Calibration of an Articulated Camera System

Size: px
Start display at page:

Download "Calibration of an Articulated Camera System"

Transcription

1 Calbraton of an Artculated Camera System CHEN Junzhou and Kn Hong WONG Department of Computer Scence and Engneerng The Chnese Unversty of Hong Kong {jzchen, Abstract Multple Camera Systems (MCS) have been wdely used n many vson applcatons and attracted much attenton recently. There are two prncple types of MCS, one s the gd Multple Camera System (MCS); the other s the Artculated Camera System (ACS). In an MCS, the relatve poses (relatve -D poston and orentaton) between the cameras are nvarant. Whle, n an ACS, the cameras are artculated through movable jonts, the relatve pose between them may change. Therefore, through calbraton of an ACS we want to fnd not only the relatve poses between the cameras but also the postons of the jonts n the ACS. Although calbraton methods for MCS have been extensvely developed durng the past decades, the studes of ACS calbraton are stll rare. In ths paper, two ACS calbraton methods are proposed. The frst one uses the feature correspondences between the cameras n the ACS. The second one requres only the ego-moton nformaton of the cameras and can be used for the calbraton of the nonoverlappng vew ACS. In both methods, the ACS s assumed to have performed general transformatons n a statc envronment. The effcency and robustness of the proposed methods are tested by smulaton and real experments. In the real experment, the ntrnsc and extrnsc parameters of the ACS are calbrated usng the same mage sequences, no extra data capturng step s requred. The correspondng trajectory s recovered and llustrated usng the calbraton results of the ACS. To our knowledge, we are the frst to study the calbraton of ACS.. Introducton Calbraton of a Multple Camera System (MCS) s an essental step n many computer vson tasks such as SLAM (Smultaneous Localzaton and Map), survellance, stereo and metrology,, 7,. Both the ntrnsc and extrnsc parameters of the MCS are requred to be estmated before the MCS can be used. The ntrnsc parameters 9 descrbe the nternal camera geometrc and optcal character- Fgure. An obot wth Four Cameras Attached on It, Where the Cameras are Artculated. stcs of each camera n the MCS. In a gd Multple Camera System (MCS), the cameras are fxed to each other. The extrnsc parameters 5 of an MCS descrbe the relatve pose (the relatve -D poston and orentaton, totally, sx degrees of freedom) between the cameras n the MCS. Calbraton methods of the ntrnsc parameters of a camera are well establshed 5, 9. Calbraton methods for the extrnsc parameters of an MCS are also wdely studed. For nstance, Maas proposed an automatc MCS calbraton technque wth a movng reference bar whch can be seen by all cameras. Antone and Teller developed an algorthm whch recovers the relatve poses of cameras by overlappng portons of the outdoor scene. Baker and Alomonos presented MCS calbraton methods usng calbraton objects such as a wand wth LEDs or a rgd board wth known patterns, 4. Dornaka proposed a stereo rg selfcalbraton method by the monocular eppolar geometres and geometrc constrants of a movng MCS, n whch only the feature correspondences between the monocular mages of each camera are requred 8. In hand-eye calbraton, t s demonstrated that when a sensor s mounted on a movng robot hand, the relatonshp between the sensor coordnate system and hand coordnate system can be calculated by the moton nformaton of the hand and the sensor. One example of usng knematc nformaton of the cameras for MCS s dscussed by Casp and Iran

2 6, they ndcated that f the cameras of a non-overlappng vew MCS are close to each other and share a same projecton center, ther recorded mage sequences can be algned effectvely by the estmated transformatons nsde each mage sequence. However, n some types of MCS, the relatve poses between the cameras are not fxed, hence the calbraton methods for the MCS cannot be used drectly. In Fgure, a novel applcaton of lmb pose estmaton by attachng cameras on the arms of a robot s shown. On each arm of the robot, two cameras are artculated to each other through the elbow jont of the arm. When the robot moves, the relatve pose between the cameras may change, whle, the coordnate of the elbow jont refers to each camera attached on the correspondng arm s nvarant. In ths paper, such a type of MCS s named as Artculated Camera System (ACS). The jont of the elbow s named as the jont n the ACS. ACSs can be easly found n the real world, such as camera systems attached on human, robots and anmals. Before usng an ACS, t has to be calbrated. However, there are stll some unsolved problems: () In an ACS wth overlappng vew, tradtonal calbraton methods cannot estmate the postons of the jonts n the ACS. () In a nonoverlappng vew ACS, nether the postons of the jonts n the ACS nor the relatve poses between the cameras n the ACS can be estmated by tradtonal calbraton methods. These consderatons n mnd motvate us to develop the technologes n ths paper. The rest of ths paper are organzed as follows: Secton and analyss the constrants n a movng ACS. The correspondng calbraton methods are proposed. Secton 4 and 5 evaluate the proposed method by smulaton and real experment. In secton 6, a bref concluson and the future plan are presented.. Calbraton of ACS wth Overlappng Vews system of camera A, C B the coordnate system of camera B. Suppose there are enough feature correspondences between the cameras so that the pose of C A and C B referrng to the same coordnate system C W can be estmated. Therefore, the relatve pose between C A and C B s known. We want to fnd the poston of O n the ACS. Let H AW and H BW be the Eucldean transformaton matrxes descrbe the C A and C B refer to C W, so that for any pont P : P A = H AW P W = P B = H BW P W = AW T AW BW T BW P W () P W (), where s the rotaton matrx, T s vector, P W, P A and P B are the homogenous coordnates of the -D Pont P refer to C W, C A and C B respectvely. Accordng to equatons () and (): P W = H AW P A = H BW P B () H AW P A H BW P B = (4) T AW T AW T AW PA T BW T BW T BW PB = (5) T AW P A T BW P B = T AWT AW T BWT BW (6), where T s the transpose of. Suppose the ACS performed n transformatons, for the -th transformaton of the ACS, accordng to equaton (6): ( AW) T PA ( BW) T PB = ( AW) T TAW ( BW) T TBW (7) Let Õ = ŌA T ŌB T T, where Ō A and ŌB are the coordnates of the jont O refer to C A and C B respectvely. Equaton (7) can be rewrtten as: ( AW ) T ( BW )T Õ = ( AW) T T AW ( BW) T T BW (8) Fgure. An Artculated Camera System wth Overlappng Vews Suppose two rgd objects are artculated at jont O and two cameras (camera A and B) are fxed on the two rgd objects respectvely (See Fgure ). Let C A be the coordnate Snce camera A and B are fxed on the artculated rgd objects, Õ s nvarant durng the transformaton of the ACS. The transformatons ( AW, BW, T AW and T BW for... n) of the camera coordnate systems are calculated by the projected mage sequences. We propose that Õ can be estmated by a least squares method, when the ACS has moved to many dfferent postons and captured enough samples of AW, BW, T AW and T BW.

3 Let M A = ( A I)T, ( A I)T,..., ( n A I)T T, T A = (T A )T, (T A )T,..., (T n A )T T, we have: M A Ō A = T A () Snce the transformatons ( A and T A,...n) of camera A can be calculated by the projected mage sequence. We propose ŌA can be estmated by a least squares method. Smlarly, Ō B can also be estmated. Therefore, O A and O B are recovered. Fgure. A Non-overlappng Vew Artculated Camera System. Calbraton of Non-Overlappng Vew ACS In many stuatons, there s no overlappng vew between the cameras n an ACS. And the lack of common features makes the calbraton method proposed n secton become nvald (See Fgure ). Moreover, snce the relatve pose between the cameras n the ACS cannot be estmated by the overlappng vews, the calbraton of the relatve poses between the non-overlappng vew cameras s also requred. In ths secton, a calbraton method based on the ego-moton nformaton of the cameras n an ACS s dscussed... ecoverng the Poston of the Jont efers to the Cameras n the ACS Let CA nt and CB nt be the coordnate systems of camera A and B respectvely at the ntal state (tme t = ). Suppose the ACS performs n transformatons. Snce the coordnate of the jont O refers to camera A s fxed durng the transformaton of the ACS. At tme t =, we have: O A = H AO A = A T A O A (9), where H A s the Eucldean transformaton matrx of camera A at tme refers to CA nt. A and T A descrbe the orentaton and orgn of camera A at tme refer to CA nt. Also O A s the coordnate of pont O at ntal state refers to CA nt, and O A s the coordnate of pont O at tme refers to CA nt. If the poston of the jont O refers to CA nt s fxed durng the transformatons of the ACS, we have: OA = O A,,...,n. For -th transformaton of the ACS, accordng to equaton (9): O A = H A O A = A T A O A () ( A I)ŌA = T A ().. The Unqueness of the Jont Pose Estmaton If the dfferent segments of the artculated camera system (ACS) are connected by D rotatonal jonts (connected by pont rotatonal jonts) and the ACS can perform general transformatons, the soluton of the jont pose estmaton s unque: For the jont pose estmaton method usng specal moton (n secton.). Suppose the soluton of the jont pose estmaton s not unque, there must exst at lest two dfferent D ponts O and O satsfy equaton (). We have: M A O = T A and M A O = T A. Therefore, any pont P = so +( s)o wll also satsfy equaton (), where s s an arbtrary scalar. Accordng to the defnton of P, P s the pont on the lne passng through the ponts O and O. Snce P satsfy equaton () represents that the poston of the P refers to the camera n the ACS s nvarant durng the transformaton of the ACS, t means the dfferent segments of ACS are connected by the D rotatonal axs nstead of the D rotatonal jonts. The poston of the ponts on the D rotatonal axs refer to the camera n the ACS s nvarant durng the transformaton of the ACS. However, t conflcts wth the assumpton. Smlarly, the unqueness of the jont pose estmaton method usng overlappng vews (n secton ) can also be verfed... ecoverng the elatve Pose Between the Cameras of the Non-overlappng vew ACS Let H BA be the Eucldean transformaton matrx between CA nt and Cnt B, so that for any pont P : P B = H BA P A = BA T BA P A = H BA P A (), where P A and P B are the homogenous coordnate of Pont P refer to CA nt and Cnt B respectvely. The relatve pose ( BA and T BA ) between CA nt and CB nt s defned as: BA = T BA (4) T BA = T BA T BA (5)

4 Let OB be the coordnate of jont O at tme refer to CB nt. Snce the coordnate of the jont O refer to camera B s nvarant: OB = B TB O B = B TB BA T BA O A = B BA B T BA + TB O A (6) Accordng to equatons (9) and (): OB = H BAOA BA T = BA = A T A BA A BAT A + T BA Accordng to equatons (6) and (7): B BA B T BA + TB ŌA BA = A BATA + T BA ŌA B BA Ō A + B T BA + TB BA = AŌA + BA TA + T BA O A O A (7) (8) (9) B BA Ō A + BT BA BA AŌA BA T A + T B T BA = () Snce ŌA can be estmated by the method dscussed n secton., the BA and T BA can be estmated by a least square method, when the ACS perform enough general motons. In our smulaton and real experment, the estmated BA s refned by a method dscussed n 4. Then the roll, ptch and yaw correspondng to the BA are estmated accordng to the defnton of the rotaton matrx 9. Let BA = M(r, p, y), where r p and y are the correspondng roll, ptch and yaw of BA, M s a functon from roll, ptch and yaw to the correspondng rotaton matrx. Then, the r, p, y, T BA and ŌA are optmzed by mnmzng the nonlnear error functon: n E(r, p, y, T BA, O A ) = ( BM(r, p, y)ōa + BT BA = M(r, p, y) AŌA M(r, p, y)t A + T B T BA ) () usng a Levenberg-Marquardt method. Fnally, the BA s recovered from the optmzed r, p and y. The relatve pose between the CA nt and Cnt B s calculated by equatons (4) and (5). 4. Smulaton In ths secton, the proposed calbraton methods are evaluated wth synthetc transformaton data. 4.. Performance w.r.t. Nose n Transformaton Data Setup and Notatons: In each test, one ACS wth cameras and jont s generated randomly. In whch, O A meters, O B meters. The generated ACS performs random transformatons. Performance of the Calbraton Method for ACS wth Overlappng Vews: In the frst smulaton, the proposed algorthm s tested tmes. Zero mean Gaussan nose s added to the transformaton data of the cameras. The confguraton, nput and output of our smulaton system are lst as Table. Snce we assume there are overlappng vews between the two cameras, the relatve pose between them can be estmated by many exstng methods as dscussed n secton. Only the performance of jont pose estmaton s evaluated n our smulaton. The error of jont estmaton are computed by: Err = ŌA ˆŌ A ŌA + ŌB ˆŌ B ŌB (), where ŌA s the ground truth, ˆŌ A s the estmated poston of jont O refer to camera A. Smlarly, Ō B s the ground truth, ˆŌ B s the estmated poston of jont O refer to camera B. The correspondng results are shown n Fgure 4 and 5. Table. Confguraton, Input and Output Confguraton No. of Cameras n the ACS No. of Jonts n the ACS andom transformatons per test (n) Number of tests Input ( =...n) otatons of cameras ( AW, BW ) Translatons of cameras (TAW, T BW ) Zero Mean Gaussan nose: σ rot.4 and σ trans.meters Output Mean error of jont pose estmaton STD error of jont pose estmaton Performance of the Calbraton Method for Non- Overlappng Vews ACS: In the second smulaton, frstly, the pose of the jont s fxed refers to CA nt durng the transformatons of the ACS. The pose of the jont refers to the camera A (O A ) s calbrated by the transformatons of camera A. Smlarly, O B s calbrated. Then, the ACS performs several general transformatons (the jont s not needed to

5 Mean Error of Jont Pose Estmaton otaton Nose (degree) Mean Error of Jont Pose Estmaton Translaton Nose (meter) Fgure 4. Mean Error of Jont Poston Estmaton STD Error of Jont Pose Estmaton otaton Nose (degree) STD Error of Jont Pose Estmaton Translaton Nose (meter).. Table. Confguraton, Input and Output Confguraton No. of Cameras n the ACS No. of Jonts n the ACS andom transformatons per test (n) Number of tests Input ( =...n) Transformatons wth fxed jont pose: otatons of cameras ( A, B ) Translatons of cameras (TA, T B ) General transformatons: otatons of cameras ( A, B ) Translatons of cameras (TA, T B ) Zero Mean Gaussan nose: σ rot.4 and σ trans.meters Output Mean error of jont pose estmaton STD error of jont pose estmaton Mean error of relatve translaton estmaton STD error of relatve translaton estmaton Mean error of relatve rotaton estmaton STD error of relatve rotaton estmaton Mean Error of Jont Pose Estmaton Fgure 5. STD Error of Jont Poston Estmaton be fxed refer to CA nt ), the relatve pose between the cameras are calbrated usng the estmated jont pose and the transformatons of the cameras. The confguraton, nput and output of the smulaton system are lsted as Table. The error of jont pose, relatve rotaton, relatve translaton estmaton are calculated by equaton (), () and (4) respectvely. Fgure 6 and 7 show the results of jont pose estmaton. Compare wth the calbraton method usng the overlappng vews, the calbraton method usng specal motons s more accurate. The mean and STD error of the relatve rotaton and translaton estmaton are presented n Fgure 8, 9, and. The proposed algorthms are shown to be stable, when the zero mean Gaussan nose from to.4 s added to the roll, ptch and yaw of the rotaton data, and the zero mean Gaussan nose from to. meters s added to the translaton data. Err = roll roll + ptch ptch + yaw ŷaw () Mean Error of Jont Pose Estmaton otaton Nose (degree) Translaton Nose (meter) Fgure 6. Mean Error of Jont Poston Estmaton STD Error of Jont Pose Estmaton x otaton Nose (degree) STD Error of Jont Pose Estmaton Translaton Nose (meter).. Err = T AB ˆT AB T AB (4) Fgure 7. STD Error of Jont Poston Estmaton

6 Mean Error of elatve otaton Estmaton STD Error of elatve Translaton Estmaton Mean Error of elatve otaton Estmaton (degree).5.5 otaton Nose (degree) Translaton Nose (meter) STD Error of elatve Translaton Estmaton.5..5 otaton Nose (degree) Translaton Nose (meter) Fgure 8. Mean Error of elatve otaton Estmaton Fgure. STD Error of elatve Translaton Estmaton STD Error of elatve otaton Estmaton (degree) otaton Nose (degree) STD Error of elatve otaton Estmaton Translaton Nose (meter) Fgure 9. STD Error of elatve otaton Estmaton Mean Error of elatve Translaton Estmaton otaton Nose (degree) Mean Error of elatve Translaton Estmaton Translaton Nose (meter) Fgure. Mean Error of elatve Translaton Estmaton 5. eal Experment In the real experments, an ACS wth two cameras (Cannon PowerShot G9) s set up as Fgure (a). The ntrnsc parameters of each camera n the ACS are calbrated by Bouguet s mplementaton ( Camera Calbraton Toolbox for Matlab ) of 5. Snce the Bouguet s Toolbox can also estmate the pose nformaton of the camera, the transformatons of each camera are calculated usng the same mage sequence for the ntrnsc calbraton smultaneously. No addtonal mages nor manual nput s requred n the real experments Calbraton of the Pose of the Jont n Each Camera By Overlappng Vews (Algorthm I): In the frst real experment, the two cameras n the ACS observe the same checker plane and record mages smultaneously. The two cameras are free to move durng the transformaton of the ACS. Two mage sequences (Q and Q ) are recorded, each sequence conssts of 5 mages of sze 6. The estmated jont pose are lst n Table as algorthm I. By Fxed-Jont Motons (Algorthm II): In the second real experment, the jont of the ACS s fxed refers to the world coordnate system durng the transformaton of the ACS. The two cameras do not need to vew the same checker plane. And each camera records the mage sequence ndependently. Two mage sequences (Q and Q 4 ) are recorded, each sequence conssts of mages of sze 6. The camera pose of the frst mage s selected as the ntal pose to generate the transformaton sequence of each camera. The estmated jont pose are lst n Table as algorthm II. The poses of the jont refer to the two cameras n the ACS are also estmated manually for comparson purpose. Snce the camera pose of any mage n each mage sequence can be chosen as the ntal camera pose (see secton.), the proposed algorthm s also tested by choosng dfferent mages as the reference. The mean and standard dervaton of the correspondng calbraton results are presented n Table Calbraton of elatve Pose Between the Cameras n the Non-Overlappng Vew ACS (Algorthm III) In the thrd real experment, frstly, we use the nonoverlappng vew ACS calbraton method to process the mage sequences Q and Q. The jont pose (Ō A ) estmated by algorthm II s used as the nput for the relatve pose calbraton. Snce there are overlappng vews between Q and Q, we also calbrate the relatve pose between the two cameras by the feature correspondences for compar-

7 Table. esults Of Jont Pose Calbraton I: the algorthm usng overlappng vews. II: the algorthm usng fxed-jont motons. M: manual measurement(ground truth). O A s the coordnate of the jont refers to camera A, the same apples to O B. Algorthm Jont Pose (mm) X Y Z I O A O B II O A O B M O A ± 5± -4± O B -7± 5± -± Table 4. Mean and STD of the Jont Pose Calbraton Algorthm II Usng Dfferent eference Images. (O A s the coordnate of the jont refers to camera A, the same apples to O B.) Algorthm Jont Pose (mm) II X Y Z Mean O A O B STD O A O B son. The calbraton result are lsted n Table 5. After the jont pose refers to each camera n the ACS and relatve pose between the cameras n the ACS are calbrated, the trajectory of the ACS s recovered (see Fgure ). The proposed Table 5. esult of elatve Pose Calbraton III: our method. F: usng feature correspondences. Algorthm elatve otaton (Degree) oll Ptch Yaw III F Algorthm elatve Translaton (mm) T x T y T z III F calbraton method s also tested by non-overlappng vew mage sequences. Fgure (b), (c), (d) shows the confguraton of the non-overlappng vew ACS calbraton system n the real experment. Two mage sequences (Q 5 and Q 6 ) are recorded, each sequence conssts of 7 mages of sze 6. There s no overlappng vew between Q 5 and Q 6. Fgure 4 shows some samples of the recorded mages. We also manually measured the relatve pose between the two cameras for comparson. Snce no feature correspondence can be used, we only get a rough estmaton by a ruler. The calbraton results are shown n Table 6. After the relatve pose between the cameras at the ntal state s estmated, the trajectory of the non-overlappng vew ACS s recovered (see Fgure 5). Fgure. The Trajectory of the ACS ecovered from Q and Q (a) (c) (d) Fgure. The ACS wth Two Cannon PowerShot G9 Used n the eal Experment. (a) The ACS Used n the eal Experment. (b) The ACS and two Checker Planes. (c) In the Front of the ACS. (d) On the Top of the ACS. (b) Img Img 6 Img Img 7 (a) Images ecorded by Camera A Img Img 6 Img Img 7 (b) Images ecorded by Camera B Fgure 4. Images ecorded by the ACS 6. Concluson In ths paper, an ACS calbraton method s developed. Both the smulaton and real experment show that the pose of the jont n an ACS can be estmated robustly. When there s no overlappng vew between the cameras n an ACS, the jont pose and the relatve pose between the cameras can also be calculated. The trajectory of an ACS can be recov-

8 Table 6. esult of elatve Pose Calbraton Usng Non- Overlappng Vew Image Sequences. (III: our method. M: manual measurement.) Algorthm elatve otaton (Degree) oll Ptch Yaw III M ± 5 9 ± 5 ± 5 Algorthm elatve Translaton (mm) T x T y T z III M 9± ± 8 ± Fgure 5. The Trajectory of the ACS ecovered from Q 5 and Q 6 ered after the ACS s calbrated. The proposed calbraton method requres only the mage sequences recorded by the cameras n the ACS. In the real experment, the ntrnsc and extrnsc parameters of the ACS are calbrated usng the same mage sequences smultaneously. Our future plan may focus on usng an ACS attached on dfferent parts of human body to track the moton of the human. We foresee that f calbraton of artculated cameras become a smple routne, researchers wll fnd many novel and nterestng applcatons for such a camera system. Acknowledgement. We apprecate the revews for ther suggestons and menton of the relatve works. We would lke to thank Prof. Jaya JIA for dscusson, Ms. SHAO Lu n HKBU for checkng the Englsh, Mr. Gang LI, Hongnng DAI and other frends n CUHK for ther help. The research s supported by by a drect grant (code 55) from the Faculty of Engneerng, The Chnese Unversty of Hong Kong, Shatn, Hong Kong. eferences M. Antone and S. Teller. Scalable extrnsc calbraton of omn-drectonal mage networks. Internatonal Journal of Computer Vson, 49():4 74,. P. Baker and Y. Alomonos. Complete calbraton of a mult-camera network. Proc. IEEE Workshop on Omndrectonal Vson, :4 4,. P. Baker, A. Ogale, and C. Fermuller. The Argus eye: a new magng system desgned to facltate robotc tasks of moton. obotcs & Automaton Magazne, IEEE, (4): 8, 4. 4 P. T. Baker and Y. Alomonos. Calbraton of a multcamera network. Conference on Computer Vson and Pattern ecognton Workshop, 7:7,. 5 B. Caprle and V. Torre. Usng vanshng ponts for camera calbraton. Internatonal Journal of Computer Vson, 4():7 9, Y. Casp and M. Iran. Algnng Non-Overlappng Sequences. Internatonal Journal of Computer Vson, 48():9 5,. 7 S. Dockstader and A. Tekalp. Multple camera trackng of nteractng and occluded human moton. Proceedngs of the IEEE, 89():44 455,. 8 F. Dornaka. Self-calbraton of a stereo rg usng monocular eppolar geometres. Pattern ecognton, 4():76 79, I. Hartley and A. Zsserman. Multple vew geometry n computer vson. Cambrdge Unversty Press, ISBN: 55458, second edton, 4., 4. Horaud and F. Dornaka. Hand-eye calbraton. Internatonal Journal of obotcs esearch, 4():95, 995. M. Kaess and F. Dellaert. Vsual SLAM wth a Mult- Camera g. Techncal report, Georga Insttute of Technology, 6. T. Kanade, P. ander, and P. Narayanan. Vrtualzed realty: constructng vrtual worlds from real scenes. Multmeda, IEEE, 4():4 47, 997. H. G. Maas. Image sequence based automatc multcamera system calbraton technques. In Internatonal Archves of Photogrammetry and emote Sensng, (B5):76 768, Z. Zhang. A flexble new technque for camera calbraton. Techncal report, Techncal eport MS-T- 98-7, Mcrosoft esearch, Z. Zhang. A flexble new technque for camera calbraton. IEEE Transactons on Pattern Analyss and Machne Intellgence, (): 4,., 6

Calibration of an Articulated Camera System

Calibration of an Articulated Camera System Calbraton of an Artculated Camera System CHEN Junzhou and Kn Hong WONG Department of Computer Scence and Engneerng The Chnese Unversty of Hong Kong {jzchen, khwong}@cse.cuhk.edu.hk Abstract Multple Camera

More information

Calibration of an Articulated Camera System with Scale Factor Estimation

Calibration of an Articulated Camera System with Scale Factor Estimation Calbraton of an Artculated Camera System wth Scale Factor Estmaton CHEN Junzhou, Kn Hong WONG arxv:.47v [cs.cv] 7 Oct Abstract Multple Camera Systems (MCS) have been wdely used n many vson applcatons and

More information

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,

More information

What are the camera parameters? Where are the light sources? What is the mapping from radiance to pixel color? Want to solve for 3D geometry

What are the camera parameters? Where are the light sources? What is the mapping from radiance to pixel color? Want to solve for 3D geometry Today: Calbraton What are the camera parameters? Where are the lght sources? What s the mappng from radance to pel color? Why Calbrate? Want to solve for D geometry Alternatve approach Solve for D shape

More information

Line-based Camera Movement Estimation by Using Parallel Lines in Omnidirectional Video

Line-based Camera Movement Estimation by Using Parallel Lines in Omnidirectional Video 01 IEEE Internatonal Conference on Robotcs and Automaton RverCentre, Sant Paul, Mnnesota, USA May 14-18, 01 Lne-based Camera Movement Estmaton by Usng Parallel Lnes n Omndrectonal Vdeo Ryosuke kawansh,

More information

METRIC ALIGNMENT OF LASER RANGE SCANS AND CALIBRATED IMAGES USING LINEAR STRUCTURES

METRIC ALIGNMENT OF LASER RANGE SCANS AND CALIBRATED IMAGES USING LINEAR STRUCTURES METRIC ALIGNMENT OF LASER RANGE SCANS AND CALIBRATED IMAGES USING LINEAR STRUCTURES Lorenzo Sorg CIRA the Italan Aerospace Research Centre Computer Vson and Vrtual Realty Lab. Outlne Work goal Work motvaton

More information

Structure from Motion

Structure from Motion Structure from Moton Structure from Moton For now, statc scene and movng camera Equvalentl, rgdl movng scene and statc camera Lmtng case of stereo wth man cameras Lmtng case of multvew camera calbraton

More information

Angle-Independent 3D Reconstruction. Ji Zhang Mireille Boutin Daniel Aliaga

Angle-Independent 3D Reconstruction. Ji Zhang Mireille Boutin Daniel Aliaga Angle-Independent 3D Reconstructon J Zhang Mrelle Boutn Danel Alaga Goal: Structure from Moton To reconstruct the 3D geometry of a scene from a set of pctures (e.g. a move of the scene pont reconstructon

More information

A Comparison and Evaluation of Three Different Pose Estimation Algorithms In Detecting Low Texture Manufactured Objects

A Comparison and Evaluation of Three Different Pose Estimation Algorithms In Detecting Low Texture Manufactured Objects Clemson Unversty TgerPrnts All Theses Theses 12-2011 A Comparson and Evaluaton of Three Dfferent Pose Estmaton Algorthms In Detectng Low Texture Manufactured Objects Robert Krener Clemson Unversty, rkrene@clemson.edu

More information

New dynamic zoom calibration technique for a stereo-vision based multi-view 3D modeling system

New dynamic zoom calibration technique for a stereo-vision based multi-view 3D modeling system New dynamc oom calbraton technque for a stereo-vson based mult-vew 3D modelng system Tao Xan, Soon-Yong Park, Mural Subbarao Dept. of Electrcal & Computer Engneerng * State Unv. of New York at Stony Brook,

More information

A 3D Reconstruction System of Indoor Scenes with Rotating Platform

A 3D Reconstruction System of Indoor Scenes with Rotating Platform A 3D Reconstructon System of Indoor Scenes wth Rotatng Platform Feng Zhang, Lmn Sh, Zhenhu Xu, Zhany Hu Insttute of Automaton, Chnese Academy of Scences {fzhang, lmsh, zhxu, huzy}@nlpr.a.ac.cnl Abstract

More information

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching A Fast Vsual Trackng Algorthm Based on Crcle Pxels Matchng Zhqang Hou hou_zhq@sohu.com Chongzhao Han czhan@mal.xjtu.edu.cn Ln Zheng Abstract: A fast vsual trackng algorthm based on crcle pxels matchng

More information

Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input

Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input Real-tme Jont Tracng of a Hand Manpulatng an Object from RGB-D Input Srnath Srdhar 1 Franzsa Mueller 1 Mchael Zollhöfer 1 Dan Casas 1 Antt Oulasvrta 2 Chrstan Theobalt 1 1 Max Planc Insttute for Informatcs

More information

Resolving Ambiguity in Depth Extraction for Motion Capture using Genetic Algorithm

Resolving Ambiguity in Depth Extraction for Motion Capture using Genetic Algorithm Resolvng Ambguty n Depth Extracton for Moton Capture usng Genetc Algorthm Yn Yee Wa, Ch Kn Chow, Tong Lee Computer Vson and Image Processng Laboratory Dept. of Electronc Engneerng The Chnese Unversty of

More information

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,

More information

Finding Intrinsic and Extrinsic Viewing Parameters from a Single Realist Painting

Finding Intrinsic and Extrinsic Viewing Parameters from a Single Realist Painting Fndng Intrnsc and Extrnsc Vewng Parameters from a Sngle Realst Pantng Tadeusz Jordan 1, Davd G. Stork,3, Wa L. Khoo 1, and Zhgang Zhu 1 1 CUNY Cty College, Department of Computer Scence, Convent Avenue

More information

Range images. Range image registration. Examples of sampling patterns. Range images and range surfaces

Range images. Range image registration. Examples of sampling patterns. Range images and range surfaces Range mages For many structured lght scanners, the range data forms a hghly regular pattern known as a range mage. he samplng pattern s determned by the specfc scanner. Range mage regstraton 1 Examples

More information

An Optimal Algorithm for Prufer Codes *

An Optimal Algorithm for Prufer Codes * J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,

More information

EVALUATION OF RELATIVE POSE ESTIMATION METHODS FOR MULTI-CAMERA SETUPS

EVALUATION OF RELATIVE POSE ESTIMATION METHODS FOR MULTI-CAMERA SETUPS EVALUAION OF RELAIVE POSE ESIMAION MEHODS FOR MULI-CAMERA SEUPS Volker Rodehorst *, Matthas Henrchs and Olaf Hellwch Computer Vson & Remote Sensng, Berln Unversty of echnology, Franklnstr. 8/9, FR 3-,

More information

Alignment of Non-Overlapping Sequences

Alignment of Non-Overlapping Sequences Algnment of Non-Overlappng Sequences Yaron Casp Mchal ran Dept. of Computer Scence and Appled Math The Wezmann nsttute of Scence 76100 Rehovot, srael Ths paper shows how two mage sequences that have no

More information

Model reconstruction and pose acquisition using extended Lowe s method

Model reconstruction and pose acquisition using extended Lowe s method Model reconstructon and pose acquston usng extended Lowe s method, by M.M.Y. Chang and K.H. Wong 1 Model reconstructon and pose acquston usng extended Lowe s method Mchael Mng-Yuen Chang and Kn-Hong Wong

More information

Computer Animation and Visualisation. Lecture 4. Rigging / Skinning

Computer Animation and Visualisation. Lecture 4. Rigging / Skinning Computer Anmaton and Vsualsaton Lecture 4. Rggng / Sknnng Taku Komura Overvew Sknnng / Rggng Background knowledge Lnear Blendng How to decde weghts? Example-based Method Anatomcal models Sknnng Assume

More information

Real-time Motion Capture System Using One Video Camera Based on Color and Edge Distribution

Real-time Motion Capture System Using One Video Camera Based on Color and Edge Distribution Real-tme Moton Capture System Usng One Vdeo Camera Based on Color and Edge Dstrbuton YOSHIAKI AKAZAWA, YOSHIHIRO OKADA, AND KOICHI NIIJIMA Graduate School of Informaton Scence and Electrcal Engneerng,

More information

Model-Based Bundle Adjustment to Face Modeling

Model-Based Bundle Adjustment to Face Modeling Model-Based Bundle Adjustment to Face Modelng Oscar K. Au Ivor W. sang Shrley Y. Wong oscarau@cs.ust.hk vor@cs.ust.hk shrleyw@cs.ust.hk he Hong Kong Unversty of Scence and echnology Realstc facal synthess

More information

Articulated Tree Structure from Motion A Matrix Factorisation Approach

Articulated Tree Structure from Motion A Matrix Factorisation Approach Artculated Tree Structure from Moton A Matrx Factorsaton Approach arl Scheffler, Konrad H Scheffler, hrstan Omln Department of omputer Scence Unversty of the estern ape 7535 ellvlle, South Afrca cscheffler,

More information

The motion simulation of three-dof parallel manipulator based on VBAI and MATLAB Zhuo Zhen, Chaoying Liu* and Xueling Song

The motion simulation of three-dof parallel manipulator based on VBAI and MATLAB Zhuo Zhen, Chaoying Liu* and Xueling Song Internatonal Conference on Automaton, Mechancal Control and Computatonal Engneerng (AMCCE 25) he moton smulaton of three-dof parallel manpulator based on VBAI and MALAB Zhuo Zhen, Chaoyng Lu* and Xuelng

More information

Simultaneous Object Pose and Velocity Computation Using a Single View from a Rolling Shutter Camera

Simultaneous Object Pose and Velocity Computation Using a Single View from a Rolling Shutter Camera Smultaneous Object Pose and Velocty Computaton Usng a Sngle Vew from a Rollng Shutter Camera Omar At-Ader, Ncolas Andreff, Jean Marc Lavest, and Phlppe Martnet Unversté Blase Pascal Clermont Ferrand, LASMEA

More information

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes SPH3UW Unt 7.3 Sphercal Concave Mrrors Page 1 of 1 Notes Physcs Tool box Concave Mrror If the reflectng surface takes place on the nner surface of the sphercal shape so that the centre of the mrror bulges

More information

3D Virtual Eyeglass Frames Modeling from Multiple Camera Image Data Based on the GFFD Deformation Method

3D Virtual Eyeglass Frames Modeling from Multiple Camera Image Data Based on the GFFD Deformation Method NICOGRAPH Internatonal 2012, pp. 114-119 3D Vrtual Eyeglass Frames Modelng from Multple Camera Image Data Based on the GFFD Deformaton Method Norak Tamura, Somsangouane Sngthemphone and Katsuhro Ktama

More information

3D Rigid Facial Motion Estimation from Disparity Maps

3D Rigid Facial Motion Estimation from Disparity Maps 3D Rgd Facal Moton Estmaton from Dsparty Maps N. Pérez de la Blanca 1, J.M. Fuertes 2, and M. Lucena 2 1 Department of Computer Scence and Artfcal Intellgence ETSII. Unversty of Granada, 1871 Granada,

More information

Calibrating a single camera. Odilon Redon, Cyclops, 1914

Calibrating a single camera. Odilon Redon, Cyclops, 1914 Calbratng a sngle camera Odlon Redon, Cclops, 94 Our goal: Recover o 3D structure Recover o structure rom one mage s nherentl ambguous??? Sngle-vew ambgut Sngle-vew ambgut Rashad Alakbarov shadow sculptures

More information

Parallelism for Nested Loops with Non-uniform and Flow Dependences

Parallelism for Nested Loops with Non-uniform and Flow Dependences Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr

More information

A high precision collaborative vision measurement of gear chamfering profile

A high precision collaborative vision measurement of gear chamfering profile Internatonal Conference on Advances n Mechancal Engneerng and Industral Informatcs (AMEII 05) A hgh precson collaboratve vson measurement of gear chamferng profle Conglng Zhou, a, Zengpu Xu, b, Chunmng

More information

A Binarization Algorithm specialized on Document Images and Photos

A Binarization Algorithm specialized on Document Images and Photos A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a

More information

PROJECTIVE RECONSTRUCTION OF BUILDING SHAPE FROM SILHOUETTE IMAGES ACQUIRED FROM UNCALIBRATED CAMERAS

PROJECTIVE RECONSTRUCTION OF BUILDING SHAPE FROM SILHOUETTE IMAGES ACQUIRED FROM UNCALIBRATED CAMERAS PROJECTIVE RECONSTRUCTION OF BUILDING SHAPE FROM SILHOUETTE IMAGES ACQUIRED FROM UNCALIBRATED CAMERAS Po-Lun La and Alper Ylmaz Photogrammetrc Computer Vson Lab Oho State Unversty, Columbus, Oho, USA -la.138@osu.edu,

More information

Quick error verification of portable coordinate measuring arm

Quick error verification of portable coordinate measuring arm Quck error verfcaton of portable coordnate measurng arm J.F. Ouang, W.L. Lu, X.H. Qu State Ke Laborator of Precson Measurng Technolog and Instruments, Tanjn Unverst, Tanjn 7, Chna Tel.: + 86 [] 7-8-99

More information

Pose, Posture, Formation and Contortion in Kinematic Systems

Pose, Posture, Formation and Contortion in Kinematic Systems Pose, Posture, Formaton and Contorton n Knematc Systems J. Rooney and T. K. Tanev Department of Desgn and Innovaton, Faculty of Technology, The Open Unversty, Unted Kngdom Abstract. The concepts of pose,

More information

Analysis of Continuous Beams in General

Analysis of Continuous Beams in General Analyss of Contnuous Beams n General Contnuous beams consdered here are prsmatc, rgdly connected to each beam segment and supported at varous ponts along the beam. onts are selected at ponts of support,

More information

Analysis on the Workspace of Six-degrees-of-freedom Industrial Robot Based on AutoCAD

Analysis on the Workspace of Six-degrees-of-freedom Industrial Robot Based on AutoCAD Analyss on the Workspace of Sx-degrees-of-freedom Industral Robot Based on AutoCAD Jn-quan L 1, Ru Zhang 1,a, Fang Cu 1, Q Guan 1 and Yang Zhang 1 1 School of Automaton, Bejng Unversty of Posts and Telecommuncatons,

More information

Planar Catadioptric Stereo: Multiple-View Geometry and Image-Based Camera Localization

Planar Catadioptric Stereo: Multiple-View Geometry and Image-Based Camera Localization Manuscrpt Marottn et al., Spec. Issue Vs. Serv. Clck here to vew lnked References Planar Catadoptrc Stereo: Multple-Vew Geometry and Image-Based Camera Localzaton Gan Luca Marottn a,stefano Schegg b,fabo

More information

DISTRIBUTED POSE AVERAGING IN CAMERA SENSOR NETWORKS USING CONSENSUS ON MANIFOLDS

DISTRIBUTED POSE AVERAGING IN CAMERA SENSOR NETWORKS USING CONSENSUS ON MANIFOLDS DISTRIBUTED POSE AVERAGING IN CAMERA SENSOR NETWORKS USING CONSENSUS ON MANIFOLDS Roberto Tron, René Vdal Johns Hopns Unversty Center for Imagng Scence 32B Clar Hall, 34 N. Charles St., Baltmore MD 21218,

More information

Detection of an Object by using Principal Component Analysis

Detection of an Object by using Principal Component Analysis Detecton of an Object by usng Prncpal Component Analyss 1. G. Nagaven, 2. Dr. T. Sreenvasulu Reddy 1. M.Tech, Department of EEE, SVUCE, Trupath, Inda. 2. Assoc. Professor, Department of ECE, SVUCE, Trupath,

More information

Positive Semi-definite Programming Localization in Wireless Sensor Networks

Positive Semi-definite Programming Localization in Wireless Sensor Networks Postve Sem-defnte Programmng Localzaton n Wreless Sensor etworks Shengdong Xe 1,, Jn Wang, Aqun Hu 1, Yunl Gu, Jang Xu, 1 School of Informaton Scence and Engneerng, Southeast Unversty, 10096, anjng Computer

More information

CS 534: Computer Vision Model Fitting

CS 534: Computer Vision Model Fitting CS 534: Computer Vson Model Fttng Sprng 004 Ahmed Elgammal Dept of Computer Scence CS 534 Model Fttng - 1 Outlnes Model fttng s mportant Least-squares fttng Maxmum lkelhood estmaton MAP estmaton Robust

More information

ScienceDirect. The Influence of Subpixel Corner Detection to Determine the Camera Displacement

ScienceDirect. The Influence of Subpixel Corner Detection to Determine the Camera Displacement Avalable onlne at www.scencedrect.com ScenceDrect Proceda Engneerng ( ) 8 8 th DAAAM Internatonal Symposum on Intellgent Manufacturng and Automaton, DAAAM The Influence of Subpxel Corner Detecton to Determne

More information

A Robust Method for Estimating the Fundamental Matrix

A Robust Method for Estimating the Fundamental Matrix Proc. VIIth Dgtal Image Computng: Technques and Applcatons, Sun C., Talbot H., Ourseln S. and Adraansen T. (Eds.), 0- Dec. 003, Sydney A Robust Method for Estmatng the Fundamental Matrx C.L. Feng and Y.S.

More information

Self-Calibration from Image Triplets. 1 Robotics Research Group, Department of Engineering Science, Oxford University, England

Self-Calibration from Image Triplets. 1 Robotics Research Group, Department of Engineering Science, Oxford University, England Self-Calbraton from Image Trplets Martn Armstrong 1, Andrew Zsserman 1 and Rchard Hartley 2 1 Robotcs Research Group, Department of Engneerng Scence, Oxford Unversty, England 2 The General Electrc Corporate

More information

Secure and Fast Fingerprint Authentication on Smart Card

Secure and Fast Fingerprint Authentication on Smart Card SETIT 2005 3 rd Internatonal Conference: Scences of Electronc, Technologes of Informaton and Telecommuncatons March 27-31, 2005 TUNISIA Secure and Fast Fngerprnt Authentcaton on Smart Card Y. S. Moon*,

More information

The Research of Ellipse Parameter Fitting Algorithm of Ultrasonic Imaging Logging in the Casing Hole

The Research of Ellipse Parameter Fitting Algorithm of Ultrasonic Imaging Logging in the Casing Hole Appled Mathematcs, 04, 5, 37-3 Publshed Onlne May 04 n ScRes. http://www.scrp.org/journal/am http://dx.do.org/0.436/am.04.584 The Research of Ellpse Parameter Fttng Algorthm of Ultrasonc Imagng Loggng

More information

Large Motion Estimation for Omnidirectional Vision

Large Motion Estimation for Omnidirectional Vision Large Moton Estmaton for Omndrectonal Vson Jong Weon Lee, Suya You, and Ulrch Neumann Computer Scence Department Integrated Meda Systems Center Unversty of Southern Calforna Los Angeles, CA 98978, USA

More information

ROBOT KINEMATICS. ME Robotics ME Robotics

ROBOT KINEMATICS. ME Robotics ME Robotics ROBOT KINEMATICS Purpose: The purpose of ths chapter s to ntroduce you to robot knematcs, and the concepts related to both open and closed knematcs chans. Forward knematcs s dstngushed from nverse knematcs.

More information

UAV global pose estimation by matching forward-looking aerial images with satellite images

UAV global pose estimation by matching forward-looking aerial images with satellite images The 2009 IEEE/RSJ Internatonal Conference on Intellgent Robots and Systems October -5, 2009 St. Lous, USA UAV global pose estmaton by matchng forward-lookng aeral mages wth satellte mages Kl-Ho Son, Youngbae

More information

INVERSE DYNAMICS ANALYSIS AND SIMULATION OF A CLASS OF UNDER- CONSTRAINED CABLE-DRIVEN PARALLEL SYSTEM

INVERSE DYNAMICS ANALYSIS AND SIMULATION OF A CLASS OF UNDER- CONSTRAINED CABLE-DRIVEN PARALLEL SYSTEM U.P.B. Sc. Bull., Seres D, Vol. 78, Iss., 6 ISSN 454-58 INVERSE DYNAMICS ANALYSIS AND SIMULATION OF A CLASS OF UNDER- CONSTRAINED CABLE-DRIVEN PARALLEL SYSTEM We LI, Zhgang ZHAO, Guangtan SHI, Jnsong LI

More information

Amnon Shashua Shai Avidan Michael Werman. The Hebrew University, objects.

Amnon Shashua Shai Avidan Michael Werman. The Hebrew University,   objects. Trajectory Trangulaton over Conc Sectons Amnon Shashua Sha Avdan Mchael Werman Insttute of Computer Scence, The Hebrew Unversty, Jerusalem 91904, Israel e-mal: fshashua,avdan,wermang@cs.huj.ac.l Abstract

More information

APPLICATION OF AN AUGMENTED REALITY SYSTEM FOR DISASTER RELIEF

APPLICATION OF AN AUGMENTED REALITY SYSTEM FOR DISASTER RELIEF APPLICATION OF AN AUGMENTED REALITY SYSTEM FOR DISASTER RELIEF Johannes Leebmann Insttute of Photogrammetry and Remote Sensng, Unversty of Karlsruhe (TH, Englerstrasse 7, 7618 Karlsruhe, Germany - leebmann@pf.un-karlsruhe.de

More information

Computer Vision. Exercise Session 1. Institute of Visual Computing

Computer Vision. Exercise Session 1. Institute of Visual Computing Computer Vson Exercse Sesson 1 Organzaton Teachng assstant Basten Jacquet CAB G81.2 basten.jacquet@nf.ethz.ch Federco Camposeco CNB D12.2 fede@nf.ethz.ch Lecture webpage http://www.cvg.ethz.ch/teachng/compvs/ndex.php

More information

Image Alignment CSC 767

Image Alignment CSC 767 Image Algnment CSC 767 Image algnment Image from http://graphcs.cs.cmu.edu/courses/15-463/2010_fall/ Image algnment: Applcatons Panorama sttchng Image algnment: Applcatons Recognton of object nstances

More information

Kinematics of pantograph masts

Kinematics of pantograph masts Abstract Spacecraft Mechansms Group, ISRO Satellte Centre, Arport Road, Bangalore 560 07, Emal:bpn@sac.ernet.n Flght Dynamcs Dvson, ISRO Satellte Centre, Arport Road, Bangalore 560 07 Emal:pandyan@sac.ernet.n

More information

Improving Initial Estimations for Structure from Motion Methods

Improving Initial Estimations for Structure from Motion Methods Improvng Intal Estmatons for Structure from Moton Methods Chrstopher Schwartz Renhard Klen Insttute for Computer Scence II, Unversty of Bonn Abstract In Computer Graphcs as well as n Computer Vson and

More information

Corner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity

Corner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity Journal of Sgnal and Informaton Processng, 013, 4, 114-119 do:10.436/jsp.013.43b00 Publshed Onlne August 013 (http://www.scrp.org/journal/jsp) Corner-Based Image Algnment usng Pyramd Structure wth Gradent

More information

A New Feature of Uniformity of Image Texture Directions Coinciding with the Human Eyes Perception 1

A New Feature of Uniformity of Image Texture Directions Coinciding with the Human Eyes Perception 1 A New Feature of Unformty of Image Texture Drectons Concdng wth the Human Eyes Percepton Xng-Jan He, De-Shuang Huang, Yue Zhang, Tat-Mng Lo 2, and Mchael R. Lyu 3 Intellgent Computng Lab, Insttute of Intellgent

More information

Robust Recovery of Camera Rotation from Three Frames. B. Rousso S. Avidan A. Shashua y S. Peleg z. The Hebrew University of Jerusalem

Robust Recovery of Camera Rotation from Three Frames. B. Rousso S. Avidan A. Shashua y S. Peleg z. The Hebrew University of Jerusalem Robust Recovery of Camera Rotaton from Three Frames B. Rousso S. Avdan A. Shashua y S. Peleg z Insttute of Computer Scence The Hebrew Unversty of Jerusalem 994 Jerusalem, Israel e-mal : roussocs.huj.ac.l

More information

MOTION PANORAMA CONSTRUCTION FROM STREAMING VIDEO FOR POWER- CONSTRAINED MOBILE MULTIMEDIA ENVIRONMENTS XUNYU PAN

MOTION PANORAMA CONSTRUCTION FROM STREAMING VIDEO FOR POWER- CONSTRAINED MOBILE MULTIMEDIA ENVIRONMENTS XUNYU PAN MOTION PANORAMA CONSTRUCTION FROM STREAMING VIDEO FOR POWER- CONSTRAINED MOBILE MULTIMEDIA ENVIRONMENTS by XUNYU PAN (Under the Drecton of Suchendra M. Bhandarkar) ABSTRACT In modern tmes, more and more

More information

New Extensions of the 3-Simplex for Exterior Orientation

New Extensions of the 3-Simplex for Exterior Orientation New Extensons of the 3-Smplex for Exteror Orentaton John M. Stenbs Tyrone L. Vncent Wllam A. Hoff Colorado School of Mnes jstenbs@gmal.com tvncent@mnes.edu whoff@mnes.edu Abstract Object pose may be determned

More information

3D Modeling Using Multi-View Images. Jinjin Li. A Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science

3D Modeling Using Multi-View Images. Jinjin Li. A Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science 3D Modelng Usng Mult-Vew Images by Jnjn L A Thess Presented n Partal Fulfllment of the Requrements for the Degree Master of Scence Approved August by the Graduate Supervsory Commttee: Lna J. Karam, Char

More information

arxiv: v1 [cs.ro] 8 Jul 2016

arxiv: v1 [cs.ro] 8 Jul 2016 Non-Central Catadoptrc Cameras Pose Estmaton usng 3D Lnes* André Mateus, Pedro Mraldo and Pedro U. Lma arxv:1607.02290v1 [cs.ro] 8 Jul 2016 Abstract In ths artcle we purpose a novel method for planar pose

More information

A 3D Human Skeletonization Algorithm for a Single Monocular Camera Based on Spatial Temporal Discrete Shadow Integration

A 3D Human Skeletonization Algorithm for a Single Monocular Camera Based on Spatial Temporal Discrete Shadow Integration appled scences Artcle A 3D Human Skeletonzaton Algorthm for a Sngle Monocular Camera Based on Spatal Temporal Dscrete Shadow Integraton Je Hou *, Baolong Guo, Wangpeng He and Jnfu Wu School of Aerospace

More information

Correspondence-free Synchronization and Reconstruction in a Non-rigid Scene

Correspondence-free Synchronization and Reconstruction in a Non-rigid Scene Correspondence-free Synchronzaton and Reconstructon n a Non-rgd Scene Lor Wolf and Assaf Zomet School of Computer Scence and Engneerng, The Hebrew Unversty, Jerusalem 91904, Israel e-mal: {lwolf,zomet}@cs.huj.ac.l

More information

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers IOSR Journal of Electroncs and Communcaton Engneerng (IOSR-JECE) e-issn: 78-834,p- ISSN: 78-8735.Volume 9, Issue, Ver. IV (Mar - Apr. 04), PP 0-07 Content Based Image Retreval Usng -D Dscrete Wavelet wth

More information

DESIGN OF A HAPTIC DEVICE FOR EXCAVATOR EQUIPPED WITH CRUSHER

DESIGN OF A HAPTIC DEVICE FOR EXCAVATOR EQUIPPED WITH CRUSHER DESIGN OF A HAPTIC DEVICE FOR EXCAVATOR EQUIPPED WITH CRUSHER Kyeong Won Oh, Dongnam Km Korea Unversty, Graduate School 5Ga-1, Anam-Dong, Sungbuk-Gu, Seoul, Korea {locosk, smleast}@korea.ac.kr Jong-Hyup

More information

Six-axis Robot Manipulator Numerical Control Programming and Motion Simulation

Six-axis Robot Manipulator Numerical Control Programming and Motion Simulation 2016 Internatonal Conference on Appled Mechancs, Mechancal and Materals Engneerng (AMMME 2016) ISBN: 978-1-60595-409-7 S-as Robot Manpulator Numercal Control Programmng and Moton Smulaton Chen-hua SHE

More information

MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION

MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION Paulo Quntlano 1 & Antono Santa-Rosa 1 Federal Polce Department, Brasla, Brazl. E-mals: quntlano.pqs@dpf.gov.br and

More information

Accounting for the Use of Different Length Scale Factors in x, y and z Directions

Accounting for the Use of Different Length Scale Factors in x, y and z Directions 1 Accountng for the Use of Dfferent Length Scale Factors n x, y and z Drectons Taha Soch (taha.soch@kcl.ac.uk) Imagng Scences & Bomedcal Engneerng, Kng s College London, The Rayne Insttute, St Thomas Hosptal,

More information

Human Skeleton Reconstruction for Optical Motion Capture

Human Skeleton Reconstruction for Optical Motion Capture Journal of Computatonal Informaton Systems 9: 0 (013) 8073 8080 Avalable at http://www.jofcs.com Human Skeleton Reconstructon for Optcal Moton Capture Guanghua TAN, Melan ZHOU, Chunmng GAO College of Informaton

More information

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters Proper Choce of Data Used for the Estmaton of Datum Transformaton Parameters Hakan S. KUTOGLU, Turkey Key words: Coordnate systems; transformaton; estmaton, relablty. SUMMARY Advances n technologes and

More information

Learning the Kernel Parameters in Kernel Minimum Distance Classifier

Learning the Kernel Parameters in Kernel Minimum Distance Classifier Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department

More information

High-Boost Mesh Filtering for 3-D Shape Enhancement

High-Boost Mesh Filtering for 3-D Shape Enhancement Hgh-Boost Mesh Flterng for 3-D Shape Enhancement Hrokazu Yagou Λ Alexander Belyaev y Damng We z Λ y z ; ; Shape Modelng Laboratory, Unversty of Azu, Azu-Wakamatsu 965-8580 Japan y Computer Graphcs Group,

More information

Aligning Non-Overlapping Sequences,

Aligning Non-Overlapping Sequences, Internatonal Journal of Computer Vson 48(1), 39 51, 2002 c 2002 Kluwer Academc Publshers. Manufactured n The Netherlands. Algnng Non-Overlappng Sequences, YARON CASPI AND MICHAL IRANI Department of Computer

More information

An Image Fusion Approach Based on Segmentation Region

An Image Fusion Approach Based on Segmentation Region Rong Wang, L-Qun Gao, Shu Yang, Yu-Hua Cha, and Yan-Chun Lu An Image Fuson Approach Based On Segmentaton Regon An Image Fuson Approach Based on Segmentaton Regon Rong Wang, L-Qun Gao, Shu Yang 3, Yu-Hua

More information

Dynamic Camera Assignment and Handoff

Dynamic Camera Assignment and Handoff 12 Dynamc Camera Assgnment and Handoff Br Bhanu and Ymng L 12.1 Introducton...338 12.2 Techncal Approach...339 12.2.1 Motvaton and Problem Formulaton...339 12.2.2 Game Theoretc Framework...339 12.2.2.1

More information

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices Internatonal Mathematcal Forum, Vol 7, 2012, no 52, 2549-2554 An Applcaton of the Dulmage-Mendelsohn Decomposton to Sparse Null Space Bases of Full Row Rank Matrces Mostafa Khorramzadeh Department of Mathematcal

More information

Tsinghua University at TAC 2009: Summarizing Multi-documents by Information Distance

Tsinghua University at TAC 2009: Summarizing Multi-documents by Information Distance Tsnghua Unversty at TAC 2009: Summarzng Mult-documents by Informaton Dstance Chong Long, Mnle Huang, Xaoyan Zhu State Key Laboratory of Intellgent Technology and Systems, Tsnghua Natonal Laboratory for

More information

A NEW IMPLEMENTATION OF THE ICP ALGORITHM FOR 3D SURFACE REGISTRATION USING A COMPREHENSIVE LOOK UP MATRIX

A NEW IMPLEMENTATION OF THE ICP ALGORITHM FOR 3D SURFACE REGISTRATION USING A COMPREHENSIVE LOOK UP MATRIX A NEW IMPLEMENTATION OF THE ICP ALGORITHM FOR 3D SURFACE REGISTRATION USING A COMPREHENSIVE LOOK UP MATRIX A. Almhde, C. Léger, M. Derche 2 and R. Lédée Laboratory of Electroncs, Sgnals and Images (LESI),

More information

An Adaptive Complementary Filter For Gyroscope/Vision Integrated Attitude Estimation

An Adaptive Complementary Filter For Gyroscope/Vision Integrated Attitude Estimation Paper Int l J. of Aeronautcal & Space Sc. 17(), 1 1 (16) DOI: http://dx.do.org/1.5139/ijass.16.17..1 An Adaptve Complementary Flter For Gyroscope/Vson Integrated Atttude Estmaton Chan Gook Park* Department

More information

Inverse-Polar Ray Projection for Recovering Projective Transformations

Inverse-Polar Ray Projection for Recovering Projective Transformations nverse-polar Ray Projecton for Recoverng Projectve Transformatons Yun Zhang The Center for Advanced Computer Studes Unversty of Lousana at Lafayette yxz646@lousana.edu Henry Chu The Center for Advanced

More information

A Revisit of Methods for Determining the Fundamental Matrix with Planes

A Revisit of Methods for Determining the Fundamental Matrix with Planes A Revst of Methods for Determnng the Fundamental Matrx wth Planes Y Zhou 1,, Laurent Knep 1,, and Hongdong L 1,,3 1 Research School of Engneerng, Australan Natonal Unversty ARC Centre of Excellence for

More information

A New Approach For the Ranking of Fuzzy Sets With Different Heights

A New Approach For the Ranking of Fuzzy Sets With Different Heights New pproach For the ankng of Fuzzy Sets Wth Dfferent Heghts Pushpnder Sngh School of Mathematcs Computer pplcatons Thapar Unversty, Patala-7 00 Inda pushpndersnl@gmalcom STCT ankng of fuzzy sets plays

More information

Palmprint Feature Extraction Using 2-D Gabor Filters

Palmprint Feature Extraction Using 2-D Gabor Filters Palmprnt Feature Extracton Usng 2-D Gabor Flters Wa Kn Kong Davd Zhang and Wenxn L Bometrcs Research Centre Department of Computng The Hong Kong Polytechnc Unversty Kowloon Hong Kong Correspondng author:

More information

Delayed Features Initialization for Inverse Depth Monocular SLAM

Delayed Features Initialization for Inverse Depth Monocular SLAM Delayed Features Intalzaton for Inverse Depth Monocular SLAM Rodrgo Mungua and Anton Grau Department of Automatc Control, Techncal Unversty of Catalona, UPC c/ Pau Gargallo, 5 E-0808 Barcelona, Span, {rodrgo.mungua;anton.grau}@upc.edu

More information

Invariant Shape Object Recognition Using B-Spline, Cardinal Spline, and Genetic Algorithm

Invariant Shape Object Recognition Using B-Spline, Cardinal Spline, and Genetic Algorithm Proceedngs of the 5th WSEAS Int. Conf. on Sgnal Processng, Robotcs and Automaton, Madrd, Span, February 5-7, 6 (pp4-45) Invarant Shape Obect Recognton Usng B-Splne, Cardnal Splne, and Genetc Algorthm PISIT

More information

A Background Subtraction for a Vision-based User Interface *

A Background Subtraction for a Vision-based User Interface * A Background Subtracton for a Vson-based User Interface * Dongpyo Hong and Woontack Woo KJIST U-VR Lab. {dhon wwoo}@kjst.ac.kr Abstract In ths paper, we propose a robust and effcent background subtracton

More information

The Comparison of Calibration Method of Binocular Stereo Vision System Ke Zhang a *, Zhao Gao b

The Comparison of Calibration Method of Binocular Stereo Vision System Ke Zhang a *, Zhao Gao b 3rd Internatonal Conference on Materal, Mechancal and Manufacturng Engneerng (IC3ME 2015) The Comparson of Calbraton Method of Bnocular Stereo Vson System Ke Zhang a *, Zhao Gao b College of Engneerng,

More information

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana

More information

X- Chart Using ANOM Approach

X- Chart Using ANOM Approach ISSN 1684-8403 Journal of Statstcs Volume 17, 010, pp. 3-3 Abstract X- Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are

More information

Human skeleton proportions from monocular data

Human skeleton proportions from monocular data 1266 Peng et al. / J Zheang Unv SCIENCE A 2006 7(7):1266-1274 Journal of Zheang Unversty SCIENCE A ISSN 1009-3095 (Prnt); ISSN 1862-1775 (Onlne) www.zu.edu.cn/zus; www.sprngerlnk.com E-mal: zus@zu.edu.cn

More information

3D vector computer graphics

3D vector computer graphics 3D vector computer graphcs Paolo Varagnolo: freelance engneer Padova Aprl 2016 Prvate Practce ----------------------------------- 1. Introducton Vector 3D model representaton n computer graphcs requres

More information

MOTION BLUR ESTIMATION AT CORNERS

MOTION BLUR ESTIMATION AT CORNERS Gacomo Boracch and Vncenzo Caglot Dpartmento d Elettronca e Informazone, Poltecnco d Mlano, Va Ponzo, 34/5-20133 MILANO boracch@elet.polm.t, caglot@elet.polm.t Keywords: Abstract: Pont Spread Functon Parameter

More information

Recovering Camera Pose from Omni-directional Images

Recovering Camera Pose from Omni-directional Images Recoveg Camera Pose from Omn-drectonal Images Ada S.K. WAN 1 Angus M.K. SIU 1 Rynson W.H. LAU 1,2 1 Department of Computer Scence, Cty Unversty of Hong Kong, Hong Kong 2 Department of CEIT, Cty Unversty

More information

Towards A Human Robot Interaction Framework with Marker-less Augmented Reality and Visual SLAM

Towards A Human Robot Interaction Framework with Marker-less Augmented Reality and Visual SLAM Journal of Automaton and Control Engneerng Vol. 2, No. 3, September 2014 Towards A Human Robot Interacton Framework wth Marker-less Augmented Realty and Vsual SLAM Eranda Lakshantha and Smon Egerton Faculty

More information

A Scalable Projective Bundle Adjustment Algorithm using the L Norm

A Scalable Projective Bundle Adjustment Algorithm using the L Norm Sxth Indan Conference on Computer Vson, Graphcs & Image Processng A Scalable Projectve Bundle Adjustment Algorthm usng the Norm Kaushk Mtra and Rama Chellappa Dept. of Electrcal and Computer Engneerng

More information