ARTIFICIAL NEURAL NETWORK FOR TEXTURE CLASSIFICATION USING SEVERAL FEATURES: A COMPARATIVE STUDY

Transcription

1 ATIFICIAL NEUAL NETWOK FO TEXTUE CLASSIFICATION USING SEVEAL FEATUES: A COMPAATIVE STUDY Mohmmed W. Ashour, Khled M. Mhr, nd Mhmoud F. Hussin College of Engineering & Technology, Arb Acdemy for Science & Technology nd Mritime Trnsport, Alexndri, Egypt eng.m.shour@gmil.com, khmhr@st.edu, mfrouk@pmi.uwterloo.c ABSTACT Texture nlysis plys n essentil nd mjor rule in imge clssifiction nd segmenttion in wide rnge of pplictions such s medicl imging, remote sensing nd industril inspection. In this pper, we review the well known pproches of texture feture extrction nd perform comprtive study between them. These pproches re nmely gry level histogrm, edge detection, nd co-occurrence mtrices, besides Gbor nd Biorthogonl wvelet trnsformtions. The feed forwrd rtificil neurl network (ANN) with bck- propgtion lgorithm (BPA) is used s supervised clssifier. Experiments re conducted on two different dtsets tken from multi-clss engineering surfces produced by six mchining processes nd from Brodtz (966) textures lbum respectively. The clssifiction ccurcy is tested for both dtsets, while the qulity of estimtion is tested for surfce roughness prmeters of the mchined surfces dtset only bsed on the roughness prmeters evluted from contct mesurement test. Keywords: Feture extrction, Supervised neurl network, Texture Clssifiction.. INTODUCTION Texture nlysis is defined s the clssifiction or segmenttion of texturl fetures with respect to the shpe of smll element, density nd direction of regulrity []. A number of texture feture extrction pproches hve been proposed in the literture such s neighborhood reltionships, Fourier trnsform, numericlly clculted prmeters, wvelet trnsform, nd multi-resolution nlysis. Hrlick et l. [] used the neighborhood reltionships for intensity mtrix to extrct fetures from the texture. Also, Wei Min Shi et l. [3] proposed pttern recognition theory to perform surfce roughness clssifiction by comprison with known surfces. While, Sin-Wng-Sonei et l. [4] ttempted to detect nd clssify surfce defects, in textured mterils using wvelet pckets. Shmuel Peleg, [5] presented method for texture clssifiction bsed on the chnge in the properties of the imges with the chnge in its resolution. Yun Zhng- et l. [6] focused on the development of wvelet-integrted technique for str mplitude rdr imges (SA) nd multispectrl imges fusion. For roughness estimtion, Kung-Chyi et l. [7] discussed the estimtion of surfce roughness of workpieces in turning opertions. Luk et l. [8] utilized sttisticl prmeters, derived from the gry level intensity histogrm such s the rnge nd men vlue of the distribution nd correlted them with the roughness vlue ( ) determined from the stylus method. In this pper, we use the gry level histogrm, edge detection, co-occurrence mtrices, Biorthogonl wvelet trnsform nd Gbor wvelet trnsform s feture extrction techniques. An input vector of significnt fetures is tken for some tested smples to nlyze nd clssify its texture nd to estimte of the mchined textures lso without contct by using the ANN clssifier. A supervised clssifier bsed on feed forwrd bck- propgtion neurl network is proposed, it uses n dptive lerning rte with momentum term lgorithm. The recently reserch in texture clssifiction is usully depends on supervised clssifiers such s supervised ANN nd Support Vector Mchines (SVM) or unsupervised clssifiers such s Self Orgnizing Mps (SOM). For exmple, oberto Mrmo et l. [9] used the supervised ANN to clssify crbonte rock textures bsed on the digitized imges of thin section rocks. S. Arivzhgn et l. [0] hs found texture fetures by clculting the men nd vrince of the Gbor filtered imge, nd chieved rottion normliztion by the circulr shift of the feture elements, then texture similrity mesurement of the query imge nd the trget imge in the dtbse ws computed by minimum distnce criterion. Chih-Ming Chen et l. [] used the texture fetures derived from Gbor nd other four wvelet trnsforms for clssifiction nd clustering bsed on SVM nd SOM, with comprison between both techniques to show tht these pproprite clssifiers perform resonbly well. The encourging nd experimentl results show tht the suggested methodology cn identify nd clssify the type of the mchining process used to produce the work-pieces nd lso cn clssify the Brodtz ptterns by supervised ANN. The reminder of this pper is orgnized s follows. The proposed lgorithm cn be divided entirely into two min prts: the texture nlysis nd texture clssifiction. In section the methodology pplied in our experiment is discussed with brief overview on the proposed techniques in texture nlysis nd fetures extrction, lso it contins the detiled description of the texture clssifiction prt with the supervised rtificil neurl network clssifier nd its phses. Section 3 contins the discussions nd the nlysis of ACIT 007, 6-8 November 007, Lttki, Syri 484

2 the proposed lgorithm experimentl results. Finlly section 4 concludes the pper.. THE METHODOLOGY The im of this pper is to provide wide comprison between some different schemes in texture feture extrction, s illustrted in Figure the proposed lgorithm is divided into three stges, in the first one fetures extrction methods which re histogrm, edge detection, co-occurrence mtrices, Biorthogonl wvelet trnsform nd Gbor wvelet trnsform re pplied to our texture imge, then secondly the input feture vector is selected for ech imge to be clssified in the third stge by the ANN clssifier, fter tht the prmeter is estimted for the mchined dtset surfces nd compred with the mesured. The following sections re describing these stges in detils... EDGE DETECTION METHOD The edge is chrcterized by n brupt chnge in intensity indicting the boundry between two regions in n imge, which hve different gry-level vlues, nd is often referred to s discontinuity. detection is usully bsed on the clcultion of intensity grdients cross the imge. Figure shows the detected edges for n imge. The occurrence of high locl intensity grdient, indicting sudden intensity trnsition, nd is evidence for the existence of n edge discontinuity [7]. The edges re extrcted from the mchined surfce imges t 0 nd 90 degree of rottion horizontlly nd verticlly to hve new imges with edges detected. Then, the men nd vrince of ech imge re clculted nd stored respectively in vector t column shpe with length equls the number of columns (80*) elements, tht s pplied for ll imges of specimens giving 36 columns, ech hs 560 elements. These vectors re divided into six mtrices ccording to clss type, ech of size (560*6) nd re used s n input to the ANN. ()..3 GAY LEVEL CO-OCCUENCE MATICES The co-occurrence mtrix is generlly referred to s gry-level co-occurrence mtrix whose entries re trnsitions between ll pirs of two gry levels [8]. The gry-level trnsitions re clculted bsed on two prmeters, displcement nd ngulr rottion, giving four gry-level co-occurrence mtrices t 0, 45, 90,35 degrees orienttion s shown in Figure 3. In Figure 3 cells &5 re0 0, cells & 6 re 35 0, cells 3&7 re 90 0, nd cells 4 & 8 re 45 0 nerest neighbors. For n imge hving sptil resolution N x * N y nd gry scle level 56, the ngulr reltionship between pirs t distnce d= between pixels is s follows: (b) Figure : () Originl imge, (b) Detected edges. Figure : Experimentl Setup. At 0 0 orienttions (horizontl) the co-occurrence mtrix contins N y (N x -) nerest horizontl neighbor pirs.. FEATUE EXTACTION METHODS AND SYSTEM SETUP.. GAY LEVEL HISTOGAM The histogrm is the number of occurrences of ech gry-level intensity in n imge [6]. The histogrm of ech imge gives vector of length 56. The output of histogrm lgorithm for ll specimens gives 36 vectors ech with 56 vlues; these vectors re divided into six mtrices ccording to clss type, ech of size (56*6) nd then re used s n input to the ANN. Figure 3: Neighbors reltionship. ACIT 007, 6-8 November 007, Lttki, Syri 485

3 At 45 0 orienttion (right digonl) the co-occurrence mtrix contins (N y -) (N x -) nerest right digonl neighbor pirs. By symmetry there will be N y (N x -) nerest verticl neighbor pirs nd (N y -) (N x -) nerest left digonl neighbor pirs [8]. In this method the men nd vrince of Neighbors reltionship t 0 0 nd 80 0 orienttions re collected together t one vector of (5+5) for ech specimen imge giving 36 columns, ech hs 04 elements to be used s n input to the ANN supervised clssifier...4 WAVELET TANSFOMATIONS The WT of continuous function s(t) is given by: t τ W, = ( ) s( t) dt ψ τ () Where, nd τ re the diltion nd trnsltion prmeters. W, is the wvelet trnsform of s(t). In, τ j the most common formultion; =. The trnsltion is discredited with respect to ech scle j by using τ = k T. In this cse, the wvelet bsis functions re obtined by j / j ψ ( t) = ψ ( t ) () j, k kt The two prmeters expnsion of signl is termed s prticulr wvelet bsis functions (or wvelets) ψ(t),nd scling function φ (t). The wvelet decomposition of signl is obtined by convolving the signl with fmily of rel orthonorml bsis functions ψ jk (t) [9]; where k represents trnsltion of the wvelet function of time, while integer j, however, is n indiction of the wvelet frequency nd generlly referred to s scle (higher scle corresponds to finer locliztion nd vice vers). The two wvelet prmeters scling φ ( t ) ψ ( t ) nd wvelet bsis re utilized s expressed by [9] s ( t) = Ak j ( ) + ( ) 0, k t D j, kψ j, k t j= j0 φ (3) where: k: n integer representing the trnsformtion of the wvelet function nd indiction of time or spce in wvelet trnsform. j: n indiction of the wvelet frequency or spectrum. A k : pproximtion coefficient. D j,k: represents the detils of the signl t different scles. ψ : is used to define the detils, nd φ: is used to define the pproximtions. Wvelet cn be divided into different clsses nd in mny different wys. The most commonly used clsses cn be ctegorized into two clsses Orthogonl nd Biorthogonl wvelets...4. OTHOGONAL WAVELET In this system the nlysis (the decomposition) nd the synthesis (the reconstruction) filters re not symmetric, nd the order of filters is lwys n even number. Orthogonl wvelets re very successful in numericl nlysis like solving prtil differentil equtions, speech coding nd other similr pplictions, where symmetry is not mjor requirement...4. BIOTHOGONAL WAVELET In this system the decomposition nd reconstruction filters cn be forced to be symmetric. For ψ 7,, Φ 7, the st number represents the order of low pss filter (LPF) for decomposition, while the nd number represents the order of reconstructed LPF [9]. In imge processing pplictions, Bi-orthogonl wvelets, which re symmetric, re more desirble. Symmetric wvelets llow extension t the imge boundries nd prevent imge contents from shifting between sub-bnds. The two-dimensionl wvelet trnsform (-DWT) for imge ppliction leds to decomposition of pproximtion coefficients t level j in four components s shown in figure 4, the pproximtion t level j+, nd the detils in three orienttions (horizontl, verticl, nd digonl). In this pper, since our im is to clssify, identify nd estimte the surfce roughness prmeter for the engineering surfces dtset, we will hve to extrct the most high frequency components from these imges to get better performnce from our technique, so we pply two dimensionl Biorthogonl wvelet trnsform to the tested imges, then we extrct from the trnsform coefficients the three detils (horizontl, verticl, nd digonl) with orienttions 0 0, 90 0, 45 0 respectively. Figure 4: Two Dimensions wvelet trnsformtion. We hve performed severl tests on different types of fetures, the finl set is composed of the following: - Sum: is the sum of pixels vlues in detil component [8]. - Mximum: is the lrgest element long different dimensions of n rry. 3- Minimum: is the smllest element long different dimensions of n rry. 4- Men: is the men vlue of the elements long different dimensions of n rry. 5- Stndrd devition (std): for -D mtrix is row vector contining the stndrd devition of the elements in ech column nd cn be expressed s. ACIT 007, 6-8 November 007, Lttki, Syri 486

4 n n ( ) s = x i x, where x = n i= n i= xi (4) 6- Medin: is the medin vlues of the elements long different dimensions of n rry. 7- nge: is the minimum nd mximum vlues vector of elements long different dimensions. 8- z: is known s the ISO 0 point height prmeter in ISO 487/-948, is mesured on the roughness profile only nd is numericlly the verge height difference between the five highest peks nd the five lowest vlleys within the smpling length [4]. 9-3y: the devition from the third highest pek to the third lowest vlley in ech smple length is found, 3y is then the lrgest of these vlues [4]. 0-3z: is the verticl men from the third highest pek to the third lowest vlley in smple length over the ssessment length [4]. - Energy: is the percentge of energy corresponding to the pproximtion, nd it is extrcted from the wvelet decomposition coefficients. The previous fetures re extrcted from the three detils produced by trnsforming one imge, nd then collected to gther, in order to hve finlly single vector of length 33 elements representing ech imge. These output vectors re divided into six mtrices ccording to clss type, ech mtrix of size (33*6) nd then re used s n input to the ANN...5 GABO WAVELET TANSFOM A Gbor function is the product of Gussin function nd complex sinusoid, two dimensionl Gbor function g(x,y) nd its Fourier trnsform G(u,v) cn be written s [0] x y g ( x, y) = exp + + π jwx πσ xσy σσ σ y = ( u W ) v G( u, v) exp + σ u σ v Where, σ u πσ =, x σ v = πσ y (5) (6) Gbor functions form complete but non-orthogonl bsis set. Expnding signl using this bsis provides loclized frequency description. A clss of self-similr functions referred to s Gbor wvelets, is now considered. Let g(x,y) be the mother Gbor wvelet, then this self-similr filter dictionry cn be obtined by pproprite diltions nd rottions of g(x,y) through the generting function :,, g mn ( x, y) = G( x, y ) (7), m nd x = ( xcosθ + ysinθ ) (8) x, m = ( xsinθ + ycosθ ) (9) Where nπ θ = k nd k is the totl number of orienttions. The scle fctor is -m ment to ensure tht the energy is independent of m. Feture extrction using Gbor functions is motivted by the fct tht, these filters cn be considered s orienttion nd scle tunble detectors. Here, in this method the output is obtined by pplying Gbor filter on ech texture imge for different orienttions in steps of 30 0, nd constnt vlues for vrinces long x nd y-xes (S=0.05, F=0.05) respectively, nd phse (P= 0), fter tht the men nd stndrd devition of ll trnsformed coefficients re found, these fetures re collected together in one single vector of length (*=4) elements to represent ech imge nd then used s n input to the ANN.. ATIFICIAL NEUAL NETWOK CLASSIFIE The used supervised clssifier is bsed on feed forwrd bck- propgtion neurl network, which uses n dptive lerning rte with momentum term lgorithm; this clssifier is constructed nd trined with the following configurtion nd prmeters: Three lyer rchitecture, i.e. one hidden lyer. Number of nodes in the input lyer equl to the length of input vector. Output lyer is n identity mtrix with digonl length equl to the number of input t ech btch network. Type of ctivtion function between input nd hidden lyers is tn-sigmoid but the function between hidden nd output lyer is pure line. The mximum epochs for trining = The error gol = The disply frequency =50. In trining mode the sme ANN rchitectures is used nd trined for two different dtsets seprtely. In order to be ble to test unlimited number of smples we chose to trin ech 6 smples together in one independent ANN. > m,n Integers Figure 5: Three lyers feed-forwrd neurl network showing its inputs nd outputs. ACIT 007, 6-8 November 007, Lttki, Syri 487

5 Then t the reclling mode we cll ll the trined smples for ech testing. Figure 5 illustrtes the used neurl network with its inputs nd outputs. When clssifiction is done using ny of the proposed texturl nlysis methods, the clssifier receives six input mtrices from ech method for the mchined surfces dtset, nd eight mtrices from ech method when clssifying Brodtz textures. Ech mtrix is trined using one different network by pssing the mtrix columns one by one to the input lyer of the network, nd then weights nd bises re sved for reclling mode. Once the trining hs been performed, test mode is pplied to ensure tht the network will give the sme response nd produce the sme desired output when the sme input is pplied t ny time (i.e. clssify the specimens). 3. EXPEIMENTAL WOK AND ESULTS 3. FIST DATASET The first used dtset consists of 36 smples of multiclss engineering surfces produced by six mchining processes nmely, turning, grinding, Horizontl-milling, verticl-milling, lpping nd shping []. Turning is the opertion of producing surfces of rottion using single point cutting tool but in grinding the mteril is removed by mens of rotting brsive wheel to obtin better surfce finish, nd Milling is the process of removing mteril from the surfce of the work piece by feeding the work-piece pst rotting multipoint cutter, while Lpping is the finl brsive finishing opertion tht produces extreme dimensionl ccurcy, corrects minor imperfections of shpe, refines surfce finish, nd produces close fit between mting surfces. Shping cn be defined s the oldest single point mchining process since it uses stright-line cutting motion to generte flt surfce [4]. Some preprocessing work is needed to be done before getting the tested imge redy. Firstly, we prepre our 36 rel specimens using the specified six mchining processes then the roughness of its surfces is mesured using precise stylus instrument form Tlysurf series- instrument nd store this dt s reference on our PC to be compred with the estimted ones when pplying our technique lter, fter tht we cquire imges for the textures of size using precision cmer to be our first dtset. 3.. OUGHNESS MEASUEMENTS oughness is one of the min components in engineering surfces texture; it cn be expressed using the following sets of prmeters: Amplitude prmeters: mesure the verticl chrcteristics of the surfce devitions. Spcing prmeters: mesure the horizontl chrcteristics of the surfce devitions. Hybrid prmeters: which re combintions of both horizontl nd verticl devitions [8]. These prmeters re mesured long profile produced by precise instrument trveling cross the surfce. The profile is divided into smple lengths, which re long enough to include sttisticlly relible mount of dt, yet short enough to exclude undesired dt from the mesurement. The mplitude prmeters include: or the verge roughness vlue (the universlly recognized nd most used interntionl mesure of roughness) nd it is clculted s follows: L r ( x) dx = / L Z (0) r 0 Where: Lr is the length in the direction of x xis used for ssessing the profile under evlution, nd Z(x) is the mplitude long the line profile [5]. 3.. IMAGE ACQUISSITION This stge describes the process of converting the picture into its numericl representtion, which is suitble for further imge processing steps. Intensity imges with 56 liner gry tones (zero corresponds to blck color nd 55 to white), were cptured for test specimen surfces. The surfces were viewed under n opticl microscope equipped with digitl cmer. Cptured imges were lso stored in PC for further nlysis. () (b) (c) (d) (e) Figure 6: Different mchined processes smples, (), (b), (c), (d) nd (e) re turning, grinding, milling, lpping nd shping respectively ESULTS The experimentl results of the different five texturl fetures extrction techniques using the mchined textures dtset were s follows: The Histogrm method gives number of input neurons (56) to the ANN with number of hidden neurons equl to (00), which implies n verge simplicity for the ANN rchitecture, nd it hs (6/36) smples re hving minimum error in estimtion, regrding its ccurcy (97.%) for ech smple. The edge detection method gives highest number of input neurons (560) in the ANN nd it gives number of hidden neurons equl to (00), which implies the most complex ANN rchitecture, nd it ACIT 007, 6-8 November 007, Lttki, Syri 488

6 hs the lowest number (/36) smples in estimtion with minimum error, regrding its low ccurcy (88.88%) for ech clss. The Co-occurrence mtrix method gives number of input neurons (04) in the ANN nd gives number of hidden neurons equl to (00), which implies lso n verge complexity ANN rchitecture, but it hs the highest number (9/36) smples in estimtion with minimum error, regrding its good ccurcy (00%) for ech clss. The bio-orthogonl wvelet method gives quite low number of input neurons (33) t the ANN nd it lso gives low number of hidden neurons (00) which implies simple ANN rchitecture, nd it hs (4/36) smples in estimtion with minimum error, regrding its good ccurcy (00%) for ech clss. The Gbor wvelet method gives lowest number of input neurons (4) in the ANN nd the lowest number of hidden neurons (60) which implies the simplest ANN rchitecture, nd it hs (3/36) smples in estimtion with minimum error, regrding its good ccurcy (00%) for ech clss. 3. SECOND DATASET To prepre the second dtset we collect from Brodtz lbum the imges (D to D96) of size The trining nd reclling re performed to ll imges using the sme supervised ANN model with the sme prmeters vlues nd the sme tolernce which is ( ± 0.005)., Brodtz texture imges were used in this work s second dtset in order to verify the correct ccurcy resulted when using the engineering mchined surfces dtset. (multiplied by 0-6 ) for the 36 mchined surfces with the estimted ones which creted by the suggested estimtor dimensioned in micron. A MATLAB7 (the Mth Works inc.) bsed computer code nlysis hs been developed for imge processing nd neurl network implementtion. Figure 7: Accurcy of vrious texturl nlysis methods using the six different clss dtset. 3.. ESULTS The ccurcy results of the clssified Brodtz textures, s shown in tble nd figure 9 re between 85.4% nd 9.66% nd it nerly mtches the results obtined by our first dt set. 3.3 ANALYSIS AND DISCUSSION Since we re working s n offline mode imge inspection, the number of epochs in the ANN trining phse will not be considered in our comprtive study, tble shows comprison between input nd hidden lyers neurons, while Figure 7 shows the ccurcy for the pplied five feture extrction methods using the six different multi-clss dtset we find tht the best ccurcy is reched when Co-occurrence, Biorthogonl nd Gbor methods re pplied while the lowest complexity ANN Architecture is the Gbor. In Figure 9 for Brodtz textures we cn see nerly similr ccurcy comprison which my mch the one in the first group. With the knowledge of ctul surfce roughness vlues ( ), which mesured nd stored before nd relted to ANN output tolernce, the estimted roughness vlues re obtined nd shown in Figure 8, with minimum error using the vrious texturl nlysis methods for the sme dtset. While, Tbles 3, 4, 5, 6, 7 nd 8 show the mesured roughness vlues Figure 8: Number of smples estimted with minimum error in ech clss using the five methods. Tble : Number of Neurons in Input nd Hidden Lyers. Anlysis Method Histogrm Co-occ. Wvelet Gbor Input Lyer Hidden Lyer Tble : esulted ccurcy of vrious texturl nlysis methods using Brodtz textures dtset Anlysis Method Histogrm Co-occ. Wvelet Gbor Defined Smples Undefined Smples Accurcy ACIT 007, 6-8 November 007, Lttki, Syri 489

7 Tble 6: Mesured nd Estimted for V-Milling Smples using the five fetures extrction methods Smple Mesured Figure 9: Accurcy comprison for the five texture nlysis methods pplied to Brodtz textures. Tble 3: Mesured nd Estimted for Turning Smples using the five fetures extrction methods Smple Mesured Histogrm Wvelet Gbor N.D Histogrm Wvelet Gbor N.D Tble 4: Mesured nd Estimted for Grinding Smples using the five fetures extrction methods Smple Mesured Tble 7: Mesured nd Estimted for Lpping Smples using the five fetures extrction methods Smple Mesured Histogrm Wvelet Gbor Histogrm Tble 8: Mesured nd Estimted for Shping Smples using the five fetures extrction methods Smple Mesured Wvelet Histogrm N.D. Gbor N.D Tble 5: Mesured nd Estimted for H-Milling Smples using the five fetures extrction methods Smple Mesured Histogrm Wvelet Gbor N.D Wvelet Gbor CONCLUSIONS AND FUTUE WOK We hve proposed different pproches bsed on imge processing nd multi-lyer perceptron supervised neurl network tht llowed us to clssify two different grey level dtsets of digitized texture imges. The proposed system cn estblish the reltionship between ctul surfce roughness nd texture fetures of mchined surfce imges (the first dtset), which cn be identified nd clssified bsed on severl texturl nlysis methods. ACIT 007, 6-8 November 007, Lttki, Syri 490

8 The need of using second textures dtset - which is tken from Brodtz imges Album - is to confirm tht our lgorithm performs efficiently. Moreover, our system cn effectively estimte surfce roughness vlue of the first dtset textures with no contct to the specimens nd without knowledge bout mchines prmeters (i.e. cutting speed, feed rte, nd depth of cut) since we hve mesured roughness vlues reserved s reference for these textures. The supervised neurl network clssifier uses BPA nd n dptive lerning rte with momentum term lgorithm. Inputs to the ANN include fetures vectors relted to gry level histogrm, edge detection, co-occurrence mtrices, Biorthogonl wvelet trnsformtion nd Gbor wvelet trnsformtion. This clssifier gives ccurcy between 88.88% nd 00% in clssifiction of the mchined work-pieces, while it gives ccurcy between 85.4% nd 9.66% in clssifiction of the Brodtz textures. As future work we pln to use other promising texture clssifiction techniques such s support vector mchines. On the other hnd we believe tht study tht compres the performnce of the proposed texturl nlysis methods with the most recently pplied techniques such s ridgelet, curvelet nd contorlet trnsformtions would be useful to provide us more efficient representtion for the selected fetures vector nd then leds to more ccurte clssifiction for the pttern. Moreover, our lgorithm cn be implemented on progrmmble hrdwre bsed on the dvntge of the simplicity of texture feture extrction criteri. EFEENCES [] Tuceryn nd Jin, Texture Anlysis, in The Hndbook of Pttern ecognition nd Computer Vision, World Scientific, nd edn., 998. [] obert m. Hrlick, K. Shnmugm, nd its hl dinstein, texture feture for imge clssifiction, IEEE trnsction systems, November 973. [3] Wei Min Shi surfce roughness clssifiction using pttern recognition theory, opticl engineering, vol. 34 No. 6, June 995. [4] Sin-Wng-Sonei On-Line surfce defects Detection Using Wvelet-Bsed Prmeter Estimtion, Journl of Mnufcturing Science nd Engineering, Volume 5, Issue, pp. -8 Februry 003. [5] Shmuel Peleg, Texture Clssifiction Bsed on the Chnging in Imge Properties, iris.usc.edu/informtion/iris-conferences. My, 00. [6] Yun Zhng, Gng Hong, J. Bryn Mercer, Dn Edwrds nd Joel Mduck, A Wvelet Approch for the Fusion of dr Amplitude nd Opticl Multispectrl Imges, Multi-Conference on Systemics, Cybernetics nd Informtics, Orlndo, July 005. [7] Kung-Chyi, Accurte Estimtion of Surfce oughness from Texture Fetures of the Surfce Imge Using n Adptive Neuro-fuzzy Inference System, Precision Engineering, Elsevier, 6 My 004. [8] F. luk, nd V. H. North, Mesurement of Surfce oughness by Mchine Vision System, Journl of Physics, E. Scientific Instruments, , 989. [9] oberto Mrmo, Sbrin Amodio, Texturl identifiction of crbonte rocks by imge processing nd neurl network, universit di pvi@ vision. Unipv.it., 00 [0] S. Arivzhgn, L. Gnesn, S. Pdm Priyl, Texture clssifiction using Gbor wvelets bsed rottion invrint fetures, Pttern ecognition letters, Elsevier, 006. [].E.eson, O.B.E.,A..C.S.D.Sc. (Birm.) The mesurement of surfce texture, Mcmilln nd Co Ltd, 970. [] S.K. Hgr Choudhury, S.K. Bose nd A.K. Hgr Choudhury, Elements of Workshop technology, Medi Promotion & Publisher PVT LTD, Bomby, 99. [3] Chih-Ming Chen, Chien-Chng Chen nd Chur- Chin Chen A Comprison of Texture Fetures Bsed on SVM nd SOM, IEEE, 006. [4] Tylor Hobson precision A guide to surfce texture prmeters, info@tylor_hobson.de [5] Prithwijit Guh, Automted visul inspection of steel surfce, texture segmenttion nd development of perceptul similrity mesures, Indin institute of technology, knpur, April, 00. [6] F.J. Seinstr, D. Koelm, Modeling Performnce of low level imge processing routines on MIMD Computers, Kruisln 403, 098 SJ Amsterdm, The Netherlnds, fjseins@wins.uv.nl, my 004. [7] Ming-Huwi Horng, Texture fetures coding method for texture clssifiction, opt. eng. 4() 8-83, Jn [8] Ming-Huwi Horng, Texture fetures coding method for texture clssifiction, opt. eng. 4() 8-83 Jn ACIT 007, 6-8 November 007, Lttki, Syri 49.