Techniques and Applications for Persistent Backgrounding in a Humanoid Torso Robot

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1 Techniques and Applications fo Pesistent Backgounding in a Humanoid Toso Root David Walke Duhon, Jeod J. Weinman, Eik Leaned-Mille Astact One of the most asic capailities fo an agent with a vision system is to ecognize its own suoundings. Yet supisingly, despite the ease of doing so, many oots stoe little o no ecod of thei own visual suoundings. This pape exploes the utility of keeping the simplest possile pesistent ecod of the envionment of a stationay toso oot, in the fom of a collection of images captued fom vaious pan-tilt angles aound the oot. We demonstate that this paticulaly simple pocess of stoing ackgound images can e useful fo a vaiety of tasks, and can elieve the system designe of cetain equiements as well. We exploe thee uses fo such a ecod: auto-caliation, novel oject detection with a moving camea, and developing attentional saliency maps. I. INTRODUCTION Backgound sutaction is a simple technique fo detecting changes in image o video data, and hence fo detecting the appeaance of novel ojects, the disappeaance of ojects, the motion of ojects, o changing imaging conditions. Backgound sutaction is among the most asic and widespead techniques used in compute vision, and the asic algoithm is easy to implement. As such, it has found widespead use in ootics and othe systems, especially in systems with stationay cameas [4], [1]. While many ootics systems make use of ackgound sutaction in some fashion, ou expeience suggests that most ootics platfoms do not use ackgounding to its fullest potentional. In paticula, when it is used, it fequently comes with some o all of the following limitations: it is only used when cameas ae in a fixed position; the use of the oot is equied to explicitly and manually e-acquie a ackgound image pio to each use of ackgound sutaction (any ackgound images ae thown out at the end of an expeimental session, o upon poweing down the oot); and ackgounding is not used fo any pupose eyond the immediate goal of detecting motion o novel ojects. We ague in this pape that ackgounding techniques can fom a widely useful susystem in ootics systems, especially non-moile systems such as humanoid tosos and in moile systems that spend lage amounts of time in the same envionment, such as a paticula la. The motivation fo ou system stems fom the human visual system when humans awake fom sleep, they can immediately oient themselves y the familia stuctues and ojects which they see aound themselves. The visual memoy of the envionment aound them gives them immediate The authos ae with the Depatment of Compute Science, Univesity of Massachusetts Amhest, Amhest, MA wduhon,weinman,elm@cs.umass.edu. infomation aout thei cuent situation including thei physical oientation, the time of day (estimated y diffeences in lighting), new people o ojects that have appeaed in the scene, and a visual index into pio memoies, visual and othewise, associated with thei cuent location. Ou goal is to endow ou humanoid toso with simila capailities. In paticula, ou goals in this pape ae to use a ackgounding system to pefom cetain simple calliation pocedues fo the oot on powe-up; pefom asic novel-oject detection tasks, even in the pesence of camea motion; and gathe useful statistics and uild a model of aeas of the poale appeaance of new ojects in the envionment. While we focus on just these thee tasks in this pape, we elieve a well-designed ackgounding system can also povide many othe asic capailities, such as detemining time of day, playing a ole in colo constancy (y poviding a consistent efeence fo elative colo measuements unde vaying lighting conditions) and self-diagnosis (e.g. detecting senso dift). We efe to the stoage of a collection of ackgound images on a pemanent stoage device and softwae fo using these images to accomplish vaious tasks, collectively, as pesistent ackgounding. The following section intoduces the hadwae setup and discusses the fist of the thee applications: the use of SIFT featues to localize the position of the BiSight head of ou oot. Section III then discusses a technique that enales the diffeentiation of foegound and ackgound using a moving camea. Finally, section IV intoduces an application that allows us to model stale vesus unstale egions in the humanoid toso s visual space. All of these applications opeate on the same set of data, the pesistent ackgound of images. II. LOCALIZATION OF THE BISIGHT HEAD Ou application platfom is the UMass humanoid toso oot, Dexte (Figue 1). Dexte is equipped with two seven degee-of-feedom Baett Whole Am Manipulatos (WAMs), each with a thee-fingeed Baett hand mounted at its wist. Moe elevant fo ou puposes is Dexte s TRC BiSight head (Figue 2). The BiSight camea system has fou degees of feedom, two of which lie in the pan and tilt angles fo the mount, with the othe two consisting of the hoizontal vege angles fo each camea. The hoizontal vege angles fo each camea ae fixed at 0 adians so that the optical axes ae paallel, and so the task is educed to

2 detemining the pan and tilt angles of the mount given the cuent image. Fo ou fist application the task is to utilize a visual ackgound model to infe the position of the BiSight mount given a fesh image. We have two motivations fo solving this polem. Fist, the pocess of zeoing the BiSight is tedious and slow. Upon poweing up the oot, a special pogam must e invoked which slowly moves the BiSight until it is at the exteme of its tilt angle, and susequently moves until it is at the exteme of the pan angle. This must e done sufficiently slowly so that thee is no isk of gea damage when the BiSight eaches its limit of motion and encountes a physical stop. Afte eaching the physical limits of its pan and tilt ange, the countes fo these vaiales ae set to thei initial values. This pocess takes moe than 30 seconds and is done many times a day when the oot is in egula use. Ou fist motivation is to ypass this pocedue completely and initialize the pan-tilt vaiales fom the ackgound images y simply ecognizing the position and angle of the head fom what it is looking at. Second, while ootic video systems can e uilt with encodes that sense asolute position and hence do not need explicit initialization, thee ae typically tade-offs in cost and accuacy that come with such devices. In addition, encodes and othe hadwae with moving pats ae pone to failue (we have had multiple encode failues in ou own la). Thus, a second motivation is to eplace the functionality of encodes with pats that seve multiple puposes, namely video cameas with pan-tilt capaility. This educes the nume of moving pats of the system and will hopefully lead to moe oust design in the long tem. Fig. 1. Dexte We stat with a set of ackgound images taken ove a ange of known BiSight pan and tilt values. These values ae detemined using the existing caliation pocedue. Now, when given a new image, the idea is to utilize featue coespondences etween the new image and the ackgound images to infe the new image s position. While thee ae Fig. 2. BiSight Head many choices of featues that can e used effectively fo this pupose, we have chosen SIFT featues. SIFT featues ae a suitale choice oth ecause they demonstate a high degee of affine invaiance and ecause they ae highly distinctive, educing the possiility of false matches [6]. As a fist appoximation, y vaying pan and tilt on the BiSight we would seem to e mapping out an image sphee. The elationship etween BiSight pan/tilt angle and pixel column/ow of a paticula featue would then e vey simple. By computing the spheical wap of the ackgound images and the new image we should e ale to position the new image with a high degee of pecision among the panoama of ackgound images. The set of acquied ackgound images, howeve, does not fom a neat spheical image in ou system. In paticula, the otational cente of the BiSight is offset fom the optical cente of each camea causing the camea to tanslate as the BiSight pans. Howeve, helping to some extent is the fact that the tilt axes of the BiSight and of the cameas ae appoximately aligned. Fo the analysis that follows, we make the assumption that this alignment is exact. Despite the tanslation of the camea duing panning, it is easy to deive fom stoed ackgound images and a single newly acquied image easonaly accuate estimates of the tue pan/tilt angles. The asic algoithm is simple: when given a new image, find the two closest ackgound images in the set as judged y the nume of featue coespondences. Next, find SIFT featues that ae pesent in all thee images (the two ackgound images and the new quey image). In all thee images, a given featue will e at a cetain hoizontal and vetical pixel offset, coesponding to a cetain hoizontal and vetical angle displacement fom the camea s optical axis. To detemine pan angle, we simply linealy intepolate the pan of the two known ackgound images to get the pan of the new image, weighting the contiution of each ackgound image y its hoizontal angle distance to the new image. Detemining tilt is simila, though an exta step is needed, as will e explained elow. Fist to justify the algoithm fo pan, examine Figue 3. Figue 3 shows a id s eye view of the BiSight. In the figue, A epesents the otational cente of the BiSight, F

3 3 2.5 φ vs θ; ρ set at π/3 adians (/) = 1/2 (/) = 1/4 (/) = 1/8 (/) = 1/16 φ (adians) θ (adians) Fig. 4. Gaphs showing the nealy linea elationship etween φ and the isight mount s pan angle (θ), justifying ou linea intepolation method of identifying pan angle fom hoizontal image position of a located featue. Fig. 3. A model of BiSight panning and its elation to the optical cente of the camea used to acquie images. See text fo details. epesents the position of a featue in space pojected onto the otational plane, while AD epesents the offset fom the otational cente to the optical cente of the ight camea of the BiSight. Letting θ epesent pan, y the Law of Sines: Fo tilt the situation is slightly diffeent ecause the vetical angle displacement of a featue depends on oth pan and tilt. Refeing to Figue 5, y the Law of Cosines: w 2 = cos (ρ θ). (3) Letting σ epesent tilt, we also have: sin φ = sin ψ = sin(2π (ρ θ) φ) = sin((ρ θ)+φ) = sin(ρ θ)cos(φ)+sin(φ)cos(ρ θ). φ, which is appoximately linealy elated to the hoizontal position of a featue in an image (see Figue 3), can then e expessed as a function of θ: ( ) sin φ = sin(ρ θ)cos(φ)+sin(φ)cos(ρ θ) 0 = ( ) sin(ρ θ)cos(φ)+ cos (ρ θ) sin φ tan φ = ( φ = actan sin(ρ θ) cos (ρ θ)) ( (1) sin(ρ θ) ( cos (ρ θ)) ). (2) Figue 4 shows that fo >>, pan vaies almost linealy with φ, justifying the use of linea intepolation. Also, fo elatively small, local lineaity is a fai appoximation. σ + λ = actan( h ). w (4) Comining these two: ( ) σ = actan h cos (ρ θ) λ. (5) Tilt is clealy a linea function of λ, the vetical angle displacement, ut tilt also depends ( nonlinealy on changes ) h in pan. Letting ψ = actan cos (ρ θ), we can detemine ψ fo the ackgound images using the sum ψ = σ + λ. Using oth ackgound images we can linealy intepolate to find an estimate of ψ new fo the new image. With ψ new in hand we can then use the veticle angle displacement to detemine σ new fo the new image. Thus, the tilt estimate fo the new image ecomes: σ new = ψ new λ. (6) Because ψ is appoximately a linea function of pan Figue 6, the linea intepolation technique yields easonale estimates. A. Results fo Pan/Tilt Detemination To demonstate the technique, we took 20 images acoss a ange of andom BiSight pan and tilt values. We wanted to see how close the pan/tilt values fed to the contolle would match those infeed fom a small dataase of ackgound

4 ψ vs θ; ρ set at π/3 adians, h set at 5 = 1, = 2 = 1, = 4 = 1, = 8 ψ (adians) θ (adians) Fig. 6. Gaph showing appoximately linea elationship etween ψ and the isight mount s pan angle (θ) (seetext). Fig. 5. The geomety of the isight tilt. σ epesents the angle of tilt, while λ epesents the additional vetical angle displacement of the ay to the featue F. ψ (not shown) is the sum of σ and λ. Gound Found Diffeence Pan Tilt Pan Tilt Pan Tilt mean: TABLE I TABLE SHOWING GROUND TRUTH AND ESTIMATED PAN AND TILT ANGLES, USING COMPUTATIONS VIA BACKGROUND IMAGES.ANGLES ARE SHOWN IN DEGREES. images. Ou dataase consisted of 56 images with the ange fo pan fom -0.8 to 0.75 adians and the ange fo tilt fom -0.4 to 0.75 adians, with each image spaced apat y appoximately 0.2 adians in oth pan and tilt diections. The ange fo the 20 andom images was the same. This ange epesents the typical woking ange of ou BiSight. Using ou technique we wee ale to come within one degee of gound tuth in all 20 test images. Typically the diffeence in oth pan and tilt was on the ode of a few tenths of a degee (see tale elow). III. BACKGROUNDING WITH A MOVING CAMERA Many applications of compute vision call fo a division of a scene into a ackgound and a foegound. The ackgound consists of aeas in the scene that eithe ae stale o vay in a pedictale way. The foegound aeas of a scene ae those that cannot e explained fom the ackgound model. In a typical implementation the camea is fixed so that each image pixel coesponds to the same ay in space. While this simplifies the polem considealy it also places atificial limitations on ootic platfoms equipped with a moving camea. The camea may e needed to pefom some othe task othe than ackgounding, such as guiding a each o tacking a moving oject, thus making it impossile to use standad techniques. At the same time, knowledge of changes in the visual scene is no less desiale. What we want is the aility to move the camea duing a task while simultaneously monitoing changes in the visual scene. The asic idea ehind the technique is to use a ackgound consisting of many images taken at given camea positions. In pinciple, the idea is simple, ut thee ae two difficulties that can aise. One, the given camea position is only known to a cetain pecision. This may e due to fundamental pecision limits in the BiSight, caliation eos in the camea, a time lag etween the epoting of camea position and captuing the image, and in ou case a shaky camea vege contolle. Two, even if the camea position wee known pecisely, in ou setup the changes in the visual scene as the camea moves depend upon the depth of ojects in the scene. Such a difficulty would not aise if ou camea could pan and tilt aout its optical cente [7], [8], ut alas that is not the configuation we ae woking with. In othe wods, we must addess the issue of paallax caused y the tanslation of the camea as it otates. The fist step of the algoithm is to appoximately align the new image (Figue 7, fist column) with a stoed ackgound. To do this we pick the ackgound image that has pan/tilt coodinates neaest to those of the new image (Figue 7, second column). Given the espective pan/tilt values of the ackgound image and the new image, a ough estimate can then e made of the hoizontal and vetical pixel offsets that will align the images as closely as possile. Next, we ty to efine ou estimate y seaching fo coespondences

5 using nomalized coss coelation etween andom patches in the new image and those of the ackgound image. This step is vey quick, as ou initial estimate should e faily good, so the seach fo matches can take place in a esticted ange; moeove it is sufficient to use a sample of only 1000 patches. Of the 1000 matches found, the mode of hoizontal and vetical pixel displacement pais is chosen as the est gloal estimate. Afte alignment, the new image is compaed to the ackgound image. Because the new image and the ackgound image wee taken fom slightly diffeent camea positions, thee will e some dispaities in the images due to diffeences in depths of ojects in the scene, even if no new ojects ae pesent in the scene. The thid column of Figue 7 shows inaized diffeence images afte the est gloal match of the images in the fist two columns has een made. In each ow, the only new oject in the image is the human figue nea the cente of the image. The simple diffeence image contains oth gaps that should e shown as fogound ut ae not (missing potions in silhouette of the peson) and extaneous detections that epesent false detected novel ojects. Thee ae a nume of appoaches fo dealing with these dispaities efoe pefoming simple ackgound sutaction. One appoach is to use steeo o othe cues to estimate the depth of ojects in the image, and use this depth and the stoed images to ty to estimate the pope appeaance of an image fom the newly acquied imaging position. If this image can e geneated accuately (in othe wods, if we can infe what the ackgound image should look like fom a new position), then taditional ackgound sutaction can e applied to undestand image changes. Unfotunately the pocess of estimating the appeaance of an image fom a new point of view is extemely difficult. We take an altenative appoach which lagely avoids the polem of the exact computation of an image fom a new point of view. Ou method, which depends upon detecting diffeences in a scene ased upon images fom two slightly diffeent points of view, elies on the following osevation: even though thee is no single simple tansfomation on one image which will accuately epoduce the othe image (due to dispaities), each patch of a new image is likely to have a close match in one of the stoed ackgound images, since dispaities fequently do not disupt local coespondences. To implement a matching technique ased upon this osevation, fo each pixel in the new image, we seach within a 3x3 pixel aea of the ackgound image fo the closest match in tems of sum of squaed diffeences (SSD) of local patches. Afte this seach, the lowest valued SSD found is set to e the distance etween the new image and the ackgound image at that pixel. If this SSD is low then thee is ageement etween the images atthatpixelanditislikely to e ackgound, while a high SSD indicates incompatiility etween the images at that pixel making it likely that the pixel is pat of a foegound oject. The inaized vesion of these images is shown in the fouth column of Figue 7. Finally, we take the esults of this local matching stategy and use a disciminative Makov andom field, as descied elow, to impove the esults y leveaging the spatial continuity of foegound and ackgound patches. This is a pincipled appoach fo incopoating infomation aout the mismatch of the images at each pixel and its neighos to eassess whethe a pixel is a match o not. Let w epesent the stoed ackgound image and x epesent the aligned input osevation. The coesponding laels y indicate whethe a paticula image location is pedicted to e foegound. To make such pedictions, we use a disciminative Makov field [12], [11], 1 p (y w, x, θ, γ) = exp {U (y, w, x; θ, γ)} (7) Z (w, x) whee Z (w, x) ensues the poaility distiution is nomalized. Inside the exponent is an enegy function U epesenting the compatiility etween the segmentation hypothesis and the osevations. We decompose this into two tems: local tems, which measue the compatiility etween image featues and lael fo an individual pixel, and inteaction tems which measue the compatiility etween a pai of neighoing laels. Specifically, U (y, w, x; θ, γ) = θ (y i ) F i (w, x)+ i γ (y i,y j ). (8) The vecto-valued function F i etuns featues of the ackgound and input image at pixel location i. Hee, the featue is the SSD etween the intensity channel of the two images, calculated as descied aove, as well as a constant ias tem. The class-specific paametes θ (y) act as weights on these featues. Fo contextual inteaction, the paametes γ model a ias fo cetain neighoing laels y i and y j. If these inteaction paametes γ ae all zeo, only local infomation is used; this is equivalent to classification y logistic egession. Estimating the paametes fo the model involves finding the values of θ and γ that maximize thei posteio poaility, given a pio p (θ, γ) and some laeled taining data D = {( y (k), w (k), x (k))}. The log posteio ojective k function is i j L (θ, γ; D) = [ ( U y (k), w (k), x (k)) k ( log Z w (k) x (k))] (9) α θ 1 β γ 1, (10) coesponding to using a Laplacian pio fo θ and γ, which we assume ae independent apioiwith paametes α and β. The ojective is convex and thus may e optimized with standad techniques guaanteed to find a gloal optimum. Given the paamete values and and the image featues, we pedict the most likely laeling ŷ =agmaxp (y x, θ, γ) (11) y Unfotunately, the leaning step and the pediction ae intactale due to exponential sums o seach spaces. We

6 appoximate these using the standad sum-poduct and maxpoduct loopy elief popagation algoithms [10]. A. Results of Backgounding The fifth column of Figue 7 shows the final esults of the foegound/ackgound pocessing esulting fom the disciminative Makov andom field. In the fist two ows, each step, oth the local matching analysis and the Makov andom field method, seem to help impove the foegound/ackgound segmentation. The esults in the fist two ows ae good, with only mino exta detections in the second case, and good filling of the foegound figue in oth cases. The thid ow shows a moe polematic example, caused y a damatic lighting change due to sunshine coming in the window. The aw input to ou local matching method is simply not good enough, and we need to incopoate specific techniques to deal with stong lighting changes efoe handling cases such as this. Still, we feel ou techniques ae quite good given the extemely simple natue of the stoed data, namely a simple set of ackgound images taken without special pepaation of the envionment. tilt angle (adians) Fig. 8. Foegound Ojects ove Time pan angle (adians) Likely positions of foegound ojects in pan/tilt space. IV. ENVIRONMENT OVER TIME The final use of ou pesistent ackgounding system is to develop maps of activity in the visile envionment of ou humanoid toso oot. Such maps have een developed ased upon detections of specific types of ojects, as in [5] and fo foegound/ackgound segmentation with stationay cameas [3]. We ae not awae of this having een done with a tanslating camea. Since we have a method fo easily placing each acquied image in a gloal famewok and classifying images into egions of foegound and ackgound, it is a tivial matte to accumulate statistics aout fequency of appeaances of foegound ojects in the envionment. Figue 8 shows the esults afte aout an hou of monitoing ackgound and foegound in the la. Few ojects in the scene wee moved duing the peiod so that the foegound consists mostly of people as they move aout the la. As might e expected, foegound ojects ae clusteed aound the middle of the scene athe than at the exteme pan angles, since uses typically wish to inteact with the oot in its cental wokspace. Also, ecause The BiSight platfom is elevated, moe foegound activity occus at the highe tilt angles. At low tilt angles the BiSight is gazing aove most of the activity in the oom, and this esults in a lack of changes in that potion of the scene. One of ou immediate goals is to incopoate these activity fequency maps ack into the estimate of foegound/ackgound segmentations. We hope to eliminate false detections y weighting ou detections, so that highe thesholds ae equied in aeas of infequent activity. This should e faily staightfowad in the famewok of Makov andom fields. V. CONCLUSION We have shown that a simple set of images taken of the envionment in which a humanoid oot woks is enough infomation to pefom a vaiety of inteesting tasks, including intialization of pan/tilt coodinates, foegound-ackgound segmentation, and ackgound activity modeling, even in the pesence of a moving camea. It is easy to update ackgound pictues, simply y waiting fo a peiod of time duing which thee ae no detections in the foegound to incease the poaility that a stoed set of ackgound images does not accidentally contain foegound ojects. Fotunately, the SIFT ased methods fo setting the intial pan/tilt settings ae not affected y a patially changed ackgound anyway, as spuious matches in SIFT featues ae quite unlikely. Thee ae a nume of exciting diections fo futue wok, paticulaly in the aea of ackgounding with a moving camea. Among these ae adapting these techniques to ackgound models that use mixtue distiutions, as discussed in [2], [9]. While these models wee oiginally designed to deal with small changes in the imaging envionment (due to effects like the waving of tees in the wind), we elieve they can also e used to add oustness to ackgounding techniques when thee ae small changes in camea position unde otation, as is common in many ootic vision setups. Second, the initial gloal alignment etween the new image and ackgound image can e impoved y using a moe sophisticated motion model and point-ased featue matching. This addition should help educe false positives due to misegistation. Thid, we ae expeimenting with using local featues in addition to image intensity with the expectation of educing eos due to camouflage and lighting changes. Fouth, pefomance was not a pimay concen in ou investigations ut must e fo any pactical system. The esults shown wee poduced in less than 500ms, ut, if desied, faste esults ae possile y switching to a faste disciminative Makov field infeence algoithm such as ICM (Iteative Conditional Modes). Finally, as mentioned aove we ae inteested in incopoating activity fequency maps ack into ou foegound/ackgound segmentation.

7 (a) () (c) (d) (e) Fig. 7. Thee examples: (a) input image with foegound oject, () closest ackgound image fom stoed set of pesistent ackgound images, (c) image diffeencing and theshold afte gloal alignment, (d) image diffeencing afte local matching, (e) classification of foegound/ackgound y disciminative Makov andom field. VI. ACKNOWLEDGEMENTS This wok is suppoted in pat y the National Science Foundation (NSF) unde gant IIS and MIT/NASA coopeative ageement NNJ05HB61A. REFERENCES [1] K. Toyama, J. Kumm, B. Bumitt and B. Meyes. Wallflowe: Pinciples and pactice of ackgound maintenance. In Intenational Confeence on Compute Vision, pp , [2] C. Stauffe and W. E. L. Gimson. Adaptive ackgound mixtue models fo eal-time tacking. In Compute Vision and Patten Recognition, pp , [3] C. Stauffe and W. E. L. Gimson. Leaning pattens of activity using eal-time tacking. In IEEE Tansactions on Patten Analysis and Machine Intelligence, Volume 22, No. 8, pp , [4] S. Hat, S. Ou, J. Sweeney and R. Gupen. A famewok fo leaning declaative stuctue. In Wokshop on Manipulation fo Human Envionments, at Rootics: Science and Systems, [5] B. Scassellati. Foundations of a Theoy of Mind fo a Humanoid Root. Ph. D. Thesis. Massachusetts Institute of Technology, [6] D. G. Lowe. Distinctive image featues fom scale-invaiant keypoints. Intenational Jounal of Compute Vision, 60, volume 2, pp , Jan [7] R. Collins, A. Lipton, T. Kanade, H. Fujiyoshi, D. Duggins, Y. Tsin, D. Tollive, N. Enomoto, and O. Hasegawa. A system fo video suveillance and monitoing. tech. epot CMU-RI-TR-00-12, Rootics Institute, Canegie Mellon Univesity, May, [8] E. Hayman and J. Eklundh. Statistical ackgound sutaction fo a moile oseve. In Poceedings IEEE Intenational Confeence on Compute Vision (ICCV), [9] Y. Ren, C. Chua, and Y. Ho. Statistical ackgound modeling fo nonstationay camea. Patten Recognition Lettes, 24, 1-3 (Jan. 2003), pp [10] F. Kschischang, B. Fey, and H.-A.Loelige. Facto Gaphs and the Sum-Poduct Algoithm. IEEE Tansactions on Infomation Theoy, 47, volume 2: , Fe [11] S. Kuma and M. Heet. Disciminative andom fields: A disciminative famewok fo contextual inteaction in classification. In Poceedings 2003 IEEE Intenational Confeence on Compute Vision (ICCV 03), volume 2, pp , [12] J. Laffety, A. McCallum, and F. Peeia. Conditional andom fields: Poailistic models fo segmenting and laeling sequence data. In Poc. 18th Intenational Confeence on Machine Leaning, pp Mogan Kaufmann, San Fancisco, CA, 2001.