Robust Segmentation and Tracking of Colored Objects in Video

Transcription

1 IEEE TRANSACTIONS ON CSVT, VOL. 4, NO. 6, 2004 Robus Segmenaion and Tracking of Colored Objecs in Video Theo Gevers, member, IEEE Absrac Segmening and racking of objecs in video is of grea imporance for video-based encoding, surveillance and rerieval. However, he inheren difficuly of objec segmenaion and racking is o disinguish changes in he displacemen of objecs from disurbing effecs such as noise and illuminaion changes. Therefore, in his paper, we formulae a colour-based deformable model which is robus agains noisy daa and changing illuminaion. Compuaional mehods are presened o measure colour consan gradiens. Furher, a model is given o esimae he amoun of sensor noise hrough hese color consan gradiens. The obained uncerainy is subsequenly used as a weighing erm in he deformaion process. Experimens are conduced on image sequences recorded from 3D scenes. From he experimenal resuls i is shown ha he proposed colour consan deformable mehod successfully finds objec conours robus agains illuminaion, and noisy, bu homogeneous regions. Keywords- objec segmenaion, objec racking, video, deformable models, colour, colour consancy, noise models, muli-valued gradiens. I. Inroducion The segmenaion and racking of real-world 3D objecs in video sequences is of grea imporance for video encoding, surveillance and rerieval, and significan progress has been made [], [2], [5], [6], [2], [6], [2], [23]. The inheren difficuly of video-based objec racking is ha as a rigid objec moves in a 3D scene hen is shape becomes a perspecive projecion on he image frames. In general, o achieve robus objec racking, geomeric models are used for his shape ransformaion. In he simples case, he shape of he objec undergoes a ranslaional ransformaion from one frame o anoher in which a simple cross-correlaion echnique will suffice. The ranslaional model can be exended o include roaion, scaling, shearing, up o affine ransformaion [3]. When sufficien feaure poins (i.e. edges and corners) are available, objecs can be racked reliably in his manner. However, for non-rigid objecs, such as humans, objec moion is more complex han affine ransformaions. Therefore, Kalman filering has been proposed for racking of non-rigid objecs. The performance of Kalman-based racking sysems is severely hampered in he presence of false observaions. To reduce he effec of noisy daa, he search area could be resriced [6] similar o acive conours [0], [], [3]. Acive conours use edge deecion o compue inernal/exernal energies. Assuming ha he objec displacemen beween frames is small, objec racking by deformable models achieve high racking performance. Therefore, in his paper, we focus Theo Gevers is wih he Compuer Science Insiue, Universiy of Amserdam, Kruislaan 403, 098 SJ, Amserdam, The Neherlands, gevers@science.uva.nl. on deformable models for objec racking. However, in all of he above objec racking approaches i is difficul o disinguish changes in he displacemen of he objecs which are due o real objec movemen from disurbing displacemen effecs such as noisy daa and illuminaion changes. As a consequence, he racking process can be disraced from he arge objec [23] decreasing he accuracy of he racking process. In his paper, we aim a formulaing colour-based deformable models o segmen and rack objecs in video robus agains noisy daa and varying illuminaion. To achieve his, compuaional mehods are presened o measure colour consan gradiens. Furher, a model is proposed for he esimaion of sensor noise hrough hese color consan gradiens. As a resul, he associaed uncerainy is known for each color consan gradien value. The associaed uncerainy is subsequenly used o weigh he color consan gradien during he deformaion process. As a resul, noisy and unsable gradien informaion will conribue less o he deformaion process han reliable gradien informaion yielding robus objec segmenaion and racking. The paper is organized as follows. In Secion II, definiions are given on deformable conours. In Secion III, compuaional mehods are presened o inegrae color and noise-robusness ino hese deformable conours. Finally, experimens are conduced in Secion IV. II. Definiions Deformable models are used in he process of objec segmenaion and racking by providing high-level informaion in he form of coninuiy consrains and low-level informaion in erms of minimum energy consrains relaed o image characerisics [4], [0], [5], [22], for example. In general, deformable models use low-level image characerisics based on inensiy gradien informaion. However, inensiy gradiens are sensiive o illuminaion condiions. Therefore, our aenion is focussed on he use of color informaion. Sapiro inroduces he concep of color snakes [8], [9] using snakes (via level-ses) wih gradiens compued from muli-valued images. However, profound illuminaion effecs may sill inroduce accidenal edges such as shadow and shading edges. Also severe changes in specral composiion of he illuminaion may inroduce arifacs. Therefore, we aim a compuing colour consan gradiens in a principled way o seer he deformable model o converge o objec conours insead of boundaries produced by illuminaion changes. Furher, he aim is o obain robusness agains noisy daa. To his end, he associaed

2 IEEE TRANSACTIONS ON CSVT, VOL. 4, NO. 6, uncerainy is compued for he color consan gradiens and inegraed in he deformaion scheme. To be precise, consider a deformable conour [0]: v() = [x(), y()], [0, ], () moving hrough he spaial domain of an image I o minimize an cos funcional E associaed wih he curve. In fac, E is a weighed sum of inernal and exernal energies: E = αe in + βe ex, (2) where α and β are appropriae weighs. To obain smooh and physically feasible deformaions, he inernal cos is defined by an elasiciy consrain as follows: E in = ( ( v() 2 + v() 2 d)( v() d), (3) where v() and v() denoe he firs and second derivaives of he curve wih respec o measuring respecively he elasiciy of he curve. The exernal cos is derived from he image o enable he curve o arac o salien image feaures (i.e. edges and corners). In mos deformable conours, he inensiy gradien is used giving he following exernal erm: E ex = I(x, y)d, (4) where he gradien image I(x, y) is usually based on Gaussian derivaives. However, as saed above, inensiybased gradien images are dependen on he illuminaion condiions. Consequenly, inensiy gradiens do no necessarily correspond o objec boundaries. Le he colour gradien be denoed by C, hen he colour-based exernal cos erm is as follows: E ex = C(x, y)d. (5) Our aim is o measure colour gradien C discouning illuminaion and which is robus agains noisy daa. III. Colour-based Deformable Models Firs, in secion III-A, compuaional mehods are presened o measure colour consan gradiens. Then, in secion III-B, a model is proposed for he esimaion he uncerainy of hese color consan gradiens. The obained uncerainy is used as a weighing erm in he deformaion process. A. Illuminaion Invarian Derivaives Consider he reflecion model wih narrow-band filers [7]: C k ( x) = G B ( x, n, s)e( x, λ k )B( x, λ k ), (6) where G B ( x, n, s) is he geomeric funcion dependen on he surface orienaion n and illuminaion direcion s a posiion x. Furher, E( x, λ k ) is he illuminaion and B( x, λ k ) is he surface albedo a wavelengh λ k. Various illuminaion-independen color raios have been proposed [8], [4]. These color raios are derived from neighboring poins. A drawback, however, is ha hese color raios migh be negaively affeced by he geomery and pose of he objec. Therefore, we focus on he following color raio [9]: M(C x, C x 2, C 2 x, C 2 x 2 ) = C x C 2 x 2 C x 2 C 2 x, C C 2, (7) expressing he color raio beween wo neighboring image locaions, for C, C 2 {C, C 2,..., C N } giving he measured sensor pulse response a differen wavelenghs, where x and x 2 denoe he image locaions of he wo neighboring pixels. For a sandard RGB color camera, we have: m (R x, R x2, G x, G x2 ) = R x G x2 R x2 G x, (8) m 2 (R x, R x2, B x, B x2 ) = R x B x2 R x2 B x, (9) m 3 (G x, G x2, B x, B x2 ) = G x B x2 G x2 B x. (0) The color raio is independen of he illuminaion, a change in viewpoin, objec geomery as shown by subsiuing eq. (6) in eq. (7): M(C x, C x 2, C x 2, C x 2 2 ) = C x C x 2 2 C x 2 C x 2 = B( x, λ C )B( x 2, λ C2 ) B( x 2, λ C )B( x, λ C2 ). () For he ease of exposiion, we concenrae on m based on he RG-color bands in he following discussion. Wihou loss of generaliy, all resuls derived for m will also hold for m 2 and m 3. Taking he naural logarihm of boh sides of eq. ( 8) resuls for m in: ln m (R x, R x2, G x, G x2 ) = ln( R x G x2 R x2 G x ) = ln R x + ln G x2 ln R x2 ln G x = ln( R x ) ln( R x 2 ) G x G x2 (2) Hence, he color raios can be seen as differences a wo neighboring locaions x and x 2 in he image domain of he logarihm of R/G: C m ( x, x 2 ) = (ln( R G )) x (ln( R G )) x 2 (3) By aking hese differences beween neighboring pixels, he derivaive is obained of he logarihm of image R/G which is independen of he illuminaion color, and also a change in viewpoin, he objec geomery, and illuminaion inensiy. We have aken he gradien magniude by applying Canny s edge deecor (derivaive of he Gaussian) on image ln(r/g) wih non-maximum suppression in a sandard

3 IEEE TRANSACTIONS ON CSVT, VOL. 4, NO. 6, way o obain gradien magniudes a local edge maxima denoed by C m ( x). The resuls obained so far for m hold also for m 2 and m 3, yielding (leaving ou he spaial coordinaes for illusraion simpliciy): C mm 2m 3 = ( C m, C m2, C m3 ) (4) For pixels on a uniformly colored region (i.e. wih fixed surface albedo), in heory, all hree componens will be zero whereas a leas one he hree componens will be non-zero for pixels on locaions where wo regions of disinc surface albedo mee. B. Noise Robusness of Illuminaion Invarian Derivaives The above defined color raios become unsable when inensiy is low. In fac, hese color raios are undefined a he black poin (R = G = B = 0) and hey become very unsable near his singulariy, where a small perurbaion in he RGB-values (e.g. due o noise) will cause a large jump in he ransformed values. For example, consider neighboring pixels having he values R x =, R x2 =, G x = 2, G x2 = 2 (i.e. low inensiy) and anoher neighboring pixel-combinaion having R x = 20, R x2 = 20, G x = 202, G x2 = 202 (i.e. high inensiy) on he range [0,..., 255]. Then hese pixels have he same color raios, for example m (,, 2, 2) = is equal o m (20, 20, 202, 202) =. However, if we consider a minimal value change in RGB due o noise, e.g. R x2 = 2 (insead of R x2 = ), hen his value change will causes a large jump in he corresponding color raios m (, 2, 2, 2) = 0.5 which is differen from m (20, 202, 202, 202) = As a consequence, false color consan gradiens derived from he color raios, are inroduced due o sensor noise. We aim a providing a framework o deermine he uncerainy for he color consan gradiens which is subsequenly used as a weighing erm in he deformaion process as follows. Addiive Gaussian noise is widely used o model hermal noise and is he limiing behavior of phoon couning noise and film grain noise. Therefore, in his paper, we assume ha sensor noise is normally disribued. Then, for an indirec measuremen, he rue value of a measurand u is relaed o is N argumens, denoed by u j, as follows u = q(u, u 2,, u N ) (5) Assume ha he esimae û of he measurand u can be obained by subsiuion of û j for u j. Then, when û,, û N are measured wih corresponding sandard deviaions σû,, σûn, we obain [20] û = q(û,, û N ). (6) Then, if he uncerainies in û,, û N are independen, random and relaively small, he prediced uncerainy in q is given by [20] σ q = N ( q σûi ) 2 (7) û i j= he so-called squares-roo sum mehod. Alhough (7) is deduced for random errors, i is used as an universal formula for various kinds of errors. Focusing on he firs derivaive, he subsiuion of (2) in (7) gives he uncerainy for he illuminaion invarian coordinaes σ m ( x, x 2 ) = σ2 R x σg 2 x σr 2 x2 σg 2 x2 R x 2 G x 4 + G x 2 G x 4 + G x 2 2 G x 4 + G x 2 2 G x 4 (8) Assuming normally disribued random quaniies, he sandard way o calculae he sandard deviaions σ R, σ G, and σ B is o compue he mean and variance esimaes derived from a homogeneously colored surface paches in an image under conrolled imaging condiions. From he analyical sudy of (8), i can be derived ha color raio becomes unsable around he black poin R = G = B = 0. Furher, o propagae he uncerainies from hese color componens hrough he Gaussian gradien modulus, he uncerainy in he gradien modulus is deermined by convolving he confidence map wih he Gaussian coefficiens. This resuls from he uncerainy in sums and differences as follows [20]. If several quaniies are measured wih uncerainies o compue û,, û N (9) σû,, σûn (20) q = û + û 2 + (û N + û N ) (2) hen he uncerainy in he compued value of q is he sum σ q = σû 2 + σû σ2 û N + σû 2 N (22) As a consequence, we obain: [ ( ci / x) σ ci/ x + ( c i / y) σ ci/ y] σ Cm m 2 m 3 i i [( c i/ x) + ( c i / y)] (23) where i is he dimensionaliy of he color space and c i is he noaion for paricular color channels. In his way, he effec of measuremen uncerainy due o noise is propagaed hrough he color consan raio gradien. For a Gaussian disribuion 99% of he values fall wihin a 3σ margin. If a gradien modulus is deeced which exceeds 3σ, we assume ha here is % chance ha his gradien modulus corresponds o no color ransiion: C mm 2m 3 = { if Cmm 2m 3 > 3σ Cm m 2 m 3 0 oherwise, (24) deriving a local hreshold value (leaving ou he spaial coordinaes).

4 IEEE TRANSACTIONS ON CSVT, VOL. 4, NO. 6, C. Color Invariance Color raio gradien C mm 2m 3 requires narrow-band filers o achieve full color consancy. However, general purpose color CCD cameras do no conain narrow-band filers. To his end, specral sharpening could be applied [7] o achieve his o a large exen. However, an alernaive way is o assume ha he illuminaion has a smooh or equally disribued specral power over he wavelenghs (e.g. whie ligh). We propose o parameerize he color invarian model by polar coordinaes θ θ 2 derived from RGB given by [9]: θ = arcan( R ), (25) B θ 2 = arcan( G ), (26) B which are insensiive o surface orienaion, illuminaion direcion and illuminaion inensiy [9]. Subsiuion of eqs. ( 25) - ( 26) in eq. (7) gives he uncerainy for he θ θ 2 coordinaes σ θ = σ θ2 = R 2 σ 2 B + Bσ2 R (R 2 + B 2 ) 2 (27) G 2 σ 2 B + Bσ2 G (G 2 + B 2 ) 2 (28) where σr 2, σ2 G and σ2 B denoe he sensor noise variance, and σ θ and σ θ2 represen he uncerainy (sandard deviaion) in he normalized red and green color componens, respecively. From he analyical sudy of eqs. (27) and (28), i can be derived ha normalized color becomes unsable around he black poin R = G = B = 0. As θ θ 2 is compued from he same posiion hey do no conain any local (spaial) informaion. Therefore, he gradiens are compued in he θ θ 2 domain by applying he Canny s edge deecor. To propagae he uncerainies from he color componens hrough he Gaussian gradien modulus, he mehod proposed in Secion III-B is used. Then, he uncerainy in he gradien modulus is deermined using (23) yielding for he θ θ 2 color model he following color invarian gradien A. Objec Segmenaion In his secion, he deformable model for objec segmenaion is experimenally verified wih respec o varying imaging condiions and noise. The objecs considered during he experimens were recorded in 3 RGB-colour wih he aid of he SONY XC-003P CCD colour camera (3 chips) and he Marox Magic Colour frame grabber. The digiizaion was done in 8 bis per colour. Two ligh sources of average day-ligh colour were used o illuminae he objecs in he scene. The size of he images are 28x28. The sofware has been implemened in C under UNIX operaing sysem running on a SPARC-saion (300 Mhz). In he experimens, he same weighs of eq. ( 2) have been used for he shape and image feaure consrains. Furher, he parial derivaives are compued hrough Gaussian smoohed derivaives wih σ =.0 which is arrived a hrough experimenaion. I has proved o be effecive on our es images. Figure.a shows he image of a mae cube agains a homogeneous background. The iniial conour is shown as specified by he user (he whie conour) on inpu. The image is clearly conaminaed by illuminaion effecs and noise. Noe ha he cube is pained homogeneously. As one can see, he segmenaion resuls based on I and C RGB are negaively affeced by shadows and shading. In fac, for hese gradien fields i is no clear o which boundaries he deformable conour should be pulled o. As a consequence, he final conour is biased and poorly defined. In conras, he final conours obained by he deformable mehod based on C θθ 2 gradien informaion is nicely pulled owards he rue boundary and hence correspond properly o he maerial ransiion. Noe ha in Figure.d he iniial conour has been missed parially. This an inheren problem of snakes in general where here is always a radeoff beween shape (elasiciy) and image feaure consrains. So far, he qualiy of segmenaion resuls for he various colour models is judged qualiaively by visual inspecion. Firs, he ground-ruh has been obained by a human operaor by carefully selecing he ouline of he objecs, see Figure 2. C θθ 2 = { Cθθ 2 if C θθ 2 > 3σ Cθ θ 2 0 oherwise (29) and for he sandard RGB color space we obain C RGB = { CRGB if C RGB > 3σ CRGB 0 oherwise IV. Experimens (30) Experimens are conduced on images from video sequences recorded from 3D scenes. To his end, in Secion IV-A, we focus on he segmenaion of colored objecs. In Secion IV-B, experimens on objec racking in video is considered. Fig. 2. The ground-ruh has been obained by a human operaor by carefully selecing he ouline of he objecs. To evaluae and compare he qualiy of objec segmenaion resuls more objecively, he mean error disance beween he segmenaion resuls and he ground-ruh is aken. To be precise, le X be he image raser and a a

5 IEEE TRANSACTIONS ON CSVT, VOL. 4, NO. 6, Fig.. From lef o righ a. Colour image wih he iniial conour as specified by he user (he whie conour). b. Segmenaion resul based on inensiy gradien field I. c. Segmenaion resul based on RGB gradien field C RGB. d. Segmenaion resul based on θ θ 2 gradien field C θ θ 2. Color Gradien E of Fig. E of Fig. 3 I C RGB C θθ TABLE I Comparison of performance of snake-based segmenaion differeniaed for he various color models. The mean error disance beween he segmenaion resuls and he ground-ruh is aken as a measure of correspondence. binary image conaining he rue conour A defined by A = { x X : a( x) = }. Furher, le b be a binary image, called he segmened image, conaining he segmenaion resul B = { x X : b( x) = }. Le d( x, A) denoe he shores disance from pixel x X o A, hen he mean error disance is given by: E(A, B) = d( x, A) η(b) 2 (3) x B The mean error disance beween he segmenaion resuls and he ground-ruh yields a oal average error of 4.4 pixels for I, 0.4 pixels for C RGB, and 2.5 pixels for C θθ 2 yielding promising resuls for C θθ 2, see Table I. The ime o compue he segmenaion resul was on average 49 seconds on a Ulra 0 Sparc saion. In Figure 3, an image is shown conaining wo plasic donus on op of each oher. Again he images are affeced by shadows, shading, and iner-reflecions. The segmenaion resuls based on inensiy I and colour RGB gradien are poorly defined due o he disurbing influences of he imaging condiions (mosly due o he shadows around he objecs). The final conours obained by he deformable mehod based on he C θθ 2 gradien informaion is again nicely pulled owards he rue edge. The mean error beween he segmenaion resuls of figure 3 and he groundruh yielded a oal average error of 2. pixels for I, 9.3 pixels for C RGB, and 2.4 pixels for C θθ 2, see Table I. The ime o compue he segmenaion resul was on average 52 seconds on a Ulra 0 Sparc saion. B. Objec Tracking In his secion, he racking sysem is experimenally verified on a sandard video, see figure 4. Noe ha, in his secion, i is assumed ha he objec displacemen beween frames is small. Furher, objec occlusion is no oleraed. The iniial locaion of he objec conour in he firs frame (in which he objec appears) has been ineracively seleced by a human operaor. In figures 4 and 5, six frames are shown of a person in fron of a exured background playing ping-pong. The size of he image is 260x35. The frames are clearly conaminaed by shadows, shading and iner-reflecions. Noe again ha each individual objec-par (i.e. T-shir, shor, wall and able) is pained homogeneously wih a disinc colour. Furher, he wall conains exure. The resuls of he racking sysem are shown in figure 4 racking he T-shir, and in figure 5 racking he body of a person. The racking sysem is based on C mm 2m 3. As one can see, all objecs are well racked ignoring radiomerical effecs. From he observed resuls, he racking echnique successfully segmen and rack he objecs. V. Conclusion We have formulaed a colour-based deformable model. Compuaional mehods have been presened o measure colour consan gradiens. Furher, a model has been given o esimae he amoun of sensor noise hrough hese color consan gradiens. The obained uncerainy is subsequenly used as a weighing erm in he deformaion process. From he heoreical and experimenal resuls, we conclude ha he proposed racking sysem successfully find maerial conours discouning illuminaion. Furhermore, he mehod is robus agains noisy, bu homogeneous regions. Acknowledgmens The auhor is graeful o Sennay Ghebreab for parially implemening he algorihm, and he anonymous reviewers for heir valuable commens. References [] Y. Bar-Shalom and T. Formann, Tracking and Daa Associaion, Academic Press London, 988.

6 IEEE TRANSACTIONS ON CSVT, VOL. 4, NO. 6, Fig. 3. From lef o righ a. Colour image wih he iniial conour as specified by he user (he whie conour). b. Segmenaion resul based on inensiy gradien field I. c. Segmenaion resul based on RGB gradien field C RGB. d. Segmenaion resul based on θ θ 2 gradien field C θ θ 2. Fig. 4. Frames from a video showing a person agains a exured background playing ping-pong. From op-down and lef-righ frames are showing by racking he T-shir of he person. [2] A. Blake and M. Isard, Acive Conours, Springer, 998. [3] C. M. Brown and D. Terzopoulos, Inroducion, in Real-Time Compuer Vision, C.M. Brown and D. Terzopoulos (eds), pp. 3-33, Cambridge Universiy Press, 994. [4] C. Chesnaud, P. Refregier, V. Boule, Saisical Region Snake- Based Segmenaion Adaped o Differen Physical Noise Models, PAMI(2), No., November 999, pp [5] D. Comaniciu and P. Meer, Mean Shif Analysis and Applicaions, IEEE ICCV, Greece, pp , 999. [6] J. Evans, Image-enhanced Muliple Model Tracking, Auomaica, vol. 35, pp , 999. [7] Finlayson, G.D., Drew, M.S., and Fun, B.V., Specral Sharpening: Sensor Transformaion for Improved Color Consancy, JOSA,, pp , May, 994. [8] Fun, B. V. and Finlayson, G. D., Color Consan Color Indexing, IEEE PAMI, 7(5), pp , 995. [9] Th. Gevers and Arnold W.M. Smeulders, Color Based Objec Recogniion, Paern Recogniion, 32, pp , March, 999. [0] M. Kass, A. Wikin, D. Terzopoulos, Snakes: Acive Conour Models, Inernaional Journal of Compuer Vision, (4), pp , 988. [] K.F. Lai and R.T. Chin, Deformable Conours: Modeling and Exracion, IEEE Trans. on Paern Analysis and Machine Inelligence, vol. 7(2), 995. [2] A.J. Lipon, H. Fujiyoski, R.S. Pail, Moving Targe Classificaion of and Tracking from Real-Time Video, IEEE Workshop on Applicaions and Compuer Vision, Princeon, pp. 8-4, 998. [3] R. Malladi, J.A. Sehian, B.C. Vemuri, Shape Modeling wih Fron Propagaion, IEEE Trans. on Paern Analysis and Machine Inelligence, 994. [4] S. K. Nayar, and R. M. Bolle, Reflecance Based Objec Recogniion, Inernaional Journal of Compuer Vision, Vol. 7, No. 3, pp , 996 [5] K.P. Ngoi, J.C. Jia, An acive conour model for colour region exracion in naural scenes, IVC(7), No. 3, 999, pp [6] H.T. Nguyen, M. Worring and A. Dev. Deecion of Moving Objecs in Video using a Robus Moion Similariy Measure, IEEE Trans. on Image Processing, Vol. 9, No., pp. 37-4, Jan [7] S.A. Shafer, Using Color o Separae Reflecion Componens, COLOR Res. Appl., 0(4), pp 20-28, 985. [8] G. Sapiro, D. L. Ringach, Anisoropic Diffusion of Muli-valued Images wih Applicaions, o Color Filering, IEEE PAMI, (5), , 996. [9] G. Sapiro, Color Snakes, CVIU(68), No. 2, November 997, pp [20] J. R. Taylor, An Inroducion o Error Analysis, Universiy Science Books, 982. [2] D. Terzopoulos and R. Szeliski, Tracking wih Kalman Snakes, in Acive Vision, Blake and Yuille (eds), pp. 3-20, MIT Press, 992. [22] S.C. Zhu and A. Yuille, Region Compeiion: Unifying Snakes,

7 IEEE TRANSACTIONS ON CSVT, VOL. 4, NO. 6, Fig. 5. Frames from a video showing a man agains a exured background playing ping-pong. From op-down and lef-righ frames are showing by racking he body of he person. Region Growing, and Bayes/MDL for Muliband Image Segmenaion, PAMI(8), No. 9, Sepember 996, pp [23] A. Zisserman, A. Blake, R. Curwen,. Zisserman, A. Blake, R. Curwen, A Framework for Spaio-Temporal Conrol in he Tracking of Visual Conours, in Real-Time Compuer Vision, C.M. Brown and D. Terzopoulos (eds), pp. 3-33, Cambridge Universiy Press, 994. Theo Gevers is an associae professor of Compuer Science a he Universiy of Amserdam, The Neherlands. His main research ineress are in he fundamenals of image daabase sysem design, image rerieval by conen, heoreical foundaion of geomeric and phoomeric invarians and color image processing.