSN: Issue 9, Volume 8, September 009