Multi-Viewpoint Video Coding with MPEG-2 Compatibility. Belle L. Tseng and Dimitris Anastassiou. Columbia University New York, N.Y.

Size: px
Start display at page:

Download "Multi-Viewpoint Video Coding with MPEG-2 Compatibility. Belle L. Tseng and Dimitris Anastassiou. Columbia University New York, N.Y."

Transcription

1 Muli-Viewpoin Video oding wih MPEG-2 ompaibiliy Belle L. Tseng and Dimiris Anasassiou olumbia niversiy New York, N.Y SA Absrac An ecien video coding scheme is presened as an exension of he MPEG-2 sandard o accommodae he ransmission of muliple viewpoin sequences on bandwidh-limied channels. Wih he goal of compression and speed, he proposed approach incorporaes a variey of exising compuer graphics ools and echniques. onsrucion of each viewpoin image is prediced using a combinaion of perspecive projecion of 3D models, exure mapping, and digial image warping. Immediae applicaion of he coding specicaion is foreseeable in sysems wih hardware-based realime rendering capabiliies, hus providing fas and accurae consrucions of muliple perspecives. 1 Inroducion Recen ineress in 3D echnologies promp he addiion of deph impression ono he oherwise common 2D video signals. Two major processes o perceiving 3D can be caegorized. The rs is due o he wo slighly dieren perspecives of he world oered simulaneously o our lef and righ eyes. The human visual sysem hen convers hese wo sereo images ino one single fused 3D percepion. The oher approach o sense deph, even wih only one eye-viewpoin, is hrough moion parallax. Due o moion from our head movemens, he relaive objec displacemens of he resuling perspecive view are sucien cues in deriving he 3D sensaion. Accordingly, inour presenaion, he deph appearance is conribued by boh processes. Amuli-viewpoin video, muliview for shor, is a 3D exension of he radiional movie sequence, in ha here are muliple perspecives of he same scene a any one insance in ime. omparable o a movie made by a sequence of holograms, a muliview video oers a similar lookaround capabiliy. An ideal muliview sysem allows any user o wach a rue 3D sereoscopic sequence from any perspecive he viewer chooses. Such a sysem has pracical uses in ineracive applicaions, medical echnologies, educaional and raining demonsraions, remoe sensing developmens, and is a sep owards virual realiy. 1

2 Wih he developmen of digial video echnology, a video daa compression sandard, namely he second Moion Picure Expers Group specicaion (MPEG-2), has been adoped by he Inernaional Sandards Organizaion (ISO) and he Inernaional Telecommunicaions nion (IT). MPEG-2 species he coding process for one video sequence; deailed descripions can be found in [1]. Recenly, MPEG-2 has also been shown o be applicable o wo sequences of sereoscopic signals hrough he use of spaial and emporal scalabiliy exensions[2, 3, 4, 5]. However, exending he number of video viewpoins beyond wo canno be done pracically by using he same mehodology. For his moivaion, a novel muliview codec is presened o complemen he MPEG-2 sandard. 2 Geomeric Deniions and Noaions An ordinary 2D video sequence oers only one perspecive of he acquired scene. Fora3D viewing experience, a leas 2 viewpoins are required o obain he deph impression from he lef and righ perspecives of a sereoscopic signal. In a muliview sysem, muliple viewpoins are involved. Le N be he number of viewpoin sequences, where he minimum number of viewpoins is equal o 3 for he presened specicaion, i.e., N 3. The number of viewpoins mus be exendable in he fuure, hus accommodaion of addiional viewpoins is an essenial feaure. One cenral viewpoin image is designaed o be he principal view in which he oher muliview images are prediced from. The cenral image is denoed by I from viewpoin V. The cenral viewpoin, usually he middle viewpoin, is chosen so ha is image has he highes collecion of overlapping objecs wih each of he oher viewpoin images. In his manner, he cenral viewpoin image can be used o inerpolae and predic mos of he oher views. The oher muliview images are designaed by I X from viewpoins V X. An example of four addiional views is illusraed in Figure 1, where cameras posiioned a viewpoins V X = fv L ;V R ;V T ;V B g capure respecive images I X = fi L ;I R ;I T ;I B g corresponding o he Lef, Righ, Top, and Boom. These camera-capured images available a he encoder are referred o as real views, whereas images no direcly aken by a camera, bu derived by predicion or inerpolaion mehods, are called virual views. Virual picures include hose viewpoin images seen beween wo real cameras, hus having such virual consrucions allow he viewer o see a smooher video ransiion beween wo real views. The image coordinae sysem corresponding o each viewpoin is dened as (X i ;Y i ), where viewpoin index i = f; L; R; T; Bg. Le he global recangular coordinae sysem (X; Y; Z) be 2

3 dened as corresponding o he image coordinae sysem (X ;Y ) of he cenral viewpoin, where Z denes he orhogonal axis from he cenral image plane. The camera posiion is represened by he global coordinaes (vx i ;vy i ;vz i ), and he camera zooming parameer is described by va i. In addiion, he camera roaions are given by he horizonal panning angle vb i and he verical iling angle vc i. The oal viewpoin vecor for some viewpoin index i is denoed as V i =[vx i ;vy i ;vz i ;va i ;vb i ;vc i ]. The oher viewpoin vecors are obained relaive o he cenral viewpoin, V =[0; 0; 0; 1; 0; 0]. Given he acquisiion camera conguraions, he oher viewpoin vecors are relaive ranslaions and roaions wih respec o he cenral viewpoin. If camera conguraions are unknown, hen locaing a couple of xed poins beween muliviews allows deerminaion of he relaive orienaion ransformaion[6, 7]. 3 Deph and Dispariy Esimaion Possessing he deph of an objec in one image allows for geomeric predicion of he objec locaion in all oher viewpoin images. onsequenly, our desire o deermine he deph of every objec in a scene permis consrucion of he scene from any viewpoin. The deph of an objec can be geomerically calculaed if wo or more perspecives of he objec are given, as in a collecion of muliviews. Firs, he posiions of he objec in each of he available viewpoin images mus be locaed; his problem is widely known as he correspondence problem. Afer locaing he objec posiions from wo views, he dierence in image coordinaes is ermed dispariy. Following, i can be shown mahemaically ha he deph is inversely proporional o he derived dispariy[7]. nder he block-based moion characerizaion consrain of MPEG-2, one dispariy vecor corresponding o each block of an image can be incorporaed for predicion of a second image. Block-based dispariy compensaion is he dominan approach for he coding of sereoscopic video sequences. These approaches are sucien for coding and ransmission of wo sereo signals wihou enailing rue deph deails o he decoding sysem. For accepable predicions of muli-viewpoin images however, accurae deph informaion for every pixel is required for muliple spaially-coninuous inerpolaions of inermediae viewpoin images. Thus a deph map is required covering every pixel, whose quanized values can be ransmied as he gray-level inensiy of a second \image". Since he majoriy of he deph image is quie a, he deph map can be considerably compressed. To obain a dense deph map, many ecien echniques have been developed for sereo image pairs as well as for muliple views, including [7, 8, 9, 10]. 3

4 4 MPEG-2 ompaible Muli-Viewpoin Video oding The delivery of wo viewpoin video sequences is accomplished by he scalabiliy exensions of he MPEG-2 video coding sandard, where spaial predicions are obained by dispariycompensaion on a macroblock basis. As menioned in Secion 3 however, for muliview video coding a dense deph map is required for accurae predicions of muliple views. Seeking compaibiliy wih he compression sandard, an ad hoc group of MPEG-2 is esablished o invesigae a new prole for muli-viewpoin sysems[11]. For conformiy, he muliview prole is o supplemen he main prole of MPEG-2 so ha all sandardized sysems are capable of deciphering a leas one video sequence from one viewpoin. onsequenly, he cenral viewpoin is seleced o be processed and ransmied in accordance wih he main prole. The remainder of his secion is devoed o describe he proposed encoding and decoding sysems for he processing of he oher viewpoins. 4.1 Muli-Viewpoin Encoding Sysem A block diagram of he proposed muliview encoding sysem is illusraed in Figure 2 for ve muli-viewpoin sequences: I ;I L ;I R ;I T ; and I B. Firs, deermine all viewpoin vecors, V ;V L ;V R ;V T ; and V B ; from he acquisiion camera conguraions posiioned in Figure 1. Find he cenral viewpoin image, in our case I,achieving he bes predicions for he oher views. The cenral image sequence I encoded bisream for I receiver-reconsruced cenral images, denoed c I. is hen processed in he main prole of MPEG-2. Following, he is ready for ransmission, and in parallel is decoded o deermine he Saring wih he cenral viewpoin image I a ime, a deph value z = D (x ;y )is calculaed for every pixel I (x ;y ), hus forming a deph map image, named D, corresponding o he cenral viewpoin image. Aferwards, encode he deph map D compressible image, and ransmi he encoded bisream for D. as a secondary highly Simulaneously, he encoded bisream for D is decoded o deermine he received-reconsruced deph map, assigned d D. Obain a 3D mesh model represenaion[12], called M, for he cenral image I c of ime by associaing each coordinae pair (x ;y ) wih is corresponding deph value d D (x ;y ). onsequenly, he 3D surface exure is derived from he 2D image inensiy I (x ;y ), and a graphical model of he scene is obained. This geomeric represenaion oers ease in inerpolaing dieren viewpoins. Similar o rendering approaches in compuer graphics[13, 14], given a 3D geomerical model and is associaed exure inensiy, every viewpoin can be eecively and ecienly 4

5 consruced by simple geomeric ransformaions followed by 3D exure mapping. Furhermore, inerpolaing virual viewpoin images is faciliaed and feasible. Selec a non-cenral viewpoin, designaed as V X,chosen from he se of original real viewpoins in which he bes consrucion of is image I X is desired for ime. The selecion of viewpoin V X is based on a round-robin schedule where every viewpoin is successively seleced in a cycle. In Figure 3, a round-robin roaional scheme for four non-cenral views is shown where he selecion of each image is relaed o ime in he following manner: IL, I +1, R I +2, T I +3, B I +4, L I +5, R I +6, ec, T disinguished by a double border. Following, ransmi he seleced viewpoin vecor V X for ime. The nex sep is o inerpolae he seleced non-cenral viewpoin image, referred o as prediced image PI X,by rendering he 3D mesh model M in he specied viewpoin V X. This inerpolaion sep requires rendering a wireframe image of he desired viewpoin by simple geomeric ransformaions wih perspecive projecions of he mesh model, followed by exure mapping he corresponding areas of he cenral image ono he appropriae wireframe subsrucures. Subsequenly, calculae he predicion errors PE required for he nal reconsrucion of he seleced view, by examining he dierence beween he original image I X and he prediced image PI X. Finally, encode and ransmi he residual predicion errors PE. Before moving on o he nex frame, deermine if he allocaed bi rae allows ransmission of an addiional non-cenral image. If bandwidh allows, he predicion errors for anoher view can be deermined and send. Alernaively, a new round-robin schedule can be adoped where wo viewpoins are seleced for every ime, hus perfec reconsrucion is always achievable wih unlimied bandwidh. process sars all over for he nex frame. Subsequenly, he enire 4.2 Muli-Viewpoin Decoding Sysem A block diagram of he proposed muliview decoding sysem is illusraed in Figure 4 for consrucion of any viewpoin images. Firs, he received bisream of he cenral view is decoded on he main prole of MPEG-2, whose decoded images, denoed I c, are sored in memory. Second, he bisream of he deph map is decoded and hereafer referred o as D d. Nex, obain a 3D mesh model, called M, for he cenral image c I sore he model M for ime in memory. pon receiving he seleced viewpoin vecor V X as performed in he encoding sysem. onsequenly, Following, he predicion errors associaed wih he seleced viewpoin V X for ime, sore he vecor in memory. is decoded, denoed as d PE. Now, deermine he viewpoin requesed from he user a he decoding sysem for ime 5

6 , assigned V. Subsequenly, he user-requesed viewpoin image (wheher real or virual) is inerpolaed, referred o as prediced image PI, where he index designaes he appropriae viewpoin V. The predicion PI is consruced by rendering he 3D mesh model M in he specied viewpoin V by he same viewpoin image inerpolaion procedure as in he encoder. Deermine if he encoder-seleced non-cenral viewpoin V X is similar o he user-requesed viewpoin V. If he same, V = V X, hen he user can immediaely consruc he desired image. The nal reconsruced image c I X is obained by combining he predicion errors d PE wih he prediced image PIX. This kind of reconsrucion is hereafer dened as Type IPredicion. Following, he reconsruced non-cenral image c I X is sored in memory for laer reference. Saisfying he user's reques for his viewpoin image, he whole process is repeaed for he nex ime frame. On he oher hand, if he user did no reques he same viewpoin as he one seleced by he encoder, V 6= V X, he following seps are carried ou o generae such a view. Given he userrequesed viewpoin V, rerieve from memory he image I,f d corresponding o he neares pas image I reconsruced by atype I Predicion. Similarly, rerieve he image d I,b o he neares fuure image I corresponding reconsruced by atype I Predicion. For example, referring o Figure 3, if he user requess viewpoin V R a ime + 2, hen he fron image is I +1 R (f = 1) and he back image is I +5 R (b = 3). Due o he round robin schedule for one non-cenral viewpoin selecion during each ime frame, he maximum value of f and b is limied by N, 2, where N is he number of viewpoins. Noe ha a delay may be required in order o access d he fuure d image. Following, load he 3D mesh model M,f creaed a ime, f from I,f and D,f. In d parallel, load he 3D mesh model M +b creaed a ime + b from I +b d and D d +b. Nex, generae he fron mesh image MI,f by locaing a grid of xed poins on he image I,f. Also, generae d he back mesh image MI +b by locaing a corresponding grid of xed poins on he image I +b. Aferwards, generae he inermediae mesh-prediced image MPI for ime by digially image warping beween he fron mesh image MI,f and he back mesh image MI +b. The image warping echnique and generaing mesh images by locaing appropriae xed poins are well explained in [15]. Finally, a consrucion for I c is obained by combining he inermediae mesh-prediced image MPI wih he prediced image PI. The mehod for his nal combinaion is lef o he decoding sysem using any image fusion echniques[16][17], e.g., XOR operaor, average, ec. onsrucion of his nal image c I is ermed as Type IIPredicion. A his poin, if oher viewpoin images are requesed by he users, he predicion seps may be repeaed o generae oher muliviews. Oherwise he decoding process sars a he beginning o decode he nex ime frame. 6

7 5 onclusions The proposed MPEG-2 compaible video coding specicaion for muli-viewpoin sysems offers many advanages. In addiion o providing a srucural framework for he new muliview prole of he MPEG-2 coding sandard, his codec reains he same encoding and decoding processing for one basic viewpoin sequence based on he main prole. Furhermore, because he sandard only denes he bi-sream synax and he decoding process, here is freedom in designing very high qualiy encoders and very low-cos decoders. Similarly, he presened specicaion suggess he same exibiliy, providing for creaiviy and enhancemens in accordance wih advanced echnologies. Our novel conribuion o he muliview coding research is mainly due o he concep of combining 2D image processing wih 3D compuer graphics. Many exising compuer graphical ools and animaion faciliies, currenly available in hardware, oer speedy rendering capabiliies. Thus inerpolaion of any viewpoin image sequence can be generaed quickly and ecienly. Also, consrucion of non-ransmied virual viewpoins is possible due o availabiliy of 3D models. In rendering an image of a 3D surface srucure, he exured image daa are mapped ono corresponding hree-dimensional geomeric meshed surfaces. onsequenly, exure mapping auomaically incorporaes he fore-shorening eec of 3D surface curvaures. In addiion, since he exure is a 3D funcion of is posiion in he objec, racking of image poins is faciliaed by knowing he real world 3D coordinaes of each poin. Furhermore, addiional enhancemens on he consruced images may be obained by incorporaing oher graphical ools, e.g., illuminaion and shading. Thus, innovaive ideas can be freely augmened ono our sysem as echnologies progress. References [1] \MPEG Draf Inernaional Sandard. ISO/IE : Generic oding of Moving Picures and Associaed Audio: Video," [2] B. L. Tseng and D. Anasassiou, \ompaible video coding of sereoscopic sequences using MPEG-2's scalabiliy and inerlaced srucure," in In'l Workshop on HDTV '94, (Torino, Ialy), Oc [3] A. Puri, R. Kollaris, and B. Haskell, \Sereoscopic video compression using emporal scalabiliy," in Proceedings of SPIE Visual ommunicaions and Image Processing '95, (Taipei, Taiwan), May [4] T.-H. hiang and Y.-Q. Zhang, \Sereoscopic video coding," in Symposium on Mulimedia ommunicaions and Video oding, (New York iy), o appear in Oc [5] B. L. Tseng and D. Anasassiou, \Percepual adapive quanizaion of sereoscopic video coding using MPEG-2's emporal scalabiliy srucure," in Inernaional Workshop on Sereoscopic and Three Dimensional Imaging IWS3DI '95, (Sanorini, Greece), Sep [6] W. Burger and B. Bhanu, \Esimaing 3-D egomoion from perspecive image sequences," IEEE Transacions on Paern Analysis and Machine Inelligence, vol. 12, pp. 1040{1058, Nov

8 [7] B. K. P. Horn, Robo Vision. ambridge, Massachuses: The MIT Press, [8] S. Barnard and M. Fischler, \ompuaional sereo," AM ompuing Surveys, vol. 14, pp. 553{572, Dec [9] S. Mira, \Vision models for 3D surfaces," SPIE Vol 1826 Inelligen Robos and ompuer Vision XI, pp. 182{188, [10] B. L. Tseng and D. Anasassiou, \A heoreical sudy on an accurae reconsrucion of muliview images based on he vierbi algorihm," in Inernaional onference on Image Processing IIP '95, (Washingon, D..), Oc [11] T. Homma, \MPEG onribuion 95/N0861: Repor of he Ad Hoc Group on MPEG-2 Applicaions for Muli-Viewpoin Picures," Mar [12] G. Bozdagi, A. M. Tekalp, and L. Onural, \3-D moion esimaion and wireframe adapaion including phoomeric eecs for model-based coding of facial image sequences," IEEE Transacions on ircuis and Sysems for Video Technology, vol. 4, pp. 246{256, June [13] J. D. Foley, A. van Dam, S. K. Feiner, and J. F. Hughes, ompuer Graphics: Principles and Pracice. Reading, Massachuses: Addison Wesley Publishing ompany, second ed., [14] J. Neider, T. Davis, and M. Woo, OpenGL Programming Guide. Addison-Wesley Publishing, [15] G. Wolberg, Digial Image Warping. Los Alamios, alifornia: IEEE ompuer Sociey Press, [16].-P. Yeh, \Deph percepion based on fusion of sereo images," SPIE Vol Imaging Technologies and Applicaions, pp. 221{226, [17] Y. T. Zhou, \Muli-sensor image fusion," in Inernaional onference on Image Processing, (Ausin, Texas), Nov I T Top I L Lef I ener I R Righ I B Boom Figure 1: amera onguraion of Muliple Viewpoin Images 8

9 I alculae Deph D Deermine Viewpoin V X V X D Encode I Encode D Build and Accumulae 3D Srucure & Texure M Decode I I Decode D D Bisream for I Bisream for D Seleced Viewpoin V X D E O DI N G Inerpolae Viewpoin Image S Y ST E M PI X I L if V X = V L I R I T if V X = V R if V X = V T I X PE Encode PE Pred Error PE I B if V X = V B Figure 2: Block Diagram of Muliview Encoding Sysem 9

10 IMAGES from Viewpoins I I L I R I T I B V V L V R V T V B I I L I R I T I B Type I Type II I +1 I +1 L I +1 R I +1 T I +1 B T I M E I +2 I +2 L I +2 R I +2 T I +2 B I +3 I +3 L I +3 R I +3 T I +3 B I +4 I +4 L I +4 R I +4 T I +4 B I +5 I +5 L I +5 R I +5 T I +5 B Figure 3: A Round-Robin Roaional Schedule for Encoder Selecion of One Viewpoin 10

11 Bisream for I Decode I I I V X I I X Bisream for D Decode D D Build and Accumulae 3D Srucure & Texure M FRAME STORE MEMORY V E N O DI N G ser-requesed Viewpoin V V M Inerpolae Viewpoin Image M -f I -f Generae Mesh Image by locaing Fixed Poins I +b PI MI -f MI +b M +b Generae Mesh Image by locaing Fixed Poins S Y ST WARPING beween he Two Mesh Images E M MPI Seleced Viewpoin V X if V = V OMBINE X Prediced Image PI & Mesh Pred Image MPI I Pred Error PE Decode PE PE if V = V X I X Figure 4: Block Diagram of Muliview Decoding Sysem 11

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

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

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

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

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

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

INTERNATIONAL ORGANIZATION FOR STANDARDIZATION ORGANISATION INTERNATIONALE DE NORMALISATION ISO-IEC JTC1/SC29/WG11

INTERNATIONAL ORGANIZATION FOR STANDARDIZATION ORGANISATION INTERNATIONALE DE NORMALISATION ISO-IEC JTC1/SC29/WG11 INTERNATIONAL ORGANIZATION FOR STANDARDIZATION ORGANISATION INTERNATIONALE DE NORMALISATION ISO-IEC JTC1/SC29/WG11 CODING OF MOVING PICTRES AND ASSOCIATED ADIO ISO-IEC/JTC1/SC29/WG11 MPEG 95/ July 1995

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

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

Handling uncertainty in semantic information retrieval process

Handling uncertainty in semantic information retrieval process Handling uncerainy in semanic informaion rerieval process Chkiwa Mounira 1, Jedidi Anis 1 and Faiez Gargouri 1 1 Mulimedia, InfoRmaion sysems and Advanced Compuing Laboraory Sfax Universiy, Tunisia m.chkiwa@gmail.com,

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

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

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

Real-Time Avatar Animation Steered by Live Body Motion

Real-Time Avatar Animation Steered by Live Body Motion Real-Time Avaar Animaion Seered by Live Body Moion Oliver Schreer, Ralf Tanger, Peer Eiser, Peer Kauff, Bernhard Kaspar, and Roman Engler 3 Fraunhofer Insiue for Telecommunicaions/Heinrich-Herz-Insiu,

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

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

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

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

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

PART 1 REFERENCE INFORMATION CONTROL DATA 6400 SYSTEMS CENTRAL PROCESSOR MONITOR

PART 1 REFERENCE INFORMATION CONTROL DATA 6400 SYSTEMS CENTRAL PROCESSOR MONITOR . ~ PART 1 c 0 \,).,,.,, REFERENCE NFORMATON CONTROL DATA 6400 SYSTEMS CENTRAL PROCESSOR MONTOR n CONTROL DATA 6400 Compuer Sysems, sysem funcions are normally handled by he Monior locaed in a Peripheral

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

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

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

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

A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding

A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding Regular Issue A Review on Block Maching Moion Esimaion and Auomaa Theory based Approaches for Fracal Coding Shailesh D Kamble 1, Nileshsingh V Thakur 2, and Preei R Bajaj 3 1 Compuer Science & Engineering,

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

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

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

Reinforcement Learning by Policy Improvement. Making Use of Experiences of The Other Tasks. Hajime Kimura and Shigenobu Kobayashi

Reinforcement Learning by Policy Improvement. Making Use of Experiences of The Other Tasks. Hajime Kimura and Shigenobu Kobayashi Reinforcemen Learning by Policy Improvemen Making Use of Experiences of The Oher Tasks Hajime Kimura and Shigenobu Kobayashi Tokyo Insiue of Technology, JAPAN genfe.dis.iech.ac.jp, kobayasidis.iech.ac.jp

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

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

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

A NEW APPROACH FOR 3D MODELS TRANSMISSION

A NEW APPROACH FOR 3D MODELS TRANSMISSION A NEW APPROACH FOR 3D MODELS TRANSMISSION A. Guarnieri a, F. Piroi a, M. Ponin a, A. Veore a a CIRGEO, Inerdep. Research Cener of Carography, Phoogrammery, Remoe Sensing and GIS Universiy of Padova, Agripolis

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

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

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

STRING DESCRIPTIONS OF DATA FOR DISPLAY*

STRING DESCRIPTIONS OF DATA FOR DISPLAY* SLAC-PUB-383 January 1968 STRING DESCRIPTIONS OF DATA FOR DISPLAY* J. E. George and W. F. Miller Compuer Science Deparmen and Sanford Linear Acceleraor Cener Sanford Universiy Sanford, California Absrac

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

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

RGBD Data Based Pose Estimation: Why Sensor Fusion?

RGBD Data Based Pose Estimation: Why Sensor Fusion? 18h Inernaional Conference on Informaion Fusion Washingon, DC - July 6-9, 2015 RGBD Daa Based Pose Esimaion: Why Sensor Fusion? O. Serdar Gedik Deparmen of Compuer Engineering, Yildirim Beyazi Universiy,

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

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

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

Efficient Multi-view Video Coding using 3D Motion Estimation and Virtual Frame

Efficient Multi-view Video Coding using 3D Motion Estimation and Virtual Frame Neurocompuing journal homepage: www. elsevi er.c om Efficien Muli-view Video Coding using 3D Moion Esimaion and Virual Frame Manoranjan Paul* Cenre for Research in Complex Sysems, School of Compuing &

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

Ray Casting. Outline. Outline in Code

Ray Casting. Outline. Outline in Code Foundaions of ompuer Graphics Online Lecure 10: Ray Tracing 2 Nus and ols amera Ray asing Ravi Ramamoorhi Ouline amera Ray asing (choose ray direcions) Ray-objec inersecions Ray-racing ransformed objecs

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

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

Po,,ll. I Appll I APP2 I I App3 I. Illll Illlllll II Illlll Illll Illll Illll Illll Illll Illll Illll Illll Illll Illll Illlll Illl Illl Illl

Po,,ll. I Appll I APP2 I I App3 I. Illll Illlllll II Illlll Illll Illll Illll Illll Illll Illll Illll Illll Illll Illll Illlll Illl Illl Illl Illll Illlllll II Illlll Illll Illll Illll Illll Illll Illll Illll Illll Illll Illll Illlll Illl Illl Illl US 20110153728A1 (19) nied Saes (12) Paen Applicaion Publicaion (10) Pub. No.: S 2011/0153728

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

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

Video streaming over Vajda Tamás

Video streaming over Vajda Tamás Video sreaming over 802.11 Vajda Tamás Video No all bis are creaed equal Group of Picures (GoP) Video Sequence Slice Macroblock Picure (Frame) Inra (I) frames, Prediced (P) Frames or Bidirecional (B) Frames.

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

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

Scale Recovery for Monocular Visual Odometry Using Depth Estimated with Deep Convolutional Neural Fields

Scale Recovery for Monocular Visual Odometry Using Depth Estimated with Deep Convolutional Neural Fields Scale Recovery for Monocular Visual Odomery Using Deph Esimaed wih Deep Convoluional Neural Fields Xiaochuan Yin, Xiangwei Wang, Xiaoguo Du, Qijun Chen Tongji Universiy yinxiaochuan@homail.com,wangxiangwei.cpp@gmail.com,

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

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

Stereoscopic Neural Style Transfer

Stereoscopic Neural Style Transfer Sereoscopic Neural Syle Transfer Dongdong Chen 1 Lu Yuan 2, Jing Liao 2, Nenghai Yu 1, Gang Hua 2 1 Universiy of Science and Technology of China 2 Microsof Research cd722522@mail.usc.edu.cn, {luyuan,jliao}@microsof.com,

More information

An Improved Square-Root Nyquist Shaping Filter

An Improved Square-Root Nyquist Shaping Filter An Improved Square-Roo Nyquis Shaping Filer fred harris San Diego Sae Universiy fred.harris@sdsu.edu Sridhar Seshagiri San Diego Sae Universiy Seshigar.@engineering.sdsu.edu Chris Dick Xilinx Corp. chris.dick@xilinx.com

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

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

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

4 Error Control. 4.1 Issues with Reliable Protocols

4 Error Control. 4.1 Issues with Reliable Protocols 4 Error Conrol Jus abou all communicaion sysems aemp o ensure ha he daa ges o he oher end of he link wihou errors. Since i s impossible o build an error-free physical layer (alhough some shor links can

More information

MODEL BASED TECHNIQUE FOR VEHICLE TRACKING IN TRAFFIC VIDEO USING SPATIAL LOCAL FEATURES

MODEL BASED TECHNIQUE FOR VEHICLE TRACKING IN TRAFFIC VIDEO USING SPATIAL LOCAL FEATURES MODEL BASED TECHNIQUE FOR VEHICLE TRACKING IN TRAFFIC VIDEO USING SPATIAL LOCAL FEATURES Arun Kumar H. D. 1 and Prabhakar C. J. 2 1 Deparmen of Compuer Science, Kuvempu Universiy, Shimoga, India ABSTRACT

More information

RGB-D Object Tracking: A Particle Filter Approach on GPU

RGB-D Object Tracking: A Particle Filter Approach on GPU RGB-D Objec Tracking: A Paricle Filer Approach on GPU Changhyun Choi and Henrik I. Chrisensen Cener for Roboics & Inelligen Machines College of Compuing Georgia Insiue of Technology Alana, GA 3332, USA

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

Connections, displays and operating elements. 3 aux. 5 aux.

Connections, displays and operating elements. 3 aux. 5 aux. Taser PlusKapiel3:Taser3.1Taser Plus Meren2005V6280-561-0001/08 GB Connecions, displays and operaing elemens Taser Plus Arec/Anik/Trancen Operaing insrucions A 1 2 1 2 3 4 5 6 C B A B 3 aux. 7 8 9 aux.

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

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

Collision-Free and Curvature-Continuous Path Smoothing in Cluttered Environments

Collision-Free and Curvature-Continuous Path Smoothing in Cluttered Environments Collision-Free and Curvaure-Coninuous Pah Smoohing in Cluered Environmens Jia Pan 1 and Liangjun Zhang and Dinesh Manocha 3 1 panj@cs.unc.edu, 3 dm@cs.unc.edu, Dep. of Compuer Science, Universiy of Norh

More information

Projection & Interaction

Projection & Interaction Projecion & Ineracion Algebra of projecion Canonical viewing volume rackball inerface ransform Hierarchies Preview of Assignmen #2 Lecure 8 Comp 236 Spring 25 Projecions Our lives are grealy simplified

More information

Research Article Auto Coloring with Enhanced Character Registration

Research Article Auto Coloring with Enhanced Character Registration Compuer Games Technology Volume 2008, Aricle ID 35398, 7 pages doi:0.55/2008/35398 Research Aricle Auo Coloring wih Enhanced Characer Regisraion Jie Qiu, Hock Soon Seah, Feng Tian, Quan Chen, Zhongke Wu,

More information

Integro-differential splines and quadratic formulae

Integro-differential splines and quadratic formulae Inegro-differenial splines and quadraic formulae I.G. BUROVA, O. V. RODNIKOVA S. Peersburg Sae Universiy 7/9 Universiesaya nab., S.Persburg, 9934 Russia i.g.burova@spbu.ru, burovaig@mail.ru Absrac: This

More information

Improving the Efficiency of Dynamic Service Provisioning in Transport Networks with Scheduled Services

Improving the Efficiency of Dynamic Service Provisioning in Transport Networks with Scheduled Services Improving he Efficiency of Dynamic Service Provisioning in Transpor Neworks wih Scheduled Services Ralf Hülsermann, Monika Jäger and Andreas Gladisch Technologiezenrum, T-Sysems, Goslarer Ufer 35, D-1585

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

Connections, displays and operating elements. Status LEDs (next to the keys)

Connections, displays and operating elements. Status LEDs (next to the keys) GB Connecions, displays and operaing elemens A Push-buon plus Sysem M Operaing insrucions 1 2 1 2 3 4 5 6 7 8 C B A 4 Inser he bus erminal ino he connecion of pushbuon A. 5 Inser he push-buon ino he frame.

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

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

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

Image warping Li Zhang CS559

Image warping Li Zhang CS559 Wha is an image Image arping Li Zhang S559 We can hink of an image as a funcion, f: R 2 R: f(, ) gives he inensi a posiion (, ) defined over a recangle, ih a finie range: f: [a,b][c,d] [,] f Slides solen

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

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

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

Simultaneous Localization and Mapping with Stereo Vision

Simultaneous Localization and Mapping with Stereo Vision Simulaneous Localizaion and Mapping wih Sereo Vision Mahew N. Dailey Compuer Science and Informaion Managemen Asian Insiue of Technology Pahumhani, Thailand Email: mdailey@ai.ac.h Manukid Parnichkun Mecharonics

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

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

Image warping/morphing

Image warping/morphing Image arping/morphing Image arping Digial Visual Effecs Yung-Yu Chuang ih slides b Richard Szeliski, Seve Seiz, Tom Funkhouser and leei Efros Image formaion Sampling and quanizaion B Wha is an image We

More information

Hierarchical Information Fusion for Human Upper Limb Motion Capture

Hierarchical Information Fusion for Human Upper Limb Motion Capture 1h Inernaional Conference on Informaion Fusion Seale, WA, USA, July 6-9, 009 Hierarchical Informaion Fusion for Human Upper Limb Moion Capure Zhiqiang Zhang 1,, Zhipei Huang 1 and Jiankang Wu 1, 1 Graduae

More information

Assignment 2. Due Monday Feb. 12, 10:00pm.

Assignment 2. Due Monday Feb. 12, 10:00pm. Faculy of rs and Science Universiy of Torono CSC 358 - Inroducion o Compuer Neworks, Winer 218, LEC11 ssignmen 2 Due Monday Feb. 12, 1:pm. 1 Quesion 1 (2 Poins): Go-ack n RQ In his quesion, we review how

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

AML710 CAD LECTURE 11 SPACE CURVES. Space Curves Intrinsic properties Synthetic curves

AML710 CAD LECTURE 11 SPACE CURVES. Space Curves Intrinsic properties Synthetic curves AML7 CAD LECTURE Space Curves Inrinsic properies Synheic curves A curve which may pass hrough any region of hreedimensional space, as conrased o a plane curve which mus lie on a single plane. Space curves

More information

FLORIDA INTERNATIONAL UNIVERSITY. Miami, Florida DIMUSE: AN INTEGRATED FRAMEWORK FOR DISTRIBUTED MULTIMEDIA

FLORIDA INTERNATIONAL UNIVERSITY. Miami, Florida DIMUSE: AN INTEGRATED FRAMEWORK FOR DISTRIBUTED MULTIMEDIA FLORIDA INTERNATIONAL UNIVERSITY Miami, Florida DIMUSE: AN INTEGRATED FRAMEWORK FOR DISTRIBUTED MULTIMEDIA SYSTEM WITH DATABASE MANAGEMENT AND SECURITY SUPPORT A disseraion submied in parial fulfillmen

More information

A Routing Algorithm for Flip-Chip Design

A Routing Algorithm for Flip-Chip Design A Rouing Algorihm for Flip-hip Design Jia-Wei Fang, I-Jye Lin, and Yao-Wen hang, Graduae Insiue of Elecronics Engineering, Naional Taiwan Universiy, Taipei Deparmen of Elecrical Engineering, Naional Taiwan

More information

Querying Moving Objects in SECONDO

Querying Moving Objects in SECONDO Querying Moving Objecs in SECONDO Vicor Teixeira de Almeida, Ralf Harmu Güing, and Thomas Behr LG Daenbanksyseme für neue Anwendungen Fachbereich Informaik, Fernuniversiä Hagen D-58084 Hagen, Germany {vicor.almeida,

More information

arxiv: v2 [cs.cv] 20 May 2018

arxiv: v2 [cs.cv] 20 May 2018 Sereoscopic Neural Syle Transfer Dongdong Chen 1 Lu Yuan 2, Jing Liao 2, Nenghai Yu 1, Gang Hua 2 1 Universiy of Science and Technology of China 2 Microsof Research cd722522@mail.usc.edu.cn, {jliao, luyuan,

More information

Motion Estimation of a Moving Range Sensor by Image Sequences and Distorted Range Data

Motion Estimation of a Moving Range Sensor by Image Sequences and Distorted Range Data Moion Esimaion of a Moving Range Sensor by Image Sequences and Disored Range Daa Asuhiko Banno, Kazuhide Hasegawa and Kasushi Ikeuchi Insiue of Indusrial Science Universiy of Tokyo 4-6-1 Komaba, Meguro-ku,

More information

Detection and segmentation of moving objects in highly dynamic scenes

Detection and segmentation of moving objects in highly dynamic scenes Deecion and segmenaion of moving objecs in highly dynamic scenes Aurélie Bugeau Parick Pérez INRIA, Cenre Rennes - Breagne Alanique Universié de Rennes, Campus de Beaulieu, 35 042 Rennes Cedex, France

More information

Upper Body Tracking for Human-Machine Interaction with a Moving Camera

Upper Body Tracking for Human-Machine Interaction with a Moving Camera The 2009 IEEE/RSJ Inernaional Conference on Inelligen Robos and Sysems Ocober -5, 2009 S. Louis, USA Upper Body Tracking for Human-Machine Ineracion wih a Moving Camera Yi-Ru Chen, Cheng-Ming Huang, and

More information

Dynamic Route Planning and Obstacle Avoidance Model for Unmanned Aerial Vehicles

Dynamic Route Planning and Obstacle Avoidance Model for Unmanned Aerial Vehicles Volume 116 No. 24 2017, 315-329 ISSN: 1311-8080 (prined version); ISSN: 1314-3395 (on-line version) url: hp://www.ijpam.eu ijpam.eu Dynamic Roue Planning and Obsacle Avoidance Model for Unmanned Aerial

More information

A Framework for Applying Point Clouds Grabbed by Multi-Beam LIDAR in Perceiving the Driving Environment

A Framework for Applying Point Clouds Grabbed by Multi-Beam LIDAR in Perceiving the Driving Environment Sensors 215, 15, 21931-21956; doi:1.339/s15921931 Aricle OPEN ACCESS sensors ISSN 1424-822 www.mdpi.com/journal/sensors A Framewor for Applying Poin Clouds Grabbed by Muli-Beam LIDAR in Perceiving he Driving

More information