A MRF formulation for coded structured light

Size: px
Start display at page:

Download "A MRF formulation for coded structured light"

Transcription

1 A MRF formulaion for coded srucured ligh Jean-Philippe Tardif Sébasien Roy Déparemen d informaique e recherche opéraionnelle Universié de Monréal, Canada {ardifj, roys}@iro.umonreal.ca Absrac Mulimedia projecors and cameras make possible he use of srucured ligh o solve problems such as 3D reconsrucion, dispariy map compuaion and camera or projecor calibraion. Each projecor displays paerns over a scene viewed by a camera, hereby allowing auomaic compuaion of camera-projecor piel correspondences. This paper inroduces a new algorihm o esablish his correspondence in difficul cases of image acquisiion. A probabilisic model formulaed as a Markov Random Field uses he sripe images o find he mos likely correspondences in he presence of noise. Our model is specially ailored o handle he unfavorable projecor-camera piel raios ha occur in muliple-projecor single-camera seups. For he case where more han one camera is used, we propose a robus approach o esablish correspondences beween he cameras and compue an accurae dispariy map. To conduc eperimens, a ground ruh was firs reconsruced from a high qualiy acquisiion. Various degradaions were applied o he paern images which were hen solved using our mehod. The resuls were compared o he ground ruh for error analysis and showed very good performances, even near deph disconinuiies.. Inroducion PSfrag replacemens Coded srucured ligh is an acive compuer vision mehod employing mulimedia projecors and cameras o solve problems such as camera or projecor calibraion [9], 3D reconsrucion [5,,, 4] and dispariy maps compuaion [4]. I encodes he posiion of each projecor piel wih one or many paerns projeced over some surface imaged by a camera. Those images are combined o recover he code and hus piel correspondences beween he camera and he projecor. Many kinds of srucured-ligh sysems have been described, and a good overview is presened in [, ]. Errors in he correspondences occur from noise in he images and, in some cases, ambiguiies in he paerns hemselves. Indeed, soluion usually make a compromise Bi values 3 Scene s objec Recovered bis Projeced paerns Camera images Figure. Projeced paerns for bis 3,,, in direcion for T = 4. Inverse paerns and hose used for he y direcion are no shown. beween he qualiy of he correspondences and he acquisiion ime (relaed o he number of paerns) [8, 3, ]. The use of many cameras can also increase he paern decoding robusness [7]. Mos indusrial scanners require a conrolled environmen o work properly. In some cases, he naure of he scene imposes a hosile environmen which makes he scanning much more difficul. Also, oday s sysems usually assume ha he camera-projecor piel raio (he number of piels of he projecor seen by only one piel of he camera) is around one. Bu his is no always rue. For insance, srucured ligh based muliple-projecor sysems, in which he piel raio decreases as he number of projecors increases, were demonsraed [5, 9]. Also, as hese sysems become less cosly and more widely available, suppor for poor camera qualiy and bad environmen is needed. A more robus srucured ligh approach should be developed for hose siuaions. This paper inroduces a new algorihm o esablish correspondence in difficul cases of image acquisiion. A probabilisic model formulaed as a Markov Random Field uses 3

2 In proceedings of The 5 h Inernaional Conference on 3-D Digial Imaging and Modeling (3DIM 5) sripe images o esimae he mos likely correspondences in he presence of noise. Our model is specially well-suied o handle he unfavorable projecor-camera piel raios ha occur in muliple-projecor single-camera seups. Oher degradaions can be caused by low conras due o srong ambien ligh, high image noise from low qualiy cameras, and also weak projecor lamps or large scanning disances. The probabilisic naure of hese degradaions jusifies he use of such a model. Generally, he evaluaion of he correspondence accuracy requires he use of calibraed camera, projecor and reference objec. To avoid his ask, we used muliple uncalibraed cameras o compue dispariy maps. These maps can be used direcly o evaluae performance and could be used o compare o passive sereo mehods [3]. We propose a robus approach o compue he dispariy from many camera-projecor correspondence maps. In our eperimens, a ground ruh dispariy map was firs reconsruced from a high qualiy acquisiion. Various degradaions were applied o he paern images which were hen processed using our mehod. The resuls were compared o he ground ruh for error analysis. The aricle proceeds as follows: firs we inroduce he srucured-ligh approach we chose; ne we inroduce he model for code correcion; hen we show how o build muliple-camera dispariy maps; finally, some resuls are shown and discussed.. Sysem overview To illusrae how a Markovian model can be used o achieve code correcion, we presen a complee srucured ligh sysem. We deliberaely chose a simple sysem in order o demonsrae more clearly he effeciveness of our reconsrucion model, bu i should also be direcly applicable o oher sysems. In our case, he projeced paerns are horizonal and verical black and whie sripes o allow an arbirary projecor/camera configuraion. Inverse paerns are also used o increase he robusness of he decoding. This decoding defines he iniial measuremens for he cameraprojecor correspondences. In our model, hey are he mos likely values. However, errors can occur, so we show how o compue a confidence value associaed o every bi. I is used o define a Markov Random Field for which he mos likely configuraion can be deermined using he Ieraed Condiional Mode (ICM) algorihm. 3. Complee srucured ligh sysem The complee encoding of a piel posiion is done using muliple paerns. To simplify noaion in his paper, we assume he projecor image is square and is widh is represenable wih T bis. A posiion (, y) is encoded independenly in each dimension so we illusrae our mehod using a single coordinae, say he one. In order o define our srucured ligh paerns, we define he binary encoding of a piel α of he projecor as: P (α) = α s α s where α s is he binary encoding of α, α s he binary complemen of α s and he concaenaion operaor. We also define P (α) as he h bi of P (α). The color of α in he paern is whie if P (α) is and black if i is. This encoding corresponds he projecion of paerns as shown in figure and he binary complemen formalizes he use of inverse paerns o increase robusness in he decoding process (secion 3.). Subracion of an image from is inverse and hresholding is he basic way o discover he value of a bi. Unforunaely, uncerainies occur when he difference becomes very small. This ypically happens when he conras in he images is low or when a border beween sripes is projeced ono a single piel. This basic encoding has he drawback of keeping many sripe borders aligned which can make many bis uncerain in a single code. To correc his, we rely on Gray encoding [7] which minimizes he encoding s biwise difference beween spaial neighbors in he projecor image. For eample, if we consider wo projecor piels having coordinaes 7 and 8. The use of Gray encoding changes heir binary represenaions from and o and, hus reducing he number of sripe borders locaed beween hese piels from 8 o. The encoding becomes: where G is defined as: and >> is he righ bi shif. 3.. Paern decoding P (α) = G(α s ) G(α s ) G(α s ) = α s or (α s >> ) This sep builds he firs esimae of he correspondence of each camera piel o a projecor piel and compues a confidence measure based on he observed pielwise conras. For each camera piel β and each paern, an inensiy I (β) is measured. We define he image conras as: δ(β) = ma ( I (β) ) min ( I (β) ). The Gray code correspondence can hen be recovered wih a simple image difference: C(β) = =T Bin ( I (β) I +T (β) )

3 In proceedings of The 5 h Inernaional Conference on 3-D Digial Imaging and Modeling (3DIM 5) where Bin(a) = { if a > oherwise (he decreasing inde of he concaenaion simply reflecs he fac ha he mos significan bis appear firs). Noe ha no hresholding is done a his sep. However, a hreshold is used o build a confidence mask for he measuremen: H β = { Conf ( I (β) I +T (β), β ) } =T where { if d > Kc δ(β) and δ(β) > K Conf(d, β) = r oherwise. () Hβ is he probabiliy we deermine for he h bi of he Gray code of β s projecor -coordinae o be equal o C (β), he h bi of C(β). The consans K c = and K r = 5 are conservaive enough o insure ha he conras is sufficienly high. These values rarely need o be changed in pracice. A confidence value can be seen as a probabiliy ha he measuremen can be rused. Indeed, a value of means ha he bi was unambiguously recovered and a value of means oal uncerainy. Finally, he number of s in H β indicaes he overall qualiy of he code recovered for piel β. Accordingly, he Boolean value: C v (β) = { if he # of s in Hβ T ma(log ρ, ) oherwise where ρ is an esimae of he smalles piel raio beween he projecor and is image in he camera and is plainly a margin of error. This funcion deermines if β was sufficienly illuminaed by he projecor. The value of C(β) is he mos likely value of he projecor correspondence of β. In ideal siuaions, his value is close o being eac, bu mos of he ime many errors occur. The ne secion eplains how he codes can be correced. 3.. A Markovian model for code correcion The low confidence in cerain bis of a correspondence can resul from wo facors. The firs is he projecion of a border ono a piel. Even hough his occurs more frequenly for low order bis, i can acually happen a any level. Forunaely, even if high order bis weren recovered for a piel, here is a good chance hey were for is neighbors. When all of hem have he same value for some bi, chances are his is he righ value for he curren piel oo. The second facor is relaed o he piel raio beween he camera and he projecor. For similar resoluions, if he area covered by he projecor is smaller han ha covered by he camera, low order bis canno be recovered. In his case, he neighbors are of no help. However, a hypohesis can be made, ha he objec surface is locally smooh, and hus he codes as well. In mos cases, wih he use of Gray codes, only a small number of bis of a correspondence will be uncerain. The scheme we presen ries o find he code ha bes saisfies hese assumpions. The Markovian approach is known o be well adaped o solve his ype of problems. We represen he camera image by a graph G = (B, N). A sie β B is a piel wih a value of C v (β) equal o. Each sie s neighborhood N β is he usual 8-neighborhood (possibly consising of less han 8 elemens). The labels are he T possible values of he coordinae in he projecor image. When a value is associaed o each sie, he Markovian field M is in a configuraion m whose probabiliy is funcion of he measured code C. Given is compued value c, we are looking for he mos likely value of M. In he following, we use C β for C (β) o increase he equaions compacness. We have: P (M = m C = c) P (C = c M = m) P (M = m) P (C β = c β M β = m β ) P (M = m) β B (if we suppose piel- and biwise independence) T P (Cβ = c β Mβ = m β) P (M = m) β B = T P (Cβ = c β Mβ = m β) β B = e β B β N β ξv (m β,m β ) where V is he smoohing cos funcion and ξ a smoohing facor. Taking minus he log, his is equivalen o minimizing direcly he cos funcion: T log P (Cβ = c β Mβ = m β)+ξ V (m, m ) β β β B = β B β N β () w.r.. m. The value of P (Cβ = c β M β = m β ) is modeled using he confidence ha was recovered previously. This can be epressed as: P (C β = c β M β = m β) { H β if c β = m β H β oherwise. (3) The corresponding likelihood of geing a value of as a funcion of he inensiy difference is shown in figure a. Defining V (β, β ) = G (β ) G (β ), where G convers a Gray code o is real value, is a logical

4 In proceedings of The 5 h Inernaional Conference on 3-D Digial Imaging and Modeling (3DIM 5) g replacemens δ(β) δ(β) Likelihood PSfrag replacemens (a).5 δ(β) d δ(β) δ(β) δ(β) Likelihood (b).5 δ(β) Figure. Likelihood ha a recovered bi is as a funcion of he inensiy difference d of he wo corresponding paerns, based on a) eq. and b) eq. 4. considered piel Camera Projecor bes soluion Figure 3. For a piel whose projecor correspondence has uncerain low-order bis and, four soluions are possible (empy circles). According o our model, he mos likely code minimizes he average disance o is neighbors (only he 4-neighborhood is shown for clariy). d δ(β) choice. The effec of he smoohing erm is ha i favors codes ha are in beween hose of he neighbors (figure 3). Unforunaely, i is no clear wha PDF corresponds o his relaion.. Anoher definiion clarifies he effec of he confidence and maching cos funcions. For a given piel β, a code ν is said o be compaible if i is idenical o µ = C(β) for he bis unambiguously recovered. This can be epressed as: { if such ha H Comp(β, µ, ν) = β = : µ = ν oherwise. From eq. and 3, we see ha all compaible codes have equal maching coss and ha all he ohers have probabiliy. This means ha he correc code mus be compaible. In pracice, his funcion can be used for he opimizaion o rejec labels wihou compuing he cos funcion. Also, noe ha when using eq. in he MRF, muliplying ξ by any posiive consan does no aler he amoun of smoohing. More sophisicaed approaches can be used. Indeed, even when a cerain bi of a code has low confidence, he value found by image difference is sill more likely han is complemen. A simple model for his is illusraed in figure b. The closer o zero his difference is, he more ambiguous he value becomes. The confidence funcion relaed o figure b is: Conf(d, β) = if δ(β)>k r and d >K c δ(β) if δ(β) K r oherwise d +K cδ(β) K cδ(β) (4) where K c and K r are he same as in equaion. Finally, anoher confidence measure could be used wihou resoring o hresholding; all labels would have non zero probabiliies. Unforunaely, minimizing he corresponding funcion is far oo comple in pracice. Cos funcions such as eq. are generally no oo difficul o minimize globally and efficienly. However, in our eperimens, he image resoluion and he number of labels are overwhelming. Resoluion by ICM yields convincing resuls wihin a reasonable compuaional ime (see secion 7 for deails), even hough only a local minimum is found. 4. Projecor-o-camera correspondences The projecor-o-camera correspondence map is used for image consrucion in a muliple projecor sysem [5] and also for dispariy map consrucion (secion 5). I is buil by invering he camera-o-projecor mapping obained by srucured ligh. This inverse funcion is no easily deermined. In our eperimens, we were using a camera and a projecor wih similar resoluions. Since he surface illuminaed by he projecor was conained inside he camera image, only a sampling of all he codes could be achieved. In he projecor domain, his means ha no all projecor piels have a corresponding camera piel, which creaes holes in he inverse map. One way o fill hese holes is o use inerpolaion. One mus find a scheme wih a solid geomerical inerpreaion ha performs well in erms of accuracy and eecuion ime. A firs scheme uses homography-based inerpolaion. This assumes ha camera and projecor models are linear for small areas of he image, and ha he objec surface can be approimaed locally by a small planar pach. The camera image is divided ino 4-piel paches reprojeced in he projecor image ono he correspondence poins. Then, if some projecor piel is locaed inside his pach, is value is calculaed from a homography defined wih he four corners of he pach. Anoher scheme presened in [5] uses riangular paches wih bilinear inerpolaion. The geomeric inerpreaion is less inuiive, bu i has been used successfully in applicaions where small inaccuracy could be oleraed. This scheme is specially useful when a fas implemenaion over a GPU is necessary.

5 In proceedings of The 5 h Inernaional Conference on 3-D Digial Imaging and Modeling (3DIM 5) % correspondence raio Figure 4. Percenage of eac correspondences beween wo cameras w.r.. piel raio beween he projecor and is image in he cameras. The resul of he inversion is ha for each piel α of he projecor, a corresponding camera piel C (α) is defined. When a reconsrucion is very well recovered, he relaion C(β) = C (C(β)) should be valid for all piels β illuminaed by he projecor. This does no occur in pracice, because of errors in he reconsrucion and because he piel raio can be smaller han one. 5. Dispariy map consrucion Wihou calibraion of he cameras and projecors, i is impossible o achieve a full 3D reconsrucion of he scene. However, i is possible o build simple dispariy maps [4]. The basic approach for dispariy map consrucion is o use some camera as he main view. A piel β of his camera and is correspondence β in anoher camera j boh have he same and y projecor codes. Their image disance is he dispariy. Esimaing camera-camera correspondences amouns o locaing common projecor codes. For known epipolar geomery, his is done wih a linear search, oherwise, a D search is needed. This process can be very long for large images. Also, when he piel raio is smalle han one or when a lo of errors occur in he correspondences, some codes are no presen in he oher images. Figure 4 illusraes he percenage of piels in an image ha have an eac correspondence piel in anoher image, as a funcion of he camera-projecor piel raio. As epeced, almos all codes are available for piel raios larger han, bu become scarce for low raios. In his case, direc esimaion of dispariy is unusable. A more robus approach is o use he inverse correspondence map presened in secion 4. Le us define he correspondence of projecor piel α in camera j as C j (α). Therefore, he piel in camera j corresponding o piel β of camera is simply C j (C (β)). Because he inverse mapping funcion is inerpolaed, i allows code inerpolaion. For perfecly reconsruced scenes, C (β) = C j (C j (C (β))) should be rue. For many cameras locaed on a single baseline wihou any roaion, we compue he dispariy of a piel β of camera w.r.. camera j as: D,j (β) = n n dis(, j) β C i (C (β)) dis(, i) i= where dis(, j) is he disance beween he wo cameras opical ceners. A correspondence for one camera is no used when he error beween correspondence codes is oo large. In our eperimens, we rejeced a correspondence when he disance was above piels. Rejecion of correspondences for all cameras resuls in unknown dispariy for his piel. 6. Validaion Validaion of a srucured ligh sysem is difficul and someimes involves precise seup and calibraion. A full 3D reconsrucion of a perfecly known scene can be used for error analysis. In he cone of poor image acquisiion, we propose a differen approach o es he qualiy of he correspondences. A scene conaining wo parallel and overlapping planes was carefully reconsruced in a conrolled environmen (consan ambien lighing) wih a powerful XGA projecor of lumens and a low-noise Basler Abc ccv 8 8 camera. Each plane feaured a checkerboard eure of varying colors so he conras in he images is no uniform. A dispariy map was compued using one camera moved o si locaions on a single baseline. Is accuracy was high enough o be considered as our ground ruh. Picures of he wo planes, he dispariy map and one sample slice are shown in figure 7. In previous eperimens, we had observe ha our algorihm performs much beer han classical approaches in he presence of high noise. We also waned o show ha significan improvemens could be achieved in beer, more realisic condiions. In order o do his, we measured he noise induced in he paern images when compression is urned on, as i commonly is on low qualiy cameras (cf. figure 5). Than in our ess, we corruped he images wih Gaussian noise of mean and sandard deviaion, a smaller value han he previous measuremen. In addiion, we gradually reduced he images conras by K percen (cf. figure 6). Four algorihms were esed wih gradually decreasing conras o increase correspondence errors. We recovered he codes wih each of hem and buil he dispariy maps. Codes and maps were compared o he ground ruh. The firs algorihm (labeled P ) consised solely in he recovery of he codes wihou any furher correcion. In he second, labeled Q, we used a simple low-pass filering of he codes. A 5 5 filer was empirically deermined o be a good compromise beween smoohing and disconinuiy preservaion. Finally for he hird and fourh, respecively labeled M and M, we esed our proposed Markovian approach wih he

6 In proceedings of The 5 h Inernaional Conference on 3-D Digial Imaging and Modeling (3DIM 5).5..5 Border of Plane Plane Plane..5 Figure 5. Hisogram of he noise in he paern images compressed wih MJPEG (compression raio around :). The variance of he noise is higher han ha of he noise we added o es our algorihms. Dispariy (a) Plane (b) 5 Plane (a) (b) (c) Figure 6. a) Zoom on a secion of a non-corruped paern. b) Same image wih a 4% image conras reducion and added noise. Figure 7. a) Image of he wo planes. b) Dispariy map reconsruced for he lef camera. c) One slice of he dispariy map. maching cos funcions of eq. and eq. 4. We used he ICM algorihm o perform he opimizaion, moving randomly from one piel o anoher [6]. An operaion consiss of compuing he cos funcion for every possible code for a given piel and hen selec he bes value. One ieraion conains a number of operaions equal o he number of piels in he image. The configuraion for which he cos funcion was minimum was kep and he process sopped afer 7 consecuive ieraions wihou improvemen. 7. Resuls Figure 8 gives he average error in piels in he dispariy maps and he recovered codes of each algorihm, as a funcion of he conras reducion K. This error is compued as he euclidean disance beween a recovered code and he ground ruh, meaning ha errors for high-order bis are wors han ha for low-order bis. For he dispariy error (figure 8b), he only piels considered are hose for which a dispariy value was recovered. Figure 8c illusraes he number of piels kep for differen values of K. We observed ha he Markovian approach is always superior o raw codes (P ) and filering (Q), paricularly when he decoded paerns have a lo of errors. Moreover, he number of rejeced piels is always smaller. I also came as a surprise ha he simpler model M performs as well as M, and someimes beer. For he laer, finding a good soluion is more difficul when he codes are highly corruped, especially near disconinuiies (figure ). Moreover, each ieraion for algorihm M akes abou 5 seconds, bu akes more han wice as much for M, as he likelihood canno be precompued because of memory limiaions. For our ess, convergence of ICM akes beween and 4 ieraions, and similarly for M and M. The parameer ξ > has no influence on he soluion when using M, bu has a big impac for M. This is illusraed in figure 9, in which we also observe ha M never yields significanly beer resuls as M. A value ξ equal o is equivalen o no code correcion. As ξ ges larger, he soluion for eq. 4 gradually converges o he one obained wih eq.. We observe ha for a value above., he error is close o sable, and above.5, he soluions are eacly he same. In our ess, we used a value ξ =.4. Figures 8 shows ha even a small amoun of error in he codes (8a) can resul in large differences in he dispariy maps (8b). These errors increase he number of rejeced correspondences (8c), yielding holes in he maps, as illusraed in figures and. The resuls of he filering algorihm (Q) are surprising. Bad ouliers occur frequenly, and since hey are no correced in any way, hey inroduce very large dispariy errors which appear as spikes in figure b. Consequenly, filer-

7 In proceedings of The 5 h Inernaional Conference on 3-D Digial Imaging and Modeling (3DIM 5) g replacemens code error P M M Q PSfrag replacemens K dispariy error Q P 6 PSfrag replacemens M M K # of piels M M Q P K (a) (b) (c) Figure 8. Performance as a funcion of conras reducion K. a) Mean error in he recovered codes. b) Mean error in piels of he dispariy maps. c) Number of piels wih a recovered dispariy. (a) Ground ruh (b) Algo. P (c) Algo. Q (d) Algo. M (e) Algo. M Figure. Recovered codes for conras K = 4% eured wih a checkerboard image. rag replacemens error Algo. M Algo. M Ξ oward widespread use of srucured ligh in unconrolled environmen wih commonly available equipmen. I performs more robusly han convenional mehods and recovers accurae deph disconinuiies. I was used successfully o calibrae a large muli-projecor screens used in a public performance [6]. In he fuure, a more physically plausible model could be invesigaed, bu he increased compuaional burden migh prove unsurmounable. Figure 9. Mean error in he codes w.r.. he smoohing parameer ξ. For ξ =, no correcion is made o he codes. For ξ.5, he choice of maching cos funcion makes virually no difference. ing is suiable only in he absence of error in high order bis. In fac, he filering mechanism, unless combined wih a robus piel selecion, always propagaes he error of a piel o is neighbors insead of correcing i. 8. Conclusion This paper presened a Markovian approach o coded srucured ligh reconsrucion. We consider i is one sep References [] J. Ballea, J. Salvia. Recen Progress in Srucured Ligh in order o Solve he Correspondence Problem in Sereovision. ICRA 997. [] Nelson L. Chang. Efficien Dense Correspondences using Temporally Encoded Ligh Paerns. Procams 3. [3] E. Horn, N. Kiryai. Toward Opimal Srucured Ligh Paerns. 3DIM 997. [4] P. Huang, S. Zhang. High-resoluion, Real-ime 3D Shape Acquisiion. In IEEE Workshop on real-ime 3D sensors and heir uses, 4. [5] T. P. Koninck, I. Geys, T. Jaeggli, L. V. Gool. A Graph Cu based Adapive Srucured Ligh approach for real-ime Range Acquisiion. 3DPVT 4. [6] S.Z. Li. Markov Random Field Modeling in Compuer Vision. Springer-Verlag 995.

8 In proceedings of The 5 h Inernaional Conference on 3-D Digial Imaging and Modeling (3DIM 5) (a) Algo. P (b) Algo. Q (c) Algo. M (d) Algo. M Figure. Zoom on a secion of he dispariy maps compued wih conras K = 3%. Black represens piels wih no recovered dispariy. Dispariy 5 5 Dispariy (a) Algo. P (b) Algo. Q Dispariy 5 5 Dispariy (c) Algo. M (d) Algo. M Figure. Horizonal slices of he dispariy maps of figure. Piels wih unrecovered dispariies are se o. [7] Hans-Gerd Maas. Robus Auomaic Surface Reconsrucion wih Srucured Ligh. Inernaional Archives of Phoogrammery and Remoe Sensing Vol. XXIX (99). [8] J. Pagès, J. Salvi, C. Maabosch. Implemenaion of a robus coded srucured ligh echnique for dynamic 3D measuremens. ICIP 3. [9] R. Raskar, M. S. Brown, R. Yang, W. Chen, G. Welch, H. Towles, B. Seales, H. Fuchs. Muli-Projecor Displays Using Camera-Based Regisraion. IEEE Visualizaion 999. [] C. Rocchini, P. Cignoni, C. Monani, P. Pingi, R. Scopigno. A low cos 3D scanner based on srucured ligh. Compuer Graphics Forum (Eurographics Conf. Issue). Blackwell v,3. [] S, Rusinkiewicz, O. Hall-Hol, M. Levoy. Real-Time 3D Model Acquisiion. ACM Transacions on Graphics,, 3. [] J, Salvi, J. Pagès, J. Balle. Paern codificaion sraegies in srucured ligh sysems. Paern Recogniion 4. [3] D. Scharsein, R. Szeliski. A Taonomy and Evaluaion of Dense Two-Frame Sereo Correspondence Algorihms. IJCV 47(//3):7-4,. [4] D. Scharsein, R. Szeliski. High-Accuracy Sereo Deph Maps Using Srucured Ligh. CVPR 3. [5] J.-P. Tardif, S. Roy, M. Trudeau. Muli-projecors for arbirary surfaces wihou eplici calibraion nor reconsrucion. 3DIM 3. [6] S. Roy, J.-P. Tardif. LighTwis, a muli-projecor sysem. Live performance, ArFuura 4. [7] E. W. Weissein. Gray Code, MahWorld A Wolfram Web Resource. hp://mahworld.wolfram.com/graycode.hml. [8] R. Yang, D. Goz, J. Hensley, H. Towles, M.S. Brown. PielFle: a reconfigurable muli-projecor display sysem. IEEE Visualizaion. [9] R., Andrew, G. Gill, A. Majumder, H. Towles, H. Fuchs. PielFle: A Comprehensive, Auomaic, Casually- Aligned Muli-Projecor Display. PROCAMS 3.

STEREO PLANE MATCHING TECHNIQUE

STEREO PLANE MATCHING TECHNIQUE STEREO PLANE MATCHING TECHNIQUE Commission III KEY WORDS: Sereo Maching, Surface Modeling, Projecive Transformaion, Homography ABSTRACT: This paper presens a new ype of sereo maching algorihm called Sereo

More information

CAMERA CALIBRATION BY REGISTRATION STEREO RECONSTRUCTION TO 3D MODEL

CAMERA CALIBRATION BY REGISTRATION STEREO RECONSTRUCTION TO 3D MODEL CAMERA CALIBRATION BY REGISTRATION STEREO RECONSTRUCTION TO 3D MODEL Klečka Jan Docoral Degree Programme (1), FEEC BUT E-mail: xkleck01@sud.feec.vubr.cz Supervised by: Horák Karel E-mail: horak@feec.vubr.cz

More information

Implementing Ray Casting in Tetrahedral Meshes with Programmable Graphics Hardware (Technical Report)

Implementing Ray Casting in Tetrahedral Meshes with Programmable Graphics Hardware (Technical Report) Implemening Ray Casing in Terahedral Meshes wih Programmable Graphics Hardware (Technical Repor) Marin Kraus, Thomas Erl March 28, 2002 1 Inroducion Alhough cell-projecion, e.g., [3, 2], and resampling,

More information

Image segmentation. Motivation. Objective. Definitions. A classification of segmentation techniques. Assumptions for thresholding

Image segmentation. Motivation. Objective. Definitions. A classification of segmentation techniques. Assumptions for thresholding Moivaion Image segmenaion Which pixels belong o he same objec in an image/video sequence? (spaial segmenaion) Which frames belong o he same video sho? (emporal segmenaion) Which frames belong o he same

More information

CENG 477 Introduction to Computer Graphics. Modeling Transformations

CENG 477 Introduction to Computer Graphics. Modeling Transformations CENG 477 Inroducion o Compuer Graphics Modeling Transformaions Modeling Transformaions Model coordinaes o World coordinaes: Model coordinaes: All shapes wih heir local coordinaes and sies. world World

More information

4. Minimax and planning problems

4. Minimax and planning problems CS/ECE/ISyE 524 Inroducion o Opimizaion Spring 2017 18 4. Minima and planning problems ˆ Opimizing piecewise linear funcions ˆ Minima problems ˆ Eample: Chebyshev cener ˆ Muli-period planning problems

More information

A Matching Algorithm for Content-Based Image Retrieval

A Matching Algorithm for Content-Based Image Retrieval A Maching Algorihm for Conen-Based Image Rerieval Sue J. Cho Deparmen of Compuer Science Seoul Naional Universiy Seoul, Korea Absrac Conen-based image rerieval sysem rerieves an image from a daabase using

More information

4.1 3D GEOMETRIC TRANSFORMATIONS

4.1 3D GEOMETRIC TRANSFORMATIONS MODULE IV MCA - 3 COMPUTER GRAPHICS ADMN 29- Dep. of Compuer Science And Applicaions, SJCET, Palai 94 4. 3D GEOMETRIC TRANSFORMATIONS Mehods for geomeric ransformaions and objec modeling in hree dimensions

More information

Gauss-Jordan Algorithm

Gauss-Jordan Algorithm Gauss-Jordan Algorihm The Gauss-Jordan algorihm is a sep by sep procedure for solving a sysem of linear equaions which may conain any number of variables and any number of equaions. The algorihm is carried

More information

Evaluation and Improvement of Region-based Motion Segmentation

Evaluation and Improvement of Region-based Motion Segmentation Evaluaion and Improvemen of Region-based Moion Segmenaion Mark Ross Universiy Koblenz-Landau, Insiue of Compuaional Visualisics, Universiässraße 1, 56070 Koblenz, Germany Email: ross@uni-koblenz.de Absrac

More information

NEWTON S SECOND LAW OF MOTION

NEWTON S SECOND LAW OF MOTION Course and Secion Dae Names NEWTON S SECOND LAW OF MOTION The acceleraion of an objec is defined as he rae of change of elociy. If he elociy changes by an amoun in a ime, hen he aerage acceleraion during

More information

Algorithm for image reconstruction in multi-slice helical CT

Algorithm for image reconstruction in multi-slice helical CT Algorihm for image reconsrucion in muli-slice helical CT Kasuyuki Taguchi a) and Hiroshi Aradae Medical Engineering Laboraory, Toshiba Corporaion, 1385 Shimoishigami, Oawara, Tochigi 324-855, Japan Received

More information

LAMP: 3D Layered, Adaptive-resolution and Multiperspective Panorama - a New Scene Representation

LAMP: 3D Layered, Adaptive-resolution and Multiperspective Panorama - a New Scene Representation Submission o Special Issue of CVIU on Model-based and Image-based 3D Scene Represenaion for Ineracive Visualizaion LAMP: 3D Layered, Adapive-resoluion and Muliperspecive Panorama - a New Scene Represenaion

More information

Real-Time Non-Rigid Multi-Frame Depth Video Super-Resolution

Real-Time Non-Rigid Multi-Frame Depth Video Super-Resolution Real-Time Non-Rigid Muli-Frame Deph Video Super-Resoluion Kassem Al Ismaeil 1, Djamila Aouada 1, Thomas Solignac 2, Bruno Mirbach 2, Björn Oersen 1 1 Inerdisciplinary Cenre for Securiy, Reliabiliy, and

More information

MORPHOLOGICAL SEGMENTATION OF IMAGE SEQUENCES

MORPHOLOGICAL SEGMENTATION OF IMAGE SEQUENCES MORPHOLOGICAL SEGMENTATION OF IMAGE SEQUENCES B. MARCOTEGUI and F. MEYER Ecole des Mines de Paris, Cenre de Morphologie Mahémaique, 35, rue Sain-Honoré, F 77305 Fonainebleau Cedex, France Absrac. In image

More information

A Fast Stereo-Based Multi-Person Tracking using an Approximated Likelihood Map for Overlapping Silhouette Templates

A Fast Stereo-Based Multi-Person Tracking using an Approximated Likelihood Map for Overlapping Silhouette Templates A Fas Sereo-Based Muli-Person Tracking using an Approximaed Likelihood Map for Overlapping Silhouee Templaes Junji Saake Jun Miura Deparmen of Compuer Science and Engineering Toyohashi Universiy of Technology

More information

EECS 487: Interactive Computer Graphics

EECS 487: Interactive Computer Graphics EECS 487: Ineracive Compuer Graphics Lecure 7: B-splines curves Raional Bézier and NURBS Cubic Splines A represenaion of cubic spline consiss of: four conrol poins (why four?) hese are compleely user specified

More information

Visual Indoor Localization with a Floor-Plan Map

Visual Indoor Localization with a Floor-Plan Map Visual Indoor Localizaion wih a Floor-Plan Map Hang Chu Dep. of ECE Cornell Universiy Ihaca, NY 14850 hc772@cornell.edu Absrac In his repor, a indoor localizaion mehod is presened. The mehod akes firsperson

More information

Mobile Robots Mapping

Mobile Robots Mapping Mobile Robos Mapping 1 Roboics is Easy conrol behavior percepion modelling domain model environmen model informaion exracion raw daa planning ask cogniion reasoning pah planning navigaion pah execuion

More information

Sam knows that his MP3 player has 40% of its battery life left and that the battery charges by an additional 12 percentage points every 15 minutes.

Sam knows that his MP3 player has 40% of its battery life left and that the battery charges by an additional 12 percentage points every 15 minutes. 8.F Baery Charging Task Sam wans o ake his MP3 player and his video game player on a car rip. An hour before hey plan o leave, he realized ha he forgo o charge he baeries las nigh. A ha poin, he plugged

More information

Rao-Blackwellized Particle Filtering for Probing-Based 6-DOF Localization in Robotic Assembly

Rao-Blackwellized Particle Filtering for Probing-Based 6-DOF Localization in Robotic Assembly MITSUBISHI ELECTRIC RESEARCH LABORATORIES hp://www.merl.com Rao-Blackwellized Paricle Filering for Probing-Based 6-DOF Localizaion in Roboic Assembly Yuichi Taguchi, Tim Marks, Haruhisa Okuda TR1-8 June

More information

A High-Speed Adaptive Multi-Module Structured Light Scanner

A High-Speed Adaptive Multi-Module Structured Light Scanner A High-Speed Adapive Muli-Module Srucured Ligh Scanner Andreas Griesser 1 Luc Van Gool 1,2 1 Swiss Fed.Ins.of Techn.(ETH) 2 Kaholieke Univ. Leuven D-ITET/Compuer Vision Lab ESAT/VISICS Zürich, Swizerland

More information

Learning in Games via Opponent Strategy Estimation and Policy Search

Learning in Games via Opponent Strategy Estimation and Policy Search Learning in Games via Opponen Sraegy Esimaion and Policy Search Yavar Naddaf Deparmen of Compuer Science Universiy of Briish Columbia Vancouver, BC yavar@naddaf.name Nando de Freias (Supervisor) Deparmen

More information

A METHOD OF MODELING DEFORMATION OF AN OBJECT EMPLOYING SURROUNDING VIDEO CAMERAS

A METHOD OF MODELING DEFORMATION OF AN OBJECT EMPLOYING SURROUNDING VIDEO CAMERAS A METHOD OF MODELING DEFORMATION OF AN OBJECT EMLOYING SURROUNDING IDEO CAMERAS Joo Kooi TAN, Seiji ISHIKAWA Deparmen of Mechanical and Conrol Engineering Kushu Insiue of Technolog, Japan ehelan@is.cnl.kuech.ac.jp,

More information

Real Time Integral-Based Structural Health Monitoring

Real Time Integral-Based Structural Health Monitoring Real Time Inegral-Based Srucural Healh Monioring The nd Inernaional Conference on Sensing Technology ICST 7 J. G. Chase, I. Singh-Leve, C. E. Hann, X. Chen Deparmen of Mechanical Engineering, Universiy

More information

High Resolution Passive Facial Performance Capture

High Resolution Passive Facial Performance Capture High Resoluion Passive Facial Performance Capure Derek Bradley1 Wolfgang Heidrich1 Tiberiu Popa1,2 Alla Sheffer1 1) Universiy of Briish Columbia 2) ETH Zu rich Figure 1: High resoluion passive facial performance

More information

DAGM 2011 Tutorial on Convex Optimization for Computer Vision

DAGM 2011 Tutorial on Convex Optimization for Computer Vision DAGM 2011 Tuorial on Convex Opimizaion for Compuer Vision Par 3: Convex Soluions for Sereo and Opical Flow Daniel Cremers Compuer Vision Group Technical Universiy of Munich Graz Universiy of Technology

More information

Real-time 2D Video/3D LiDAR Registration

Real-time 2D Video/3D LiDAR Registration Real-ime 2D Video/3D LiDAR Regisraion C. Bodenseiner Fraunhofer IOSB chrisoph.bodenseiner@iosb.fraunhofer.de M. Arens Fraunhofer IOSB michael.arens@iosb.fraunhofer.de Absrac Progress in LiDAR scanning

More information

Low-Cost WLAN based. Dr. Christian Hoene. Computer Science Department, University of Tübingen, Germany

Low-Cost WLAN based. Dr. Christian Hoene. Computer Science Department, University of Tübingen, Germany Low-Cos WLAN based Time-of-fligh fligh Trilaeraion Precision Indoor Personnel Locaion and Tracking for Emergency Responders Third Annual Technology Workshop, Augus 5, 2008 Worceser Polyechnic Insiue, Worceser,

More information

A Face Detection Method Based on Skin Color Model

A Face Detection Method Based on Skin Color Model A Face Deecion Mehod Based on Skin Color Model Dazhi Zhang Boying Wu Jiebao Sun Qinglei Liao Deparmen of Mahemaics Harbin Insiue of Technology Harbin China 150000 Zhang_dz@163.com mahwby@hi.edu.cn sunjiebao@om.com

More information

Video Content Description Using Fuzzy Spatio-Temporal Relations

Video Content Description Using Fuzzy Spatio-Temporal Relations Proceedings of he 4s Hawaii Inernaional Conference on Sysem Sciences - 008 Video Conen Descripion Using Fuzzy Spaio-Temporal Relaions rchana M. Rajurkar *, R.C. Joshi and Sananu Chaudhary 3 Dep of Compuer

More information

Design Alternatives for a Thin Lens Spatial Integrator Array

Design Alternatives for a Thin Lens Spatial Integrator Array Egyp. J. Solids, Vol. (7), No. (), (004) 75 Design Alernaives for a Thin Lens Spaial Inegraor Array Hala Kamal *, Daniel V azquez and Javier Alda and E. Bernabeu Opics Deparmen. Universiy Compluense of

More information

Moving Object Detection Using MRF Model and Entropy based Adaptive Thresholding

Moving Object Detection Using MRF Model and Entropy based Adaptive Thresholding Moving Objec Deecion Using MRF Model and Enropy based Adapive Thresholding Badri Narayan Subudhi, Pradipa Kumar Nanda and Ashish Ghosh Machine Inelligence Uni, Indian Saisical Insiue, Kolkaa, 700108, India,

More information

An Adaptive Spatial Depth Filter for 3D Rendering IP

An Adaptive Spatial Depth Filter for 3D Rendering IP JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE, VOL.3, NO. 4, DECEMBER, 23 175 An Adapive Spaial Deph Filer for 3D Rendering IP Chang-Hyo Yu and Lee-Sup Kim Absrac In his paper, we presen a new mehod

More information

In fmri a Dual Echo Time EPI Pulse Sequence Can Induce Sources of Error in Dynamic Magnetic Field Maps

In fmri a Dual Echo Time EPI Pulse Sequence Can Induce Sources of Error in Dynamic Magnetic Field Maps In fmri a Dual Echo Time EPI Pulse Sequence Can Induce Sources of Error in Dynamic Magneic Field Maps A. D. Hahn 1, A. S. Nencka 1 and D. B. Rowe 2,1 1 Medical College of Wisconsin, Milwaukee, WI, Unied

More information

Improved TLD Algorithm for Face Tracking

Improved TLD Algorithm for Face Tracking Absrac Improved TLD Algorihm for Face Tracking Huimin Li a, Chaojing Yu b and Jing Chen c Chongqing Universiy of Poss and Telecommunicaions, Chongqing 400065, China a li.huimin666@163.com, b 15023299065@163.com,

More information

Network management and QoS provisioning - QoS in Frame Relay. . packet switching with virtual circuit service (virtual circuits are bidirectional);

Network management and QoS provisioning - QoS in Frame Relay. . packet switching with virtual circuit service (virtual circuits are bidirectional); QoS in Frame Relay Frame relay characerisics are:. packe swiching wih virual circui service (virual circuis are bidirecional);. labels are called DLCI (Daa Link Connecion Idenifier);. for connecion is

More information

Computer representations of piecewise

Computer representations of piecewise Edior: Gabriel Taubin Inroducion o Geomeric Processing hrough Opimizaion Gabriel Taubin Brown Universiy Compuer represenaions o piecewise smooh suraces have become vial echnologies in areas ranging rom

More information

MOTION TRACKING is a fundamental capability that

MOTION TRACKING is a fundamental capability that TECHNICAL REPORT CRES-05-008, CENTER FOR ROBOTICS AND EMBEDDED SYSTEMS, UNIVERSITY OF SOUTHERN CALIFORNIA 1 Real-ime Moion Tracking from a Mobile Robo Boyoon Jung, Suden Member, IEEE, Gaurav S. Sukhame,

More information

Audio Engineering Society. Convention Paper. Presented at the 119th Convention 2005 October 7 10 New York, New York USA

Audio Engineering Society. Convention Paper. Presented at the 119th Convention 2005 October 7 10 New York, New York USA Audio Engineering Sociey Convenion Paper Presened a he 119h Convenion 2005 Ocober 7 10 New Yor, New Yor USA This convenion paper has been reproduced from he auhor's advance manuscrip, wihou ediing, correcions,

More information

Probabilistic Detection and Tracking of Motion Discontinuities

Probabilistic Detection and Tracking of Motion Discontinuities Probabilisic Deecion and Tracking of Moion Disconinuiies Michael J. Black David J. Flee Xerox Palo Alo Research Cener 3333 Coyoe Hill Road Palo Alo, CA 94304 fblack,fleeg@parc.xerox.com hp://www.parc.xerox.com/fblack,fleeg/

More information

SENSING using 3D technologies, structured light cameras

SENSING using 3D technologies, structured light cameras IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 39, NO. 10, OCTOBER 2017 2045 Real-Time Enhancemen of Dynamic Deph Videos wih Non-Rigid Deformaions Kassem Al Ismaeil, Suden Member,

More information

Dynamic Depth Recovery from Multiple Synchronized Video Streams 1

Dynamic Depth Recovery from Multiple Synchronized Video Streams 1 Dynamic Deph Recoery from Muliple ynchronized Video reams Hai ao, Harpree. awhney, and Rakesh Kumar Deparmen of Compuer Engineering arnoff Corporaion Uniersiy of California a ana Cruz Washingon Road ana

More information

In Proceedings of CVPR '96. Structure and Motion of Curved 3D Objects from. using these methods [12].

In Proceedings of CVPR '96. Structure and Motion of Curved 3D Objects from. using these methods [12]. In Proceedings of CVPR '96 Srucure and Moion of Curved 3D Objecs from Monocular Silhouees B Vijayakumar David J Kriegman Dep of Elecrical Engineering Yale Universiy New Haven, CT 652-8267 Jean Ponce Compuer

More information

Shortest Path Algorithms. Lecture I: Shortest Path Algorithms. Example. Graphs and Matrices. Setting: Dr Kieran T. Herley.

Shortest Path Algorithms. Lecture I: Shortest Path Algorithms. Example. Graphs and Matrices. Setting: Dr Kieran T. Herley. Shores Pah Algorihms Background Seing: Lecure I: Shores Pah Algorihms Dr Kieran T. Herle Deparmen of Compuer Science Universi College Cork Ocober 201 direced graph, real edge weighs Le he lengh of a pah

More information

Image Content Representation

Image Content Representation Image Conen Represenaion Represenaion for curves and shapes regions relaionships beween regions E.G.M. Perakis Image Represenaion & Recogniion 1 Reliable Represenaion Uniqueness: mus uniquely specify an

More information

Lemonia Ragia and Stephan Winter 1 CONTRIBUTIONS TO A QUALITY DESCRIPTION OF AREAL OBJECTS IN SPATIAL DATA SETS

Lemonia Ragia and Stephan Winter 1 CONTRIBUTIONS TO A QUALITY DESCRIPTION OF AREAL OBJECTS IN SPATIAL DATA SETS D. Frisch, M. Englich & M. Seser, eds, 'IAPRS', Vol. 32/, ISPRS Commission IV Symposium on GIS - Beween Visions and Applicaions, Sugar, Germany. Lemonia Ragia and Sephan Winer 1 CONTRIBUTIONS TO A QUALITY

More information

AUTOMATIC 3D FACE REGISTRATION WITHOUT INITIALIZATION

AUTOMATIC 3D FACE REGISTRATION WITHOUT INITIALIZATION Chaper 3 AUTOMATIC 3D FACE REGISTRATION WITHOUT INITIALIZATION A. Koschan, V. R. Ayyagari, F. Boughorbel, and M. A. Abidi Imaging, Roboics, and Inelligen Sysems Laboraory, The Universiy of Tennessee, 334

More information

Coded Caching with Multiple File Requests

Coded Caching with Multiple File Requests Coded Caching wih Muliple File Requess Yi-Peng Wei Sennur Ulukus Deparmen of Elecrical and Compuer Engineering Universiy of Maryland College Park, MD 20742 ypwei@umd.edu ulukus@umd.edu Absrac We sudy a

More information

A time-space consistency solution for hardware-in-the-loop simulation system

A time-space consistency solution for hardware-in-the-loop simulation system Inernaional Conference on Advanced Elecronic Science and Technology (AEST 206) A ime-space consisency soluion for hardware-in-he-loop simulaion sysem Zexin Jiang a Elecric Power Research Insiue of Guangdong

More information

Lecture 18: Mix net Voting Systems

Lecture 18: Mix net Voting Systems 6.897: Advanced Topics in Crypography Apr 9, 2004 Lecure 18: Mix ne Voing Sysems Scribed by: Yael Tauman Kalai 1 Inroducion In he previous lecure, we defined he noion of an elecronic voing sysem, and specified

More information

Spline Curves. Color Interpolation. Normal Interpolation. Last Time? Today. glshademodel (GL_SMOOTH); Adjacency Data Structures. Mesh Simplification

Spline Curves. Color Interpolation. Normal Interpolation. Last Time? Today. glshademodel (GL_SMOOTH); Adjacency Data Structures. Mesh Simplification Las Time? Adjacency Daa Srucures Spline Curves Geomeric & opologic informaion Dynamic allocaion Efficiency of access Mesh Simplificaion edge collapse/verex spli geomorphs progressive ransmission view-dependen

More information

Robot localization under perceptual aliasing conditions based on laser reflectivity using particle filter

Robot localization under perceptual aliasing conditions based on laser reflectivity using particle filter Robo localizaion under percepual aliasing condiions based on laser refleciviy using paricle filer DongXiang Zhang, Ryo Kurazume, Yumi Iwashia, Tsuomu Hasegawa Absrac Global localizaion, which deermines

More information

Quantitative macro models feature an infinite number of periods A more realistic (?) view of time

Quantitative macro models feature an infinite number of periods A more realistic (?) view of time INFINIE-HORIZON CONSUMPION-SAVINGS MODEL SEPEMBER, Inroducion BASICS Quaniaive macro models feaure an infinie number of periods A more realisic (?) view of ime Infinie number of periods A meaphor for many

More information

COSC 3213: Computer Networks I Chapter 6 Handout # 7

COSC 3213: Computer Networks I Chapter 6 Handout # 7 COSC 3213: Compuer Neworks I Chaper 6 Handou # 7 Insrucor: Dr. Marvin Mandelbaum Deparmen of Compuer Science York Universiy F05 Secion A Medium Access Conrol (MAC) Topics: 1. Muliple Access Communicaions:

More information

Virtual Recovery of Excavated Archaeological Finds

Virtual Recovery of Excavated Archaeological Finds Virual Recovery of Excavaed Archaeological Finds Jiang Yu ZHENG, Zhong Li ZHANG*, Norihiro ABE Kyushu Insiue of Technology, Iizuka, Fukuoka 820, Japan *Museum of he Terra-Coa Warrlors and Horses, Lin Tong,

More information

The Impact of Product Development on the Lifecycle of Defects

The Impact of Product Development on the Lifecycle of Defects The Impac of Produc Developmen on he Lifecycle of Rudolf Ramler Sofware Compeence Cener Hagenberg Sofware Park 21 A-4232 Hagenberg, Ausria +43 7236 3343 872 rudolf.ramler@scch.a ABSTRACT This paper invesigaes

More information

IntentSearch:Capturing User Intention for One-Click Internet Image Search

IntentSearch:Capturing User Intention for One-Click Internet Image Search JOURNAL OF L A T E X CLASS FILES, VOL. 6, NO. 1, JANUARY 2010 1 InenSearch:Capuring User Inenion for One-Click Inerne Image Search Xiaoou Tang, Fellow, IEEE, Ke Liu, Jingyu Cui, Suden Member, IEEE, Fang

More information

Effects needed for Realism. Ray Tracing. Ray Tracing: History. Outline. Foundations of Computer Graphics (Fall 2012)

Effects needed for Realism. Ray Tracing. Ray Tracing: History. Outline. Foundations of Computer Graphics (Fall 2012) Foundaions of ompuer Graphics (Fall 2012) S 184, Lecure 16: Ray Tracing hp://ins.eecs.berkeley.edu/~cs184 Effecs needed for Realism (Sof) Shadows Reflecions (Mirrors and Glossy) Transparency (Waer, Glass)

More information

Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases

Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases Lmarks: A New Model for Similariy-Based Paern Querying in Time Series Daabases Chang-Shing Perng Haixun Wang Sylvia R. Zhang D. So Parker perng@cs.ucla.edu hxwang@cs.ucla.edu Sylvia Zhang@cle.com so@cs.ucla.edu

More information

User Adjustable Process Scheduling Mechanism for a Multiprocessor Embedded System

User Adjustable Process Scheduling Mechanism for a Multiprocessor Embedded System Proceedings of he 6h WSEAS Inernaional Conference on Applied Compuer Science, Tenerife, Canary Islands, Spain, December 16-18, 2006 346 User Adjusable Process Scheduling Mechanism for a Muliprocessor Embedded

More information

MATH Differential Equations September 15, 2008 Project 1, Fall 2008 Due: September 24, 2008

MATH Differential Equations September 15, 2008 Project 1, Fall 2008 Due: September 24, 2008 MATH 5 - Differenial Equaions Sepember 15, 8 Projec 1, Fall 8 Due: Sepember 4, 8 Lab 1.3 - Logisics Populaion Models wih Harvesing For his projec we consider lab 1.3 of Differenial Equaions pages 146 o

More information

An Iterative Scheme for Motion-Based Scene Segmentation

An Iterative Scheme for Motion-Based Scene Segmentation An Ieraive Scheme for Moion-Based Scene Segmenaion Alexander Bachmann and Hildegard Kuehne Deparmen for Measuremen and Conrol Insiue for Anhropomaics Universiy of Karlsruhe (H), 76 131 Karlsruhe, Germany

More information

A new algorithm for small object tracking based on super-resolution technique

A new algorithm for small object tracking based on super-resolution technique A new algorihm for small objec racking based on super-resoluion echnique Yabunayya Habibi, Dwi Rana Sulisyaningrum, and Budi Seiyono Ciaion: AIP Conference Proceedings 1867, 020024 (2017); doi: 10.1063/1.4994427

More information

Motor Control. 5. Control. Motor Control. Motor Control

Motor Control. 5. Control. Motor Control. Motor Control 5. Conrol In his chaper we will do: Feedback Conrol On/Off Conroller PID Conroller Moor Conrol Why use conrol a all? Correc or wrong? Supplying a cerain volage / pulsewidh will make he moor spin a a cerain

More information

Image Based Computer-Aided Manufacturing Technology

Image Based Computer-Aided Manufacturing Technology Sensors & Transducers 03 by IFSA hp://www.sensorsporal.com Image Based Compuer-Aided Manufacuring Technology Zhanqi HU Xiaoqin ZHANG Jinze LI Wei LI College of Mechanical Engineering Yanshan Universiy

More information

A Tool for Multi-Hour ATM Network Design considering Mixed Peer-to-Peer and Client-Server based Services

A Tool for Multi-Hour ATM Network Design considering Mixed Peer-to-Peer and Client-Server based Services A Tool for Muli-Hour ATM Nework Design considering Mied Peer-o-Peer and Clien-Server based Services Conac Auhor Name: Luis Cardoso Company / Organizaion: Porugal Telecom Inovação Complee Mailing Address:

More information

Point Cloud Representation of 3D Shape for Laser- Plasma Scanning 3D Display

Point Cloud Representation of 3D Shape for Laser- Plasma Scanning 3D Display Poin Cloud Represenaion of 3D Shape for Laser- Plasma Scanning 3D Displa Hiroo Ishikawa and Hideo Saio Keio Universi E-mail {hiroo, saio}@ozawa.ics.keio.ac.jp Absrac- In his paper, a mehod of represening

More information

Nonparametric CUSUM Charts for Process Variability

Nonparametric CUSUM Charts for Process Variability Journal of Academia and Indusrial Research (JAIR) Volume 3, Issue June 4 53 REEARCH ARTICLE IN: 78-53 Nonparameric CUUM Chars for Process Variabiliy D.M. Zombade and V.B. Ghue * Dep. of aisics, Walchand

More information

FACIAL ACTION TRACKING USING PARTICLE FILTERS AND ACTIVE APPEARANCE MODELS. Soumya Hamlaoui & Franck Davoine

FACIAL ACTION TRACKING USING PARTICLE FILTERS AND ACTIVE APPEARANCE MODELS. Soumya Hamlaoui & Franck Davoine FACIAL ACTION TRACKING USING PARTICLE FILTERS AND ACTIVE APPEARANCE MODELS Soumya Hamlaoui & Franck Davoine HEUDIASYC Mixed Research Uni, CNRS / Compiègne Universiy of Technology BP 20529, 60205 Compiègne

More information

Video-Based Face Recognition Using Probabilistic Appearance Manifolds

Video-Based Face Recognition Using Probabilistic Appearance Manifolds Video-Based Face Recogniion Using Probabilisic Appearance Manifolds Kuang-Chih Lee Jeffrey Ho Ming-Hsuan Yang David Kriegman klee10@uiuc.edu jho@cs.ucsd.edu myang@honda-ri.com kriegman@cs.ucsd.edu Compuer

More information

Learning nonlinear appearance manifolds for robot localization

Learning nonlinear appearance manifolds for robot localization Learning nonlinear appearance manifolds for robo localizaion Jihun Hamm, Yuanqing Lin, and Daniel. D. Lee GRAS Lab, Deparmen of Elecrical and Sysems Engineering Universiy of ennsylvania, hiladelphia, A

More information

A Formalization of Ray Casting Optimization Techniques

A Formalization of Ray Casting Optimization Techniques A Formalizaion of Ray Casing Opimizaion Techniques J. Revelles, C. Ureña Dp. Lenguajes y Sisemas Informáicos, E.T.S.I. Informáica, Universiy of Granada, Spain e-mail: [jrevelle,almagro]@ugr.es URL: hp://giig.ugr.es

More information

A Hierarchical Object Recognition System Based on Multi-scale Principal Curvature Regions

A Hierarchical Object Recognition System Based on Multi-scale Principal Curvature Regions A Hierarchical Objec Recogniion Sysem Based on Muli-scale Principal Curvaure Regions Wei Zhang, Hongli Deng, Thomas G Dieerich and Eric N Morensen School of Elecrical Engineering and Compuer Science Oregon

More information

LOW-VELOCITY IMPACT LOCALIZATION OF THE COMPOSITE TUBE USING A NORMALIZED CROSS-CORRELATION METHOD

LOW-VELOCITY IMPACT LOCALIZATION OF THE COMPOSITE TUBE USING A NORMALIZED CROSS-CORRELATION METHOD 21 s Inernaional Conference on Composie Maerials Xi an, 20-25 h Augus 2017 LOW-VELOCITY IMPACT LOCALIZATION OF THE COMPOSITE TUBE USING A NORMALIZED CROSS-CORRELATION METHOD Hyunseok Kwon 1, Yurim Park

More information

A Fast Non-Uniform Knots Placement Method for B-Spline Fitting

A Fast Non-Uniform Knots Placement Method for B-Spline Fitting 2015 IEEE Inernaional Conference on Advanced Inelligen Mecharonics (AIM) July 7-11, 2015. Busan, Korea A Fas Non-Uniform Knos Placemen Mehod for B-Spline Fiing T. Tjahjowidodo, VT. Dung, and ML. Han Absrac

More information

Reconstruct scene geometry from two or more calibrated images. scene point. image plane. Reconstruct scene geometry from two or more calibrated images

Reconstruct scene geometry from two or more calibrated images. scene point. image plane. Reconstruct scene geometry from two or more calibrated images Sereo and Moion The Sereo Problem Reconsrc scene geomer from wo or more calibraed images scene poin focal poin image plane Sereo The Sereo Problem Reconsrc scene geomer from wo or more calibraed images

More information

Robust 3D Visual Tracking Using Particle Filtering on the SE(3) Group

Robust 3D Visual Tracking Using Particle Filtering on the SE(3) Group Robus 3D Visual Tracking Using Paricle Filering on he SE(3) Group Changhyun Choi and Henrik I. Chrisensen Roboics & Inelligen Machines, College of Compuing Georgia Insiue of Technology Alana, GA 3332,

More information

An efficient approach to improve throughput for TCP vegas in ad hoc network

An efficient approach to improve throughput for TCP vegas in ad hoc network Inernaional Research Journal of Engineering and Technology (IRJET) e-issn: 395-0056 Volume: 0 Issue: 03 June-05 www.irje.ne p-issn: 395-007 An efficien approach o improve hroughpu for TCP vegas in ad hoc

More information

Optimal Crane Scheduling

Optimal Crane Scheduling Opimal Crane Scheduling Samid Hoda, John Hooker Laife Genc Kaya, Ben Peerson Carnegie Mellon Universiy Iiro Harjunkoski ABB Corporae Research EWO - 13 November 2007 1/16 Problem Track-mouned cranes move

More information

THE micro-lens array (MLA) based light field cameras,

THE micro-lens array (MLA) based light field cameras, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL., NO., A Generic Muli-Projecion-Cener Model and Calibraion Mehod for Ligh Field Cameras Qi hang, Chunping hang, Jinbo Ling, Qing Wang,

More information

Motion Level-of-Detail: A Simplification Method on Crowd Scene

Motion Level-of-Detail: A Simplification Method on Crowd Scene Moion Level-of-Deail: A Simplificaion Mehod on Crowd Scene Absrac Junghyun Ahn VR lab, EECS, KAIST ChocChoggi@vr.kais.ac.kr hp://vr.kais.ac.kr/~zhaoyue Recen echnological improvemen in characer animaion

More information

3-D Object Modeling and Recognition for Telerobotic Manipulation

3-D Object Modeling and Recognition for Telerobotic Manipulation Research Showcase @ CMU Roboics Insiue School of Compuer Science 1995 3-D Objec Modeling and Recogniion for Teleroboic Manipulaion Andrew Johnson Parick Leger Regis Hoffman Marial Heber James Osborn Follow

More information

Weighted Voting in 3D Random Forest Segmentation

Weighted Voting in 3D Random Forest Segmentation Weighed Voing in 3D Random Fores Segmenaion M. Yaqub,, P. Mahon 3, M. K. Javaid, C. Cooper, J. A. Noble NDORMS, Universiy of Oxford, IBME, Deparmen of Engineering Science, Universiy of Oxford, 3 MRC Epidemiology

More information

Robust Segmentation and Tracking of Colored Objects in Video

Robust Segmentation and Tracking of Colored Objects in Video 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

More information

Tracking a Large Number of Objects from Multiple Views

Tracking a Large Number of Objects from Multiple Views Tracking a Large Number of Objecs from Muliple Views Zheng Wu 1, Nickolay I. Hrisov 2, Tyson L. Hedrick 3, Thomas H. Kun 2, Margri Beke 1 1 Deparmen of Compuer Science, Boson Universiy 2 Deparmen of Biology,

More information

Tracking a Large Number of Objects from Multiple Views

Tracking a Large Number of Objects from Multiple Views Boson Universiy Compuer Science Deparmen Technical Repor BUCS-TR 2009-005 Tracking a Large Number of Objecs from Muliple Views Zheng Wu 1, Nickolay I. Hrisov 2, Tyson L. Hedrick 3, Thomas H. Kun 2, Margri

More information

ACQUIRING high-quality and well-defined depth data. Online Temporally Consistent Indoor Depth Video Enhancement via Static Structure

ACQUIRING high-quality and well-defined depth data. Online Temporally Consistent Indoor Depth Video Enhancement via Static Structure SUBMITTED TO TRANSACTION ON IMAGE PROCESSING 1 Online Temporally Consisen Indoor Deph Video Enhancemen via Saic Srucure Lu Sheng, Suden Member, IEEE, King Ngi Ngan, Fellow, IEEE, Chern-Loon Lim and Songnan

More information

Real time 3D face and facial feature tracking

Real time 3D face and facial feature tracking J Real-Time Image Proc (2007) 2:35 44 DOI 10.1007/s11554-007-0032-2 ORIGINAL RESEARCH PAPER Real ime 3D face and facial feaure racking Fadi Dornaika Æ Javier Orozco Received: 23 November 2006 / Acceped:

More information

FIELD PROGRAMMABLE GATE ARRAY (FPGA) AS A NEW APPROACH TO IMPLEMENT THE CHAOTIC GENERATORS

FIELD PROGRAMMABLE GATE ARRAY (FPGA) AS A NEW APPROACH TO IMPLEMENT THE CHAOTIC GENERATORS FIELD PROGRAMMABLE GATE ARRAY (FPGA) AS A NEW APPROACH TO IMPLEMENT THE CHAOTIC GENERATORS Mohammed A. Aseeri and M. I. Sobhy Deparmen of Elecronics, The Universiy of Ken a Canerbury Canerbury, Ken, CT2

More information

Chapter 3 MEDIA ACCESS CONTROL

Chapter 3 MEDIA ACCESS CONTROL Chaper 3 MEDIA ACCESS CONTROL Overview Moivaion SDMA, FDMA, TDMA Aloha Adapive Aloha Backoff proocols Reservaion schemes Polling Disribued Compuing Group Mobile Compuing Summer 2003 Disribued Compuing

More information

Graffiti Detection Using Two Views

Graffiti Detection Using Two Views Graffii Deecion Using wo Views Luigi Di Sefano Federico ombari Alessandro Lanza luigi.disefano@unibo.i federico.ombari@unibo.i alanza@arces.unibo.i Sefano Maoccia sefano.maoccia@unibo.i Sefano Moni sefano.moni3@sudio.unibo.i

More information

C 0 C 1. p 1 (x 0,y 0 ) p 2 I 1. p 3. (x 0,y 0,d) C 2 C 3 I 2 I 3

C 0 C 1. p 1 (x 0,y 0 ) p 2 I 1. p 3. (x 0,y 0,d) C 2 C 3 I 2 I 3 c 1998 IEEE. Proc. of In. Conference on Compuer Vision, Bombai, January 1998 1 A Maximum-Flow Formulaion of he N-camera Sereo Corresponence Problem Sebasien Roy Ingemar J. Cox NEC Research Insiue Inepenence

More information

Robust Visual Tracking for Multiple Targets

Robust Visual Tracking for Multiple Targets Robus Visual Tracking for Muliple Targes Yizheng Cai, Nando de Freias, and James J. Lile Universiy of Briish Columbia, Vancouver, B.C., Canada, V6T 1Z4 {yizhengc, nando, lile}@cs.ubc.ca Absrac. We address

More information

MOTION DETECTORS GRAPH MATCHING LAB PRE-LAB QUESTIONS

MOTION DETECTORS GRAPH MATCHING LAB PRE-LAB QUESTIONS NME: TE: LOK: MOTION ETETORS GRPH MTHING L PRE-L QUESTIONS 1. Read he insrucions, and answer he following quesions. Make sure you resae he quesion so I don hae o read he quesion o undersand he answer..

More information

Viewpoint Invariant 3D Landmark Model Inference from Monocular 2D Images Using Higher-Order Priors

Viewpoint Invariant 3D Landmark Model Inference from Monocular 2D Images Using Higher-Order Priors Viewpoin Invarian 3D Landmark Model Inference from Monocular 2D Images Using Higher-Order Priors Chaohui Wang 1,2, Yun Zeng 3, Loic Simon 1, Ioannis Kakadiaris 4, Dimiris Samaras 3, Nikos Paragios 1,2

More information

Detection Tracking and Recognition of Human Poses for a Real Time Spatial Game

Detection Tracking and Recognition of Human Poses for a Real Time Spatial Game Deecion Tracking and Recogniion of Human Poses for a Real Time Spaial Game Feifei Huo, Emile A. Hendriks, A.H.J. Oomes Delf Universiy of Technology The Neherlands f.huo@udelf.nl Pascal van Beek, Remco

More information

Parallax360: Stereoscopic 360 Scene Representation for Head-Motion Parallax

Parallax360: Stereoscopic 360 Scene Representation for Head-Motion Parallax Parallax360: Sereoscopic 360 Scene Represenaion for Head-Moion Parallax Bicheng Luo, Feng Xu, Chrisian Richard and Jun-Hai Yong (b) (c) (a) (d) (e) Fig. 1. We propose a novel image-based represenaion o

More information

Proceeding of the 6 th International Symposium on Artificial Intelligence and Robotics & Automation in Space: i-sairas 2001, Canadian Space Agency,

Proceeding of the 6 th International Symposium on Artificial Intelligence and Robotics & Automation in Space: i-sairas 2001, Canadian Space Agency, Proceeding of he 6 h Inernaional Symposium on Arificial Inelligence and Roboics & Auomaion in Space: i-sairas 00, Canadian Space Agency, S-Huber, Quebec, Canada, June 8-, 00. Muli-resoluion Mapping Using

More information

Michiel Helder and Marielle C.T.A Geurts. Hoofdkantoor PTT Post / Dutch Postal Services Headquarters

Michiel Helder and Marielle C.T.A Geurts. Hoofdkantoor PTT Post / Dutch Postal Services Headquarters SHORT TERM PREDICTIONS A MONITORING SYSTEM by Michiel Helder and Marielle C.T.A Geurs Hoofdkanoor PTT Pos / Duch Posal Services Headquarers Keywords macro ime series shor erm predicions ARIMA-models faciliy

More information