Research article. 1. Introduction

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1 Research aricle Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai Mechanical Indusry Research Laboraories, Indusrial Technology Research Insiue, Taiwan, Republic of China, and Du-Ming Tsai, Wei-Chen Li, Wei-Yao Chiu and Ming-Chin Lin Deparmen of Indusrial Engineering and Managemen, Yuan-Ze Universiy, Taiwan, Republic of China Absrac Purpose The purpose of his paper is o develop a robo vision sysem for surface defec deecion of 3D objecs. I aims a he ill-defined qualiaive iems such as sains and scraches. Design/mehodology/approach A robo vision sysem for surface defec deecion may couner: high surface reflecion a some viewing angles; and no reference markers in any sensed images for maching. A filering process is used o separae he illuminaion and reflecion componens of an image. An auomaic marker-selecion process and a emplae-maching mehod are hen proposed for image regisraion and anomaly deecion in reflecion-free images. Findings Tess were performed on a variey of hand-held elecronic devices such as cellular phones. Experimenal resuls show ha he proposed sysem can reliably avoid reflecion surfaces and effecively idenify small local defecs on he surfaces in differen viewing angles. Pracical implicaions The resuls have pracical implicaions for indusrial objecs wih arbirary surfaces. Originaliy/value Tradiional visual inspecion sysems mainly work for wo-dimensional planar surfaces such as prined circui boards and wafers. The proposed sysem can find he viewing angles wih minimum surface reflecion and deec small local defecs under image misalignmen for hree-dimensional objecs. Keywords Roboics, Elecronic equipmen and componens, Surface defecs, Sensors Paper ype Research paper 1. Inroducion Non-conac visual inspecion based on image analysis echniques has become imporan for qualiaive evaluaion of surface qualiy in manufacuring. Defecs ha appear as local anomalies, such as sains, scraches and wear, on maerial/ produc surfaces generally lack quaniaive measures o define. They are also difficul o idenify if he surrounding background conains complex paerns. Auomaed visual inspecion has been successfully applied o a wide variey of maerial surfaces found in indusry, such as prined circui boards (PCBs) (Mogan and Ercal, 1996; Yeh and Tsai, 2001; Lea e al., 2008), semiconducor wafers (Su e al., 2002; Shankara and Zhongb, 2005; Yeh e al., 2010), liquid crysal display panels (Oh e al., 2004; Zhang and Zhang, 2005; Tsai and Tsai, 2010), solar cells (Ordaz and Lush, 2000; Fu e al., 2004; Tsai e al., 2010) and exile fabrics (Cho e al., 2005; Kumar, 2008; Xie, 2008). For PCB inspecion, embedded fiducial markers on he boards allow The curren issue and full ex archive of his journal is available a 38/4 (2011) q Emerald Group Publishing Limied [ISSN X] [DOI / ] he image under inspecion o be regisered wih respec o he golden emplae for comparison. For semiconducor wafer inspecion, he surface shows repeiive die paerns in he image so ha he defec can be idenified when i breaks he periodiciy on he surface. The visual inspecion asks for maerial surfaces menioned above can be carried ou wih one single view of he camera. Tha is, all hese indusrial producs can be reaed as wo-dimensional (2D) planar surfaces and, herefore, he inspecion ask can be compleed in a 2D image. Very recenly, high-end hand-held elecronic devices such as cellular phones and digial cameras demand high surface qualiy of he end producs before hey can be shipped o cusomers. Any appearance defecs may degrade he value of he producs. The hand-held devices are, by necessiy, hreedimensional (3D) objecs, and radiional visual inspecion sysems based on one single view canno be applied o surface defec deecion of 3D objecs. The exising visual inspecion sysems for 3D objecs mainly focus on dimension measuremens using muliple views of fixed cameras or a flexible camera mouned on a robo arm (Neo and Nehmzow, 2007; Heizmann, 2009; Saar and Brenner, 2009). The focus is on he geomeric measuremens of he 3D objec, no on he qualiaive properies of he objec surfaces. In his paper, we propose a robo vision sysem for surface defec deecion of 3D objecs, wih a specific aim oward hand-held elecronic devices. The sensing camera is mouned 381

2 Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. on he end-effecor of a robo arm. I allows he 3D objec under inspecion o be flexibly observed from differen angles and enables all surfaces of ineres o be evaluaed. Since he arge producs o be inspeced may conain separae uniform (non-exured), exured, and paerned surfaces, he image of each viewing angle of a defec-free objec is firs aken and sored as a emplae for comparison. For each new objec under esing, he same views are repeaed by he robo, and each sensed image is compared o he corresponding emplae image. A emplae-maching echnique is hen used o idenify local defecs in he wo compared images a each viewing angle. Applicaion of he robo vision sysem for surface defec deecion of 3D objecs raises wo main problems o overcome: 1 The cases of he high-end elecronic devices are generally made of meallic or plasic maerials wih very smooh surfaces. They can be highly reflecive on he surface a some viewing angles. The reflecion region in he image could be falsely deeced as a local defec, or he reflecion may conceal a defec on he surface, resuling in misdeecion. 2 Unlike he PCBs, he final appearance of an elecronic produc does no have embedded fiducial markers for image regisraion. The image aken from an individual viewing angle by he robo in a repeiive process may presen displacemen wih respec o he emplae image. In his sudy, we firs propose a reflecion-deecion process o idenify he presence/absence of reflecion in an image so ha he reflecion-free viewing angle of he camera-equipped robo can be auomaically deermined. Second, an auomaic marker-selecion process for each individual image of a defecfree reference objec is presened for image regisraion, and hen a emplae-maching algorihm ha is oleran of displacemen beween wo compared images is proposed o deec local anomalies. All hese effors make he robo vision sysem pracical for surface defec deecion of 3D objecs. The paper is organized as follows: Secion 2 describes he configuraion of he robo vision sysem. Secion 3 inroduces he view planning and reflecion deecion in images. Secion 4 presens he auomaic marker-selecion and emplae-maching processes for defec deecion. Secion 5 discusses he experimenal resuls. Various hand-held devices are used o evaluae he efficacy and feasibiliy of he proposed vision inspecion sysem. Secion 6 concludes he paper. 2. Sysem configuraion The robo vision sysem in our work is implemened on he Denso robo (model VS-6556G). I is a six-axis ariculaed robo wih a repeaabiliy of ^0.02 mm. The charge-coupled device camera is mouned on he end-effecor of he robo. A ring-shaped ligh-emiing diode (LED) wih a diffusion dome is used as he ligh source. The ring ligh cass ligh from all direcions o creae more uniform and shadow-free illuminaion. The diffuse dome ligh furher reduces specular reflecions on he objec surface. The LED dome ligh is direcly aached o he camera so ha i can move along wih he robo arm. The image aken by he camera is 1,600 1,200 pixels in size wih 8-bi gray levels. Figure 1 shows he configuraion of he robo vision sysem implemened in his sudy. Figure 1 Configuraion of he robo vision sysem Denso robo CCD camera LED domeligh 3. View planning and reflecion deecion View planning for reconsrucion and inspecion of 3D objecs wih muliple viewing angles has been exensively sudied over he pas wo decades (Willam e al., 2003; Chen and Li, 2002, 2004; Munkel e al., 2009). The objecive of view planning of a robo pah is o find he minimum number of images from differen viewing angles ha will cover all he required surfaces o reconsruc/inspec he enire 3D objec. The inspecion ask in view planning is quaniaive measuremens of geomeric dimensions. I generally assumes he objec o be modeled is enclosed by a spherical shell. The view planning is widely based on he nex bes view (NBV) (Wong e al., 1998), which deermines a new sensor posiion ha gives he larges unseen area of he objec. The opimaliy crierion of view planning is mainly based on oal visible edge lengh or oal surface area (Shin and Gerhard, 2006). I ignores he possibiliy ha he image obained from he NBV may show severe reflecion on he surface. The proposed reflecion-deecion algorihm in his paper can be used as a consrain in he view planning model. View planning can hus find a minimum number of required views wihou showing reflecions in all seleced images. However, since deailed view planning is beyond he scope of his sudy, we focus on reflecion deecion for qualiaive inspecion of 3D objec surfaces. Since he arge objecs o be inspeced in his sudy are small hand-held devices wih simple curved surfaces, we implemen a sraighforward form of view planning o obain he required views for our robo vision sysem. I is also assumed ha he 3D objec is enclosed in a sphere. All he surfaces of a hand-held produc can be observed from op views, fron and rear views, and righ- and lef-side views. We herefore need only wo orhogonal scan rajecories along he spherical shell o obain he required viewing angles. Le he opical-axis of he end-effecor-mouned camera be perpendicular o he able surface and poin o he cener of he objec in he sraigh op-view image. Denoe by B(x, y) he binary image of he op-view image of size M N wih 382

3 Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. B(x,y) ¼ 1 for objec poins and 0 for background poins. The objec o be inspeced can be placed on a high-conras background, and a simple auomaic binary hresholding echnique such as Osu s mehod (Gonzalez and Woods, 1992) can be applied o segmen he gray-level image ino a binary image. The cener is hen given by: x ¼ y ¼ P x P x 1 XX P x Bðx; yþ y Bðx; yþ x y 1 XX P y Bðx; yþ y Bðx; yþ x y The wo orhogonal direcions of he objec in he 2D opview image can be derived from he principal componen analysis. Le M be he covariance marix of he objec in he op-view image, and: " # m xx m xy M ¼ ð1þ m xy m yy where: " 1 XX # m xx ¼ P P x y Bðx; yþ x2 Bðx; yþ 2 x 2 m xy " x y 1 XX # ¼ P P x y Bðx; yþ x y Bðx; yþ 2 x y m yy " x y 1 XX # ¼ P P x y Bðx; yþ y2 Bðx; yþ 2 y 2 x y The eigenvecors e 1 and e 2 of he covariance marix M give he wo scan direcions of he orhogonal rajecories in he view sphere. Figure 2 shows he view sphere and he orhogonal scan rajecories of he robo vision sysem. For a given viewing angle in he scan rajecory, we need o deec he presence/absence of surface reflecion in he sensed image. Any views ha produce subsanial reflecion in he image mus be discarded, since hey show no surface informaion in he reflecion regions. Deecion of a reflecion region in he image is no rivial ask. One canno simply use a hresholding echnique and idenify whie pixels as pars of he reflecion region in he image. For hand-held elecronic devices wih fine meallic or plasic cases, reflecions on he objec Figure 2 Skech of he orhogonal scan rajecories in he view sphere surfaces depend highly on he sensing and lighing angles of he end-effecor-mouned camera and ligh source. Le he viewing angle of he camera be defined as he angle beween he opical-axis of he camera and he able surface. The inclined angle is measured clockwise from he able surface and is, hus, beween 08 and Figure 3(a) and (b) shows he images of a baery charger a he viewing angles of 808 and 988, respecively. Owing o he curved surface of he objec, he surface is highly reflecive. In Figure 3(a), he reflecion region conceals he prined characers and may cause false alarms. In conras, when he viewing angle of he camera is inclined from 808 o 988, as shown in Figure 3(b), he reflecion region is shifed from he lef o he righ of he objec surface and he scrach defec is no visible in he image. In Figure 3(c), he surface deails of he prined characers and he scrach defec are well presen wih minimum reflecion when he viewing angle of he camera is se a For a given image a a specific viewing angle, he observed image inensiy a a pixel locaion (x, y) is generaed by incoming illuminaion and is refleced by he surface of he objec. The observed image f(x, y) can hus be modeled as he produc of he illuminaion f i (x, y) from he ligh source and he reflecion f r (x, y) of he objec surface (Phong, 1975), i.e.: f ðx; yþ ¼f i ðx; yþ f r ðx; yþ ð2þ Figure 3 Baery charger images wih ligh reflecions a differen viewing angles (a) 80 (b) 98 End-effecor mouned CCD camera View sphere Scan rajecory given by e 2 Scan rajecory given by e 1 (c) 121 e 1 Norm Top-view of objec Table-op e 2 Noes: (a) Image a viewing angle 80, wih he prined characers concealed; (b) image a viewing angle 98, wih he scrach concealed; (c) image a viewing angle 121, wih all surface deails visible 383

4 Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. Assuming he scene illuminaion componen f i (x, y) varies slowly over space and he reflecion componen f r (x, y) conains high-frequency deails, a homomorphic filer (Moloney, 1991; Toh e al., 2000; Radke e al., 2005) can be used o separae he wo componens of he observed image. By aking he logarihm on boh sides of he model in equaion (2), we obain: ln ½ f ðx; yþš ¼ ln ½ f i ðx; yþš þ ln ½ f r ðx; yþš The image becomes addiive in he log-ransform space. The low-frequency illuminaion componen can hen be separaed by a low-pass filer, i.e.: ln ½ f i ðx; yþš ¼ LP{ln ½ f ðx; yþš} and he reflecion componen is given by: ln ½ f r ðx; yþš ¼ ln ½ f ðx; yþš 2 LP{ln ½ f ðx; yþš} where LP{ } is a Gaussian low-pass filer on he sensed image f(x,y). In his sudy, p a Gaussian filer of size wih scale parameer s ¼ ffiffiffi 2 is used for low-pass filering, i.e.: XX LPfln ½ f ðx; yþšg ¼ ln ½ f ðx þ i; y þ jþš i j ð3þ ð4þ ð5þ exp 2 i2 þ j 2 ð6þ 2s 2 The final reflecion image can hus be esimaed by: f r ðx; yþ ¼exp{ln ½ f r ðx; yþš} The reflecion region in he sensed image is deeced from he difference beween he original image f(x, y) and he esimaed reflecion image f r (x, y). Tha is: Df ðx; yþ ¼jf ðx; yþ 2 f r ðx; yþj Since he reconsruced image f r (x, y) may have an inensiy scale differen from ha of f(x, y), a simple saisical conrol limi is used o se up he hreshold for segmening he reflecion pixels in he resuling difference image Df(x, y). The reflecion hreshold is given by: T Df ¼ m Df þ K s Df ð9þ where m Df and s Df are he mean and sandard deviaion of he whole difference image of size M N, i.e.: m Df ¼ 1 MN x X X Df ðx; yþ ( ) 1=2 1 X s Df ¼ ½Df ðx; yþ 2 mdf Š 2 MN x X y Parameer K is a conrol consan, which gives he muliplicaion of he sandard deviaion from he mean. I is generally se o a fixed value beween 1 and 3, depending on he surface maerial of he 3D objec. A pixel a coordinaes (x, y) wih Df(x, y) larger han he hreshold T Df is classified as a reflecion poin. Oherwise, no reflecion poin is declared. To visualize he deecion resul of he reflecion region, a deeced reflecion poin is shown in whie and a nonreflecion poin is shown in black in he binary image. y ð7þ ð8þ Figure 4(a1)-(c1) shows he images of he baery charger a viewing angles 808, 988 and Figure 4(a2)-(c2) shows, respecively, he esimaed reflecion images of Figure 4(a1)-(c1). The deecion resuls of reflecion regions wih he conrol consan K ¼ 1 are shown as binary images in Figure 4(a3)-(c3), wherein he reflecion region in each of he original images a differen viewing angles is accuraely segmened. Figure 5(a1) shows he rear surface of a cellular phone a a viewing angle of 908. The cover of he phone is made of whie leaher wih wo black sripes on i. Figure 5(b1) is he same rear surface of he cellular phone aken a an inclined angle of 858. Figure 5(a2) and (b2) is he corresponding reflecion images, and Figure 5(a3) and (b3) is he respecive reflecion regions deeced wih a conrol consan K ¼ 1.5. The deecion resul in Figure 5(a3) shows ha he proposed reflecion-deecion mehod will no classify he pure whie region of a surface as reflecion. The highly reflecive regions on he wo black leaher sripes can be reliably deeced, as shown in Figure 5(b3). 4. Image regisraion and emplae maching A hand-held device may presen various surfaces on he cases, such as uniform/non-exured, exured, paerned and prined characer regions. A emplae-maching echnique ha compares he similariy (or dissimilariy) beween he defec-free emplae image and he scene image under es is possibly he only way o deec defecs in a complex surface conaining paerns or shaped figures. To apply emplae maching for defec deecion, he wo compared images mus be accuraely regisered. The robo arm can posiion repeaedly he end-effecormouned camera o each viewing angle rained from he defec-free emplae objec. The repeaabiliy of he robo and posiioning of he objec on he able may resul in minor displacemen beween he emplae image and he es image a a given viewing angle. The appearance of a final produc does no have fiducial markers on each sensed image of he 3D objec for regisraion. Therefore, we firs propose an auomaic marker-selecion process o deermine he mos disinguishable markers for each emplae image a a given viewing angle, and hen presen a emplae-maching mehod o find local anomalies beween he emplae image and he regisered es image. 4.1 Marker selecion and image regisraion To find he ranslaion and roaional angle beween wo images, we need a leas wo markers in each emplae image. We firs divide he emplae image a a given viewing angle ino four equal subimages and choose one marker for each subimage. The four markers should represen he riches and mos unique feaures in he individual subimages. Therefore, we selec he window wih he maximum local gradien in each subimage as he marker. In his sudy, he window size of he marker is pixels. The seleced markers will be used aferward o mach he insances of he markers in he es image. The gradien quanifies he changes of gray levels beween neighboring pixels. We apply a simple 2 2 edge operaor o calculae he gradien due o is compuaional simpliciy. Given an image f(x, y), he gradiens a coordinaes (x, y) in he x-and y-axis are given by g x and g y, where: 384

5 Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. Figure 4 Deecion of reflecion regions of he baery charger a differen viewing angles (a1) (a2) (a3) (b1) (b2) (b3) (c1) (c2) Noes: (a1)-(c1) Baery charger images a viewing angles 80, 98 and 121, respecively; (a2)-(c2) respecive esimaed reflecion images; (a3)-(c3) respecive deeced reflecion regions in he segmened images wih conrol consan K = 1 (c3) Figure 5 Deecing reflecion on whie-leaher surfaces (a1) (a2) (a3) (b1) (b2) (b3) Noes: (a1) Leaher cover image wihou ligh reflecion a viewing angle 90 ; (b1) leaher cover image wih ligh reflecion a viewing angle 85 ; (a2) and (b2) respecive reflecion images of (a1) and (b1); (a3) and (b3) respecive deeced reflecion regions 385

6 g x ðx; yþ ¼ f < j f ðx þ 1; yþ 2 f ðx; yþj x g y ðx; yþ ¼ f < j f ðx; y þ 1Þ 2 f ðx; yþj y In order o selec he marker wih rich edge feaures, we sum up he gradien magniudes in he window in individual x- and y-axis, and obain X G x X and G y : G x ðx; yþ ¼ gx ðx þ i; y þ jþ ðx * ; y * Þ¼arg i j XX G y ðx; yþ ¼ gy ðx þ i; y þ jþ i j max{g x ðx; yþ G y ðx; yþ; ;ðx; yþ [ Subimage} ð10þ (x *, y * ) is he cenral coordinaes of he seleced marker. Noe ha we do no use he sum, bu he produc, of G x and G y,o preven he selecion of a high-conras sraigh line as he marker. Once four markers are chosen from individual subimages, we reain only he wo wih he larges muliplicaive gradiens as he final fiducial markers for image regisraion. As shown in Figure 6, he image is divided ino quadrans. The four square frames are he seleced markers for individual subimages, and he wo soli-square frames ha presen mos unique paerns in he objec surface are he final markers used for regisraion. Le (x T,1, y T,1 )and(x T,2, y T,2 ) be he ceners of he wo seleced marker windows in he emplae image and (x S,1, y S,1 )and (x S,2, y S,2 ) he corresponding mached poins in he es image based on he evaluaion of he normalized cross-correlaion (NCC) in a small search region. The ranslaions in he x- and y-axis are hen given by: Dx ¼ 1 2 ðx S;1 þ x S;2 Þ ðx T;1 þ x T;2 Þ Dy ¼ 1 2 ðy S;1 þ y S;2 Þ ðy T;1 þ y T;2 Þ The roaional angle Du is obained from: where: Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. Du ¼ u S 2 u T u T ¼ an 21 y T;1 2 y T;2 x T;1 2 x T;2 u S ¼ an 21 y S;1 2 y S;2 x S;1 2 x S;2 Figure 6 Auomaic marker selecion in he quadrans of an image ð11þ ð12þ The scale facors C x and C y in he x-andy-axis are derived from: C x ¼ jx S;1 2 x S;2 j jx T;1 2 x T;2 j C y ¼ jy S;1 2 y S;2 j jy T;1 2 y T;2 j ð13þ Once he calibraion parameers (Dx, Dy), Du and (C x,c y )are obained, he image under inspecion can be regisered wih respec o is emplae image. The proposed marker-selecion crierion is very simple and effecive. I finds he maximum gradien produc in he x- and y-axis direcions so ha no high-conras sraigh line will be seleced as he marker. I can effecively find he corner of an objec wih uniform surface as he marker. I will also find he region ha conains many edges wih differen direcions in a complicaed surface. The chosen markers need no conain he riches feaures in he whole image as long as hey are unique and do no repea in a small neighborhood window. In order o evaluae he effeciveness of he markerselecion crierion, wo es samples are used for image regisraion. Figure 7(a) shows he emplae image of a diecas model jeep wih muliple surfaces a a specific viewing angle. Figure 7(b1) and (c1) is he counerpars of (a) wih a 10-pixel ranslaion and 38 roaion, respecively. Noe ha he repeaabiliy of he robo sysem implemened in his sudy is 0.02 mm, which corresponds o only 0.2 pixels in he image. The whie doed squares in Figure 7(a) are he markers chosen by he proposed selecion crierion. The doed squares in Figure 7(b1) and (c1) are he insances mached wih he emplae markers. Figure 7(b2) shows he absolue difference of gray levels beween he emplae image (a) and he ranslaed image (b1), where he brighness is proporional o he magniude of gray-level difference. The difference image indicaes ha he miniaure jeep presens significan displacemen. Figure 7(c2) shows he difference image beween he emplae image (a) and he roaed image (c1). When he ranslaed and roaed images are regisered by maching he markers, he difference images shown in Figure 7(b3) and (c3) indicaes ha he proposed markerselecion and image-regisraion processes can well aligned he image under large shifs and roaions. Figure 8 furher shows he regisraion resuls of a mouse wih simple curved surfaces, where image (a) is he emplae, image (b1) is ranslaed by 10 pixels and image (c1) is roaed by 38 wih respec o he emplae. Images in Figure 8(b2) and (c2) are he difference images before he image regisraion is applied. The misaligned pars are deeced around he objec edges. Afer he es images are well regisered wih he proposed process, he difference images for images (b1) and (c1) are uniformly black and no misaligned poins are presen, as shown in Figure 8(b3) and (c3). Regisraion markers Subimage Subimage Markers 4.2 Templae maching In order o deec he difference (i.e. a poenial defec) beween he emplae image and he regisered scene image, a emplae-maching process is required o compare he similariy (or dissimilariy) pixel by pixel. The emplaemaching mehod should be robus o minor misalignmen even in a regisered image. In his sudy, we evaluae a fas sum of absolue differences (SAD) mehod, a widely used NCC measure, and a newly proposed opical-flow maching. The SAD mehod is a simple algorihm for finding he similariy beween wo small image blocks. I akes he absolue difference beween each pixel in he emplae block 386

7 Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. Figure 7 Marker selecion and image regisraion for a diecas model jeep (a) (b1) 10-pixel ranslaion (c1) 3 -roaion (b2) Before regisraion (c2) Before regisraion (b3) Afer regisraion (c3) Afer regisraion Noes: (a) Templae image; (b1) es image wih a 10-pixel ranslaion; (c1) es image wih a 3 roaion; (b2) and (b3) difference images beween (a) and (b1) before and afer regisraion, respecively; (c2) and (c3) difference image beween (a) and (c1) before and afer regisraion, respecively; (he whie doed squares show he seleced markers and he mached insances Figure 8 Marker selecion and image regisraion for a mouse (a) (b1) 10-pixel ranslaion (c1) 3 - roaion (b2) Before regisraion (c2) Before regisraion (b3) Afer regisraion (c3) Afer regisraion Noes: (a) Templae image; (b1) es image wih a 10-pixel ranslaion; (c1) es image wih a 3 roaion; (b2) and (b3) difference images beween (a) and (b1) before and afer regisraion, respecively; (c2) and (c3) difference image beween (a) and (c1) before and afer regisraion, respecively; (he whie doed squares show he seleced markers and he mached insances 387

8 Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. and he corresponding pixel in he es block. These differences in he block are hen summed up and creae a simple similariy measure. The SAD value a pixel coordinaes (x, y) is given by: XX SADðx; yþ ¼ j f T ðx þ i; y þ jþ 2 f S ðx þ i; y þ jþj ð14þ i j 2 f x1 A ¼ f x2 f xn f y1 f y2.. f yn 3 2 " ; v ¼ u # ; b ¼ 7 v f 1 f 2.. f n where f T (x, y) and f S (x, y) are he gray levels a (x, y) in he emplae image and he regisered scene image, respecively. Coordinaes (x þ i, y þ j) are he neighboring pixels of (x, y). A pixel (x, y) is classified as a defec poin if: SADðx; yþ. m SAD þ C s SAD ð15þ where m SAD and s SAD are he mean and sandard deviaions of SAD values in he whole image. For NCC, he correlaion coefficien is used as he similariy measure for each pixel defined in a small neighborhood window. The correlaion coefficien a pixel (x, y) is defined as: P P i j dðx; yþ ¼ ½ f T ðx þ i; y þ jþ 2 f T ðx; yþš ½ f Sðx þ i; y þ jþ 2 f Sðx; yþš np i Pj ½ f T ðx þ i; y þ jþ 2 f T ðx; yþš 2 P o 1=2 j =P j ½ f Sðx þ i; x þ jþ 2 f Sðx; yþš 2 ð16þ where f T ðx; yþ and f S ðx; yþ are he mean gray levels of he neighborhood windows in he emplae image and he regisered scene image, respecively. A perfec mach of wo idenical image blocks will give a mach score of 1. A pixel (x, y) wih correlaion coefficien d(x, y) is classified as a defec poin if: dðx; yþ, m d 2 C s d ð17þ where m d and s d are he mean and sandard deviaions of correlaion coefficiens d in he whole image. The convenional maching mehods of SAD and NCC measures are sensiive o objec edges in he image. In his sudy, we also propose an opical-flow-based maching mehod for similariy measuremen. I is oleran of minor misalignmen of objec edges and ye responsive enough for small local defecs. We use he Lucas-Kanade (1981) differenial mehod for he compuaion of opical flow. The basis of differenial opical flow is he moion consrain equaion under he consan-brighness assumpion: f ðx; y; Þ ¼f ðx þ dx; y þ dy; þ dþ ð18þ where f(x, y, ) is he gray value of pixel (x, y) a frame, and is shifed by dx and dy in he respecive x- and y-axis a image frame þ d. The second erm in equaion (18) can be approximaed by he firs-order Taylor expansion and, hus: f x u þ f y v ¼ 2 f where u ¼ dx/d and v ¼ dy/d. Assuming a consan shif in a small neighborhood window N x,y for pixel (x, y), he opical-flow vecor [u, v ] can be solved by he leas square mehod in he following marix form: V 5 A 1 b where A þ is he pseudo-inverse of A, and: ð19þ For defec deecion applicaions, he discree forms, f yi and f i of he derivaives f/ x, f/ y and f/ for pixel (x i, y i ), i ¼ 1; 2;...; n, in he neighborhood window N x,y of he emplae image f T (x, y) and he regisered scene image f S (x, y) are given by: f yi ¼ 1 2 ½ f T ðx i þ 1; y i Þ 2 f T ðx i 2 1; y i ÞŠ ¼ 1 2 ½ f T ðx i ; y i þ 1Þ 2 f T ðx i ; y i 2 1ÞŠ f i ¼ f T ðx i ; y i Þ 2 f S ðx i ; y i Þ By solving equaion (19), we can obain he opical flow [u(x, y), v(x, y)] for each individual pixel (x, y) in he image. Thus: P uðx;yþ¼ f yi P vðx;yþ¼ 2P 2 P f yi P P 2 P 2 P f i 2 P 22 P f yi f i þ P f yi f yi 22 P P f yi f i 2 f yi 2 P ð20þ f yi f i 2 f yi The opical-flow magniude a pixel (x, y)isdefinedas: p Lðx; yþ ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u 2 ðx; yþþv 2 ðx; yþ ð21þ The opical-flow magniude is hen used as he similariy measure. A defecive region yields large flow magniudes, whereas a defec-free region resuls in very small flow magniudes. A pixel (x, y) is classified as a defec poin if: Lðx; yþ. m L þ C s L ð22þ where m L and s L are he mean and sandard deviaions of flow magniude L in he whole image. Since he opical-flow process can esimae he shif of a pixel in he scene image wih respec o he emplae image, i is well oleran of he minor displacemen beween he wo compared images. 5. Experimenal resuls The sysem configuraion and specificaions of he robo vision sysem have been described in Secion 2. The proposed deecion algorihms were coded in he C þ þ language and implemened on a Penium Core2 Duo, 2.67 GHz personal compuer. The image used in his sudy is very large in size. The compuaion ime of a 1,600 1,200 image is 3.55 s for reflecion deecion. Noe ha his processing ime is required only in he planning sage, and no in he inspecion sage. 5.1 Effecs of changes in parameer values of K and C In he proposed algorihms for surface defec inspecion of 3D objecs, here are wo main parameers ha affec he deecion resuls. One is he conrol consan K for reflecion deecion, and he oher is he conrol consan C for defec 388

9 Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. deecion. The effecs of changes in parameer values of K and C are separaely evaluaed in his subsecion. Generally, oo small he K value may generae random noise, while oo large he K value may reduce he reflecion area deeced in he image. Figure 9(a1) and (b1) shows he es samples of a baery charger and a diecas model jeep o evaluae he effec of changes in he value of conrol consan K for reflecion segmenaion. Figure 9(a2)-(a6) and (b2)-(b6) shows, respecively, he deeced reflecion regions of images (a1) and (b1) wih varying conrol consan K from 0.5 o 3.0. The deecion resuls show ha he deeced reflecion regions are reduced when he parameer value of K increases. A very small value of K ¼ 0.5 generaes a few noisy poins in he objec edges. The experimenal resuls indicae he reflecion region can be reliably deeced wih K value in he range beween 1 and 2, and he proposed refleciondeecion mehod is no sensiive o a small change of K value. The conrol consan C is used o se up he hreshold o segmen he defec region in he image. Too small he C value gives a igh conrol and may generae noisy poins. Too large he conrol consan C, however, gives a loose conrol and may cause he reducion of defec size. For pracical implemenaion, he conrol consan C can be learned from a se of defec-free sample images by seing he parameer o he minimum value ha resuls in no false alarms for all he es samples. Figure 10 shows an elecrical adaper used o evaluae he effec of changes in he value of conrol consan C, where image (a) is he emplae image, and images (b1) and (c1) are defec-free and deecive es samples a he same viewing angle. Figure 10(b2)-(b6) and (c2)-(c6) is, respecively, he deecion resuls of defec-free sample (b1) and defecive sample (c1) wih parameer C varying from 0.5 o 3.0. The deecion resuls show ha a very small value of C ¼ 0.5 generaes noisy poins for he defec-free image, as shown in Figure 10(b2). The deeced defec size is gradually reduced as he parameer value of C increases. Because he defec size is generally very small wih respec o he whole objec size in he image, he conrol consan C can be se in he range beween 1 and 2. The experimenal resuls in Figure 10 also show ha a C value in he range beween 1 and 2 can produce good deecion resuls. 5.2 Reflecion deecion In he experimens on reflecion deecion, hepsame Gaussian filer of size wih scale parameer s ¼ ffiffiffi 2 was applied o all es samples. Figure 11(a1)-(c1) shows he robo posing for a righ-side view (inclined angle 458), a sraigh op view, and a lef-side view (inclined angle 1358) of he diecas model jeep. Figure 11(a2)-(c2) shows he sensed images showing he rear, op, and fron of he miniaure jeep composed of muliple planar and curved surfaces. Figure 11(a3)-(c3) shows he deeced reflecion regions of he respecive images when he conrol consan K for reflecion hreshold is se a 2. I can be observed from he resuling binary images ha all reflecion areas on he jeep surfaces are accuraely deeced and segmened. Figure 12(a1)-(e1) shows he sensed images of a cellular phone wih viewing angles declining from 908 o 828. The display window of he cellular phone is highly reflecive in he sraigh op-view image. The reflecion is hen gradually reduced as he viewing angle is changed from 908 o 828. Figure 12(a2)-(e2) shows he corresponding deecion resuls Figure 9 Effec of changes in he value of conrol consan K for reflecion segmenaion (a1) 90 (b1) 120 (a2) K = 0.5 (b2) K = 0.5 (a3) K = 1.0 (b3) K = 1.0 (a4) K = 1.5 (b4) K = 1.5 (a5) K = 2.0 (b5) K = 2.0 (a6) K = 3.0 (b6) K = 3.0 Noes: (a1) Tes image of he baery charger; (b1) es image of he miniaure jeep; (a2)-(a6) deeced reflecion regions for (a1) wih varying K values; (b2)-(b6) deeced reflecion regions for (b1) wih varying K values 389

10 Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. Figure 10 Effec of changes in he conrol consan C for defec segmenaion (a) (b1) (c1) (b2) C = 0.5 (c2) C = 0.5 (b3) C = 1 (c3) C = 1 (b4) C = 1.5 (c4) C = 1.5 (b5) C = 2 (c5) C = 2 (b6) C = 3 (c6) C = 3 Noes: (a) Templae image of an elecrical adaper; (b1) defec-free es image; (c1) defecive es image wih a bold scrach; (b2)-(b6) deecion resuls of defec-free image (b1) wih varying C values; (c2)-(c6) deecion resuls of defecive image (c1) wih varying C values 390

11 Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. Figure 11 Deecing reflecion on a diecas model jeep (a1) 45 (righ-side view) (b1) 90 (c1) 135 (lef-side view) (a2) (b2) (c2) (a3) (b3) (c3) Noes: (a1)-(c1) Robo poses a he viewing angles of 45, 90 and 135, respecively; (a2)-(c2) respecive sensed images a differen viewing angles; (a3)-(c3) respecive deeced reflecion regions wih conrol consan K = 2 of he reflecion regions as binary images. All he reflecion regions are reliably deeced wih a conrol consan K ¼ 1. Figure 13(a1)-(d1) furher shows he sensed images of a whie plasic mouse a viewing angles of 808, 888, 958 and 1008 (in a horizonal scanning direcion of he image). The convex surface of he mouse presens a circular spark over a wide range of viewing angles. The deecion resuls in Figure 13(a2)-(d2) shows ha he proposed mehod can also deec well he reflecion on a curved surface. The deeced reflecion regions are superimposed on he original gray-level images, so ha he locaion changes of he reflecion regions a differen viewing angles can be easily observed. As seen in Figure 13(a3)-(d3), he deeced reflecion regions are significanly shifed from he lef o he righ in he image when he viewing angle is changed from 808 o The prined characers canno be observed a viewing angle 888 (in image (b1)) when hey are concealed by he reflecion. They can be presen compleely a viewing angle 1008 (in image (b1)). In our implemenaion, he hand-held devices generally are only composed of six surfaces and, hus, a large angular sep of is applied along a scan rajecory. I iniially akes only seven viewing angles from up o wih an incremen of For each of he seven angles, he endeffecor-mouned camera hen moves wih ^18 incremen around he given angle and finds he image wihou reflecion regions. For he objec surfaces ha no reflecions can be removed in any viewing angles, he images wih differen reflecion locaions on he surface are reained as he emplaes, and he reflecion region in each emplae image is marked as a don -care-region which will be ignored in he inspecion process. This ensures all he required inspecion areas are complee. 5.3 Templae maching for defec deecion The performance of he proposed opical-flow similariy measure for defec deecion is compared wih NCC and SAD. Prior o he emplae-maching process, he whole inspecion procedure from reflecion deecion, marker selecion o image regisraion described previously is applied o all es samples discussed in his subsecion. Figure 14 shows he defec deecion resuls of he baery charger. The emplae image of he charger a he viewing angle of 578 is shown in Figure 14(a). Figure 14(b1) is a defec-free es sample, and Figure 14(c1) is a defecive sample wih a hin scrach on he surface. The value of he conrol consan C for he hreshold of similariy measure is deermined such ha he defec-free es sample generaes he leas noise and he defec is well presened in he segmened image for individual comparaive mehods. The binary images in Figure 14(b2)-(b4) are deecion resuls of he defec-free es sample from he NCC mehod wih C ¼ 2, he SAD mehod wih C ¼ 3, and he opical-flow measure wih C ¼ 1, respecively. The binary images in Figure 14(c2)-(c4) are he deeced defec of he defecive es sample from he hree comparaive mehods. The deecion resuls show ha he opical-flow maching mehod can deec he hin scrach well, wihou presening noise. The SAD mehod presens false defec poins around he edges of prined characers. 391

12 Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. Figure 12 Deecing reflecion on a cellular phone (a1) (a2) (b1) (b2) (c1) (c2) (d1) (d2) (e1) (e2) Noes: (a1)-(e1) Sensed images a viewing angles of 90, 88, 86, 84 and 82, respecively; (a2)-(e2) deeced reflecion regions for images in (a1)-(e1) wih conrol consan K = 1 The NCC mehod is also sensiive o objec edges, and is compuaionally expensive wih a large neighborhood window size. Figure 15 shows he defec deecion resuls of he cellular phone a he viewing angle of 798. Figure 15(a) is he emplae image. Figure 15(b1) and (c1) shows a defec free- and a defecive es sample, respecively. The resuling binary images in Figure 15(b2)-(b4) and (c2)-(c4) were obained from NCC, SAD and opical-flow maching. Again, he opical-flow measure can reliably deec he small defec wihou showing any noise. The SAD mehod can also idenify he defec wih noisy poins on he wo verical edges of he phone. Figure 16 furher shows he defec deecion resuls of he elecrical adaper a he viewing angle of 908. Figure 16(a) is he emplae image. Figure 16(b1) is a defec-free sample, and Figure 16(c1) is a defecive sample wih a bold scrach. The deecion resuls in Figure 16(b2)-(b4) and (c2)-(c4) from he hree comparaive mehods also reveal ha he opical-flow maching mehod can accuraely deec he scrach defec. 392

13 Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. Figure 13 Deecing reflecion on a mouse wih curved surfaces (a1) (a2) (a3) (b1) (b2) (b3) (c1) (c2) (c3) (d1) (d2) (d3) Noes: (a1)-(d1) Sensed images a viewing angles of 80, 88, 95 and 100 ; (a2)-(d2) deeced reflecion regions wih conrol consan K = 1; (a3)-(d3) superimposing he deeced reflecion region on he original image 393

14 Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. Figure 14 Defec deecion of he baery charger from differen maching mehods (a) (b1) (c1) (b2) NCC (c2) NCC (b3) SAD (c3) SAD (b4) Opical flow measure (c4) Opical flow measure Noes: (a) Templae image a viewing angle 57 ; (b1) defec-free es image a he same viewing angle; (c1) defec es image wih a scrach on he surface; (b2)-(b4) deecion resuls of he defec-free sample (b1) from NCC, SAD and opical flow measures, respecively; (c2)-(c4) deecion resuls of he defecive sample (c1) from he hree comparaive mehods 394

15 Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. Figure 15 Defec deecion of he cellular phone (a) (b1) (c1) (b2) NCC (c2) NCC (b3) SAD (c3) SAD (b4) Opical flow measure (c4) Opical flow measure Noes: (a) Templae image a viewing angle 79 ; (b1) defec-free es image; (c1) defec es image; (b2)-(b4) deecion resuls of he defec-free sample (b1) from NCC, SAD and opical flow measures, respecively; (c2)-(c4) deecion resuls of he defecive sample (c1) from he hree comparaive mehods 395

16 Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. Figure 16 Defec deecion of he elecrical adaper (a) (b1) (c1) (b2) NCC (c2) NCC (b3) SAD (c3) SAD (b4) Opical flow measure (c4) Opical flow measure Noes: (a1) Templae image a viewing angle 90 ; (b1) defec-free es sample; (c1) defec image wih a bold scrach; (b2)-(b4) deecion resuls of he defec-free sample (b1) from NCC, SAD and opical flow measures, respecively; (c2)-(c4) deecion resuls of he defecive sample (c1) from he hree comparaive mehods 396

17 Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. The NCC mehod shows only scaered poins of he defec. The SAD mehod produces noisy edge poins of he objec in he defec-free image. In he inspecion sage, he processing ime of image regisraion is 0.2 s. Given he large image of size 1,600 1,200 pixels, he required compuaion imes of emplae maching are 0.39 s for SAD, s for NCC, and 1.98 s for opical flow. The SAD mehod is compuaionally very fas and very easy o implemen. I should be used when he compared images can be precisely regisered. The opicalflow maching mehod gives a moderae compuaion ime. I should be used when he objec may presen minor displacemen. 6. Conclusions In his paper, we have presened a robo vision sysem for surface defec deecion of 3D objecs. For he qualiaive inspecion of 3D objec surfaces, he viewing angles of he end-effecor-mouned camera mus cause no or minimum reflecion on he objec surfaces so ha he reflecion region will no be deeced as a defec and an acual defec will no be concealed. We have used an illuminaion-reflecion model o deec and segmen he reflecion region in a sensed image. The robo vision sysem can hus auomaically avoid he high-reflecion viewing angles and find he reflecion-free camera angles for surface inspecion. To eliminae variaion from robo repeaabiliy and objec displacemen, we have also proposed an auomaic markerselecion process o deermine wo discriminaive fiducial markers for each emplae image a a specific viewing angle. The seleced markers conain maximum gradien informaion and hus give he mos complicaed edge paerns for reliable image regisraion. 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18 Surface defec deecion of 3D objecs using robo vision Ya-Hui Tsai e al. Souhwes Symposium on Image Analysis and Inerpreaion, Ausin, TX, USA, pp Tsai, D.M. and Tsai, H.Y. (2010), Low-conras surface inspecion of mura defecs in liquid crysal displays using opical flow-based moion analysis, Machine Vision and Applicaions, available a: hp://dx.doi.org/ /s Tsai, D.M., Chang, C.C. and Chao, S.M. (2010), Microcrack inspecion in heerogeneously exured solar wafers using anisoropic diffusion, Image and Vision Compuing, Vol. 28, pp Willam, R.S., Roh, G. and Rives, J.F. (2003), View planning for auomaed hree-dimensional objec reconsrucion and inspecion, ACM Compuing Surveys, Vol. 35, pp Wong, L.M., Dumon, C. and Abidi, M.A. (1998), Deermining opimal sensor poses in 3-D objec inspecion, Conference on Qualiy Conrol by Arificial Vision, Takamasu, pp Xie, X. (2008), A review of recen advances in surface defec deecion using exure analysis echniques, Elecronic Leers on Compuer Vision and Image Analysis, Vol. 7, pp Yeh, C.H. and Tsai, D.M. (2001), Wavele-based approach for ball grid array (BGA) subsrae conduc pahs inspecion, Inernaional Journal of Producion Research, Vol. 39, pp Yeh, C.H., Wu, F.C., Ji, W.L. and Huang, C.Y. (2010), A wavele-based approach in deecing visual defecs on semiconducor wafer dies, IEEE Transacions on Semiconducor Manufacuring, Vol. 23, pp Zhang, Y. and Zhang, J. (2005), Fuzzy recogniion of he defec of TFT-LCD, Proceedings of he SPIE, Vol. 5637, pp Corresponding auhor Du-Ming Tsai can be conaced a: iedmsai@saurn.yzu. edu.w To purchase reprins of his aricle please reprins@emeraldinsigh.com Or visi our web sie for furher deails: 398