SURFACE TEXTURE MEASUREMENT BY COMPUTER VISION. Y.J. Chao, C. Lee, M.A. Sutton, and W.H. Peters

Transcription

1 SURFAC TXTUR MASURMNT BY COMPUTR VSON SURFAC TXTUR MASURMNT BY COMPUTR VSON YJ Cho, C Lee, MA Sutton, nd WH Peters YJ Cho, C Lee, MA Sutton, nd WH Peters Assistnt Professor, Grdute Student, Assistnt Professor nd Associte Professor, Respectively Deprtment of Mechnicl ngineering University of South Crolin Columbi, SC 2928 Assistnt Professor, Grdute Student, Assistnt Professor nd Associte Professor, Respectively Deprtment of Mechnicl ngineering University of South Crolin Columbi, SC 2928 ABSTRACT A fully utomted, non -contcting, full field method is presented to mesure the surfce roughness prmeters using computer vision system nd imge processing techniques The texture fetures, nmely, corseness, contrst nd roughness re extrcted from digitized imges of stndrd surfce roughness comprtor pltes nd correlted with the rithmetic verge roughness (R) of the surfce The vision, system offers fst nd ccurte method for the on -line utomted surfce roughness inspection of mchined components NTRODUCTON Surfce texture of mchined components plys criticl role in the considertion of the ftigue life of the components, for the selection of lubricnts used between contcting mechnicl prts, etc The methods for mesurement of surfce texture cn be clssified s contct techniques nd non -contct techniques Opticl methods re normlly used in the non -contct mesurement of surfce texture However, they re limited to lbortory testing due to either the lborious preprtion of the specimen or the pprtus To dte, the most widely used stndrd method for surfce roughness mesurement is the stylus type surfce trcing technique known s "Profilometer " With the dvncement in the computer technology, dt obtined from stylus -type instruments cn now be nlyzed rpidly by microcomputers ttched to the stylus However, the dt cquisition prt is still the trditionl mechnicl stylus which gretly limits its ppliction for high volume inspection nd significntly reduces the productivity An ccurte knowledge of the surfce condition of mchined component provided in rel time would be very useful in industril pplictions An inexpensive but crude method is lso being used in industry by visully compring the smples with stndrd surfce roughness comprtor pltes to roughly estimte the surfce roughness This qulittive visul comprison method motivtes the ppliction of computer vision system insted of humn vision to quntittively correlte texturl fetures with surfce roughness prmeters To do so, certin texturl fetures which cn correlte with the rithmetic verge of surfce roughness must be found nd defined in dvnce n the study of texturl fetures tht correspond to humn vision perception, H Tmur, S Mori nd T Ymwki [] compred psychologicl mesurements from humn subjects with computtionl mesurements from six bsic texturl fetures, nmely, corseness, contrst, directionlity, linelikeness, regulrity, nd roughness Good correspondence hs been found in correltion of 6 typicl texture ptterns for the six texturl fetures The computtionl mesures were developed nd improved in order tht they my correspond to the psychologicl mesurements According to the psychologicl experiments on humn visul perception, the mesurement of roughness cn be pproximted by the effects of corseness nd contrst Therefore, the texturl fetures relted to the surfce roughness my be defined s the combintion of corseness nd contrst (eg, F = F F ) rgg crs con n this rticle, personl computer bsed computer vision system is used to tke the imges of the test specimens The intensity ptterns of the imges re digitized nd stored in the computer Texturl fetures of corseness nd contrst re extrcted nd roughness vlues cn be computed A correltion curve between the extrcted roughness fetures from the digitized imge nd the ctul physicl roughness prmeter is estblished The developed procedure nd results indicte tht it is fesible to utilize computer vision system for on -line surfce roughness inspection of mchined components DATA ACQUSTON AND A PRSONAL COMPUTR BASD VSON SYSTM The system configurtion utilized in this project is shown in figure The video digitizing system is minly supported by n BM PC /XT computer with single video cquisition nd disply bord plugged into one of the BM stndrd expnsion slots which provides the PC /XT with complete imge processing cpbility The system provides digitized imges with resolution of 384 pixels in the horizontl direction nd 52 pixels in the verticl direction The intensity vlues re recorded with 8 -bits per pixel ch pixel hs 256 gry levels with for fully blck nd 255 fully white The video bord lso provides the cpbility of high resolution grphics, motion detection, nd pttern recognition, etc User -friendly menu -driven softwre ws developed to optimize the function of the video bord This progrm fetures mny functions for the rel -time nlysis of imge dt The digitized imge cn be trnsmitted to VAX min computer ví seril communiction port for texture nlysis nd imge processing 32 / SP Vol 66 Opticl Testing nd Metrology (986) ABSTRACT A fully utomted, non-contcting, full field method is presented to mesure the surfce roughness prmeters using computer vision system nd imge processing techniques The texture fetures, nmely, corseness, contrst nd roughness re extrcted from digitized imges of stndrd surfce roughness comprtor pltes nd correlted with the rithmetic verge roughness (R) of the surfce The vision system offers fst nd ccurte method for the on-line utomted surfce roughness inspection of mchined components NTRODUCTON Surfce texture of mchined components plys criticl role in the considertion of the ftigue life of the components, for the selection of lubricnts used between contcting mechnicl prts, etc The methods for mesurement of surfce texture cn be clssified s contct techniques nd non-contct techniques Opticl methods re normlly used in the non-contct mesurement of surfce texture However, they re limited to lbortory testing due to either the lborious preprtion of the specimen or the pprtus To dte, the most widely used stndrd method for surfce roughness mesurement is the stylus type surfce trcing technique known s "Profilometer" With the dvncement in the computer technology, dt obtined from stylus-type instruments cn now be nlyzed rpidly by microcomputers ttched to the stylus However, the dt cquisition prt is still the trditionl mechnicl stylus which gretly limits its ppliction for high volume inspection nd significntly reduces the productivity An ccurte knowledge of the surfce condition of mchined component provided in rel time would be very useful in industril pplictions An inexpensive but crude method is lso being used in industry by visully compring the smples with stndrd surfce roughness comprtor pltes to roughly estimte the surfce roughness This qulittive visul comprison method motivtes the ppliction of computer vision system insted of humn vision to quntittively correlte texturl fetures with surfce roughness prmeters To do so, certin texturl fetures which cn correlte with the rithmetic verge of surfce roughness must be found nd defined in dvnce n the study of texturl fetures tht correspond to humn vision perception, H Tmur, S Mori nd T Ymwki [] compred psychologicl mesurements from humn subjects with computtionl mesurements from six bsic texturl fetures, nmely, corseness, contrst, directionlity, linelikeness, regulrity, nd roughness Good correspondence hs been found in correltion of 6 typicl texture ptterns for the six texturl fetures The computtionl mesures were developed nd improved in order tht they my correspond to the psychologicl mesurements According to the psychologicl experiments on humn visul perception, the mesurement of roughness cn be pproximted by the effects of corseness nd contrst Therefore, the texturl fetures relted to the surfce roughness my be defined s the combintion of corseness nd contrst (eg, F = F F ) rgg crs con n this rticle, personl computer bsed computer vision system is used to tke the imges of the test specimens The intensity ptterns of the imges re digitized nd stored in the computer Texturl fetures of corseness nd contrst re extrcted nd roughness vlues cn be computed A correltion curve between the extrcted roughness fetures from the digitized imge nd the ctul physicl roughness prmeter is estblished The developed procedure nd results indicte tht it is fesible to utilize computer vision system for on-line surfce roughness inspection of mchined components DATA ACQUSTON AND A PRSONAL COMPUTR BASD VSON SYSTM The system configurtion utilized in this project is shown in figure The video digitizing system is minly supported by n BM PC/XT computer with single video cquisition nd disply bord plugged into one of the BM stndrd expnsion slots which provides the PC/XT with complete imge processing cpbility The system provides digitized imges with resolution of 384 pixels in the horizontl direction nd 52 pixels in the verticl direction The intensity vlues re recorded with 8-bits per pixel ch pixel hs 256 gry levels with for fully blck nd 255 fully white The video bord lso provides the cpbility of high resolution grphics, motion detection, nd pttern recognition, etc User-friendly menu-driven softwre ws developed to optimize the function of the video bord This progrm fetures mny functions for the rel-time nlysis of imge dt The digitized imge cn be trnsmitted to VAX min computer vi seril communiction port for texture nlysis nd imge processing 32 / SP Vol 66 Opticl Testing nd Metrology (986) Downloded From: on 2/8/26 Terms of Use:

2 The computed results re presented on high resultion grphics terminl by pproprite grphic softwres A hrdcopy cn be obtined from plotter The computed results re presented on high resultion grphics terminl by pproprite grphic softwres A hrdcopy cn be obtined from plotter PRPROCSSNG AND XTRACTON OF TXTURAL FATURS n the ppliction of computer vision, the digitized pictoril dt is often preprocessed in order to enhnce the intrinsic structure or revoke redundnt sptil informtion nd noise which re present in the gry level rry n order to recover the intrinsic imge prmeters nd chieve specific purpose, severl ctegories of pre -processing techniques such s filtering, edge opertor, nd rnge trnsformtion [2], re normlly used to ccomplish the desired trnsformtion n the current cse, to eliminte the influence of non -uniform illumintion on the object which cn occur in rugged production environment, the pre -processing method of bckground subtrction is dopted in the nlysis Bsiclly, the technique djusts the originl imge f(x) with n pproximted bckground imge fb(x) to generte new imge fn(x) The input imge is first prtitioned into m squres of equl size For ech squre, n verge gry level is obtined These verge vlues form vector A On the other hnd, mtrix X with m rows by column is constructed form the displcements which re mesured from the center of ech of the squres to the center of the imge (x,y c) Here, is the number of coefficients tht depend upon the order of the regression surfce n this pper, second order regression surfce is used nd cn be expressed s: bl b2 X b3 Y b4 X2 b5 Y2 b6 XY = () By using the lest squre error criteri, set of coefficients representing surfce which best fits the vector A (ie, verge intensity levels) cn be found The coefficient vector b cn be computed by: b = (XT X)- XT A (2) where XT is the trnspose of X Finlly, the regression surfce represented by eqn () except the term b (DC component) is subtrcted from the originl imge Thus, the djusted imge which is modified to chieve homogeneous illumintion is obtined COARSNSS (Fcrs) XTRACTON Rosenfeld's best sized opertor is used in this project Bsiclly, the techniques uses vrious sized opertors to exmine vriety of corseness mesures [3] The procedure is first to sonstuct kx mtrix, B, in which the k elements re the verge gry levels over the neighborhood of size 2 * 2 (eg, *, 2 *2,, 32 *32) t point (x,y) The B t every point (x,y) in the re of interest must be clculted Then, for ech point, differences between pirs of verges corresponding to pirs of non -overlpping neighborhoods just on opposite sides of the point in both horizontl nd verticl orienttions re clculted nd only bsolute vlue of the differences should be considered The best size for ech point cn be obtined such tht the size gives highest bsolute vlue of difference in either horizontl or verticl direction After the best size for every point in the re of interest hs been clculted, the verge of the best size cn be obtined over the picture nd defined s the corseness F t should be noted tht this method is not size detector of texture elements, but tht the finl outpúfsf is influenced by both the frequency nd the width of the "spots" on the imge Hence it cn be used s ármesure of corseness CONTRAST (Fcon) XTRACTON The feture of the contrst of n imge is bsiclly defined s the gry level vrition of the digitized imges The fourth order contrst cn be defined s: Fcon = /( 4 ) PRPROCSSNG AND XTRACTON OF TXTURAL FATURS n the ppliction of computer vision, the digitized pictoril dt is often preprocessed in order to enhnce the intrinsic structure or revoke redundnt sptil informtion nd noise which re present in the gry level rry n order to recover the intrinsic imge prmeters nd chieve specific purpose, severl ctegories of pre-processing techniques such s filtering, edge opertor, nd rnge trnsformtion [2], re normlly used to ccomplish the desired trnsformtion n the current cse, to eliminte the influence of non-uniform illumintion on the object which cn occur in rugged production environment, the pre-processing method of bckground subtrction is dopted in the nlysis Bsiclly, the technique djusts the originl imge f(x) with n pproximted bckground imge f,(x) to generte new imge f (x) The input imge is first prtitioned into m squres of equl size For ech squre, n verge gry level is obtined These verge vlues form vector A On the other hnd, mtrix X with m rows by column is constructed form the displcements which re mesured from the center of ech of the squres to the center of the imge (x,y ) Here, is the number of coefficients tht depend upon the order of the regression surfce n this pper, second order regression surfce is used nd cn be expressed s: b x b X b Y b X 2 b Y 2 b 6 XY = () By using the lest squre error criteri, set of coefficients representing surfce which best fits the vector A (ie, verge intensity levels) cn be found The coefficient vector b cn be computed by: T T b = (X X) X A (2) T where X is the trnspose of X Finlly, the regression surfce represented by eqn () except the term b, (DC component) is subtrcted from the originl imge Thus, the djusted imge which is modified to chieve homogeneous illumintion is obtined COARSNSS (Fcrs) XTRACTON Rosenfeld! s best sized opertor is used in this project Bsiclly, the techniques uses vrious sized opertors to exmine vriety of corseness mesures [3] The procedure is first to construct kxl mtrix, B, in which the k elements re the verge gry levels over the neighborhood of size 2 * 2 (eg, *, 2*2, 32*32) t point (x,y) The B t every point (x,y) in the re of interest must be clculted Then, for ech point, differences between pirs of verges corresponding to pirs of non-overlpping neighborhoods just on opposite sides of the point in both horizontl nd verticl orienttions re clculted nd only bsolute vlue of the differences should be considered The best size for ech point cn be obtined such tht the size gives highest bsolute vlue of difference in either horizontl or verticl direction After the best size for every point in the re of interest hs been clculted, the verge of the best size cn be obtined over the picture nd defined s the corseness F t should be noted tht this method is not cr s size detector of texture elements, but tht the finl output F is influenced by both the frequency nd the width of the "spots" on the imge Hence it cn be used s mesure of corseness CONTRAST (Fcon) XTRACTON The feture of the contrst of n imge is bsiclly defined s the gry level vrition of the digitized imges The fourth order contrst cn be defined s: Fcon- /( 4>* (3) where is the stndrd devition nd is known s Kurtosis nd defined s: where o is the stndrd devition nd is known s Kurtosis nd defined s: (3) 4 = p4/v 2 (4) where y, is the fourth order moment bout the men nd is the vrince These lgorithms re chosen becuse they correspond to humn visul perception very well [] where 4 is the fourth order moment bout the men nd is the vrince These lgorithms re chosen becuse they correspond to humn visul perception very well [] ROUGHNSS (Frgh) XTRACTON ROUGHNSS (Frgh) XTRACTON The mesure of roughness cn be defined s the sum of the computtionl mesures of corseness nd contrst The mesure of roughness cn be defined s the sum of the computtionl mesures of corseness nd contrst F = F F (5) rgh con crs Frgh - Fcon Fcrs Different weighing fctors cn be incorported into eqn (5) for F nd F However, they re not included in the results shown in this pper C n Different weighing fctors cn be incorported into eqn included in the results shown in this pper (5) for F nd F con crs ' However, they re not (5) SP Vol 66 Opticl Testing nd Metrology (986) / 33 SP Vol 66 Opticl Testing nd Metrology (986) / 33 Downloded From: on 2/8/26 Terms of Use:

3 DATA ANALYSS AND DSCUSSON DATA ANALYSS AND DSCUSSON The imges were tken nd digitized from stndrd surfce roughness comprtor pltes tht re normlly used for vision comprison of surfce roughness mesures They re mnufctured by sprk erosion which genertes rndom pttern of surfce texture Figures 2 nd 2b show two typicl ptterns with roughness R = 32 ym nd 63 ym, respectively t is evident tht there is qulittive difference between these two pictures Figure 3 represents the histogrms of the digitized imges of the corresponding smples shown in figure 2 t shows drop in the pek of the histogrm nd lso n increse in the bndwidth This observtion is expected nd cn be explined s follows As the test smple becomes rougher, it sctters more light nd introduces decrese of totl intensity nd drop of the pek in the histogrm The increse of bndwidth indictes the increse of contrst The imges were tken nd digitized from stndrd surfce roughness comprtor pltes tht re normlly used for vision comprison of surfce roughness mesures They re mnufctured by sprk erosion which genertes rndom pttern of surfce texture Figures 2 nd 2b show two typicl ptterns with roughness R = 32 pm nd 63 pm, respectively t is evident tht there is qulittive difference between these two pictures Figure 3 represents the histogrms of thé digitized imges of the corresponding smples shown in figure 2 t shows drop in the pek of the histogrm nd lso n increse in the bndwidth This observtion is expected nd cn be explined s follows As the test smple becomes rougher, it sctters more light nd introduces decrese of totl intensity nd drop of the pek in the histogrm The increse of bndwidth indictes the increse of contrst Four imges were tken rndomly from ech smple with vrious surfce roughness (R = 4, 8, 6, 32, 63, 25, 25, 5 ym) t mgnifiction fctor *32 nd n perture f=4 This set-up covers physicl region of 52 mm in the horizontl nd 393 mm in the verticl direction Digitized imges were then nlyzed by the lgorithms described previously to extrct fetures of corseness (F ), contrst (F ), nd roughness (F, ) bsed on 384 H * 256 V subimge of the whole imge Tbles, 2, nd 3 presents the dt of extrcted fetures from ech smple For ll three fetures, the stndrd devition flls under 6 except for the cse of R = 25 ym nd 5 ym t is primrily due to the fct tht for these high roughness smples, the digitized res re not lrge enough to include sufficient informtion This sitution cn be corrected by reducing the mgnifiction of the imging system Four imges were tken rndomly from ech smple with vrious surfce roughness (R = 4, 8, 6, 32, 63, 25, 25, 5 pm) t mgnifiction fctor *32 nd n perture f =4 This set -up covers physicl region of 52 mm in the horizontl nd 393 mm in the verticl direction Digitized imges were then nlyzed by the lgorithms described previously to extrct fetures of corseness (F ), contrst (F ), nd roughness (F,) bsed on 384 H * 256 V subimge of the whole imge Tbles r!, nd 3 presensnthe dt of extrctedrgretures from ech smple For ll three fetures, the stndrd devition flls under 6 except for the cse of R = 25 im nd 5 pm t is primrily due to the fct tht for these high roughness smples, the digitized res re not lrge enough to include sufficient informtion This sitution cn be corrected by reducing the mgnifiction of the imging system The corresponding figures of these extrcted fetures re plotted with respect to roughness RMS vlue in figures 4, 5, nd 6 t is noticed tht the feture F, shows monotoniclly scending trend with respect to the roughness vlue except for cse of R =58 ym The computed roughness feture, figure 6, provides clibrtion curve which llows one to inspect the surfce roughness condition of mchined component in rel-time The corresponding figures of these extrcted fetures re plotted with respect to roughness RMS vlue in figures 4, 5, nd 6 t is noticed tht the feture F shows monotoniclly scending trend with respect to the roughness vlue except for cse of R = 5r g pm The computed roughness feture, figure 6, provides clibrtion curve which llows one to inspect the surfce roughness condition of mchined component in rel -time CONCLUSON CONCLUSON A computer vision system combined with imge processing techniques is proposed to extrct fetures of roughness, corseness nd contrst which cn be used to correlte with RMS of mchined components t provides full-field nd ccurte technique to inspect surfce roughness in n on-line fshion The effects of chnge of prmeters of the imging system nd the illumintion conditions re currently under investigtion Smples mde of other mnufcturing processes thn the sprk erosion re lso under study by the uthors A computer vision system combined with imge processing techniques is proposed to extrct fetures of roughness, corseness nd contrst which cn be used to correlte with RMS of mchined components t provides full -field nd ccurte technique to inspect surfce roughness in n on -line fshion The effects of chnge of prmeters of the imging system nd the illumintion conditions re currently under investigtion Smples mde of other mnufcturing processes thn the sprk erosion re lso under study by the uthors ACKNOWLDGMNTS ACKNOWLDGMNTS The work ws supported in prt by the Ntionl Science Foundtion through grnt MSM nd the University of South Crolin through the Crolin Venture Fund Their support is gretly pprecited The work ws supported in prt by the Ntionl Science Foundtion through grnt MSM nd the University of South Crolin through the Crolin Venture Fund Their support is gretly pprecited RFRNCS RFRNCS H Tmur, S Mori nd T Ymwki, "Texturl Fetures Corresponding to Visul Perception", Trns of Systems, Vol 8, No 6, pp , June 97 2 Bllrd, DH nd Brown, CM, Computer Vision, Prentice Hll, KC Hyes, Jr, AN Shh, nd A Rosenfeld, "Texture Corseness: Further xperiments", Trns Syst Mn, Cybern, Vol SMC-4, pp , Sept 974 H Tmur, S Mori nd T Ymwki, "Texturl Fetures Corresponding to Visul Perception ", Trns of Systems, Vol 8, No 6, pp , June 97 2 Bllrd, DH nd Brown, CM, Computer Vision, Prentice Hll, KC Hyes, Jr, AN Shh, nd A Rosenfeld, "Texture Corseness: Further xperiments ", Trns Syst Mn, Cybern, Vol SMC -4, pp , Sept / SP Vol 66 Opticl Testing nd Metrology (986) 34 / SP Vol 66 Opticl Testing nd Metrology (986) Downloded From: on 2/8/26 Terms of Use:

4 HSTOGRAM 2 B 6 [lub-28] G-28 BM PC/XT BM DGTZNG SYSTM SYSTM DGTZNG -(TV MONTOR) RS-232 [RS-232] CCD CAMRA KH F F RP Q 2 o U NN CC 8 Y VAX /78 VAX /78 MAN COMPUTR (MAN COMPUTR 4 / SLANAR,R HiRes (SLANARHiRes GRAPHC TRMNALJ TRMNA GRAPHC 2! St Stndrd Stndrd Roughness Roughness Comprtor Pltes CornprtorPltes (HP 747A PLOTTR Figure BH 24 P GRAY GRAY LVL Figure 3 3 System Configurtion Histogrms the ptterns in in Fig Fig 2 2 Histogrms of the Fcrs, ii V v i r r r dev vq vg t i R ( urn ) Rttn) , ! 674 Tble Corseness Feture Fc on Fcon! 2() 2() t 4 R < urn ) 8(u ! Î V 3937 r 3883 T dev v'<3 vg , T ! , , i , , Tble 2 2 2(b) 75! Contrst Contrst Feture Frgh == Fcrs Fcrs Fcon Fcon!Witt, R(um) bure) t! ll ' Figure Figure 2 2 Typicl Surfce Surfce Roughness Roughness Ptterns Ptterns with R = um urn nd nd um urn , vg dev vgdev ! V ! Tble Tble 3 3 Feture Roughness Feture SP Vol 35 SP Vol66 66 Opticl Opticl Testing Testingnd ndmetrology Metrology(986) (986)// 35 Downloded From: on 2/8/26 Terms of Use:

5 SUBMAG (256,5,384,256) SUBMAG ( * # L T ROUGHNSS ( P, urn C ROUGHNSS R, ( um i 5 5 Figure 4 Correltion Between F nd Roughness Figure 4 Correltion Between F crs nd Roughness SUBMAG (256,5,384,256! SUBMAG (2565, ~j 2 6 C o n ROUGHNSS ( R, urn ) ROUGHNSS Rt um 5 Figure 5 Correltion Between F nd Roughness Figure 5 Correltion Between Fcon nd Roughness SUBMAG (256,5,384,256! SUBMAG ( r 9 h ROUGHNSS R, ( um ROUGHNSS ( R, urn ) S Figure 6 Correltion Between Frgh nd Roughness Figure 6 Correltion Between F, nd Roughness 36 / SP Vol 66 Opticl Testing nd Metrology (986) 36 /SPVol 66 Opticl Testing nd Metrology (986) Downloded From: on 2/8/26 Terms of Use: