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1 NAIST-IS-MT
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5 Generating a Panoramic Movie by Calibrating an Omnidirectional Multi-camera System 3 Sei IKEDA Abstract In the eld of mixed reality, virtualization of real outdoor scenes is widely investigated for a number of applications such astelepresence and walk-through systems. An omnidirectional multi-camera system is more useful to virtualize outdoor scenes than a monocular camera system, because the multi-camera system can easily capture high-resolution omnidirectional images. However, the exact calibration of the system is necessary to virtualize the real world accurately. In this paper, we describe a method for generating a high-resolution panoramic movie by calibrating an omnidirectional multi-camera system geometrically and photometrically. In experiments, a spherical panoramic video has been successfully computed to show the validity of the proposed calibration method. Finally, weshowsome prototypes of telepresence system using various immersive displays. Keywords: Omnidirectional Multi-Camera System, Camera Calibration, Panoramic Image Generation, Telepresence 3 Master's Thesis, Department of Information Systems, Graduate School of Information Science, Nara Institute of Science and Technology, NAIST-IS-MT , February 7, iii
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7 : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : v
8 1 ( : NHK 21) : : : : : : : : 4 2 : : : : : : : : : : : : : : : : : 9 3 Ladybug ( ) ( ) : : : : : : : : : 13 4 ( ( ) ( )) : : : : : : : : : : : 14 5 Ladybug : : : : : : : : : : : : : : : : : : : 14 6 : : : : : : : : : : : : : : : : : : 18 7 : : : : : : : : : : : : : : : : : : : : : : 19 8 : : : : : : : : : : : : : : : : : : 19 9 : : : : : : : : : : : : : : : : : : : : : : : : : : : : : cos 4 : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : (R ) : : : : : : : : : : : : : : : : (G ) : : : : : : : : : : : : : : : (B ) : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : ( : ( ) ( )) : : : ( ) : : : : : : : : : : : ( ) : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 41 vi
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15 .,.,.,, 1,. [5], ,., 360,. : [23, 24], 360.,.,. [25], [23],.,. 6
16 CYLINDRA[25, 26],,.,,. : [15, 27].,,..,.. Point Grey Research, Ladybug[15] 5.,,.,, ,,,,,.,,..,, 7
17 1 HD,.,,.,,,. 2.2,,.,,., [23], 2(a),.,, 8
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22 3 Ladybug ( ) ( ) 2 Ladybug % 15fps 20 13
23 ( ( ) ( )) ª ªª ªªªªª n ªªªª 5 Ladybug 14
24 3.2 (,,, ) (, ) ,.,, Tsai [32]..,. Tsai, 6 (X d ;Y d ) (X u ;Y u ). X u = X d (1 + 1 r r 4 ) (1) Y u = Y d (1 + 1 r r 4 ) (2) q r = Xd 2 + Yd 2 (3),, 1 :, 1,,,. [33], 2. 15
25 ,., X Y d x d y. (X d ;Y d ) (X f ;Y f ),. X d = d0 x s x (X f 0 C x ); Y d = d y (Y f 0 C y ) (4), C x ;C y, s x, d 0 x, X N cx N fx (d 0 N x = d cx x N fx )., Tc(t x ;t y ;t z ) Rc(; ; ), Mc. Mc = = = 2 4 R c Tc r 1 r 2 r 3 t x r 4 r 5 r 6 t y r 7 r 8 r 9 t z c 1 c 3 + s 1 s 2 s 3 s 1 c 2 0c 1 s 3 + s 1 s 2 c 3 t x 0s 1 c 3 + c 1 s 2 s 3 c 1 c 2 s 1 s 3 + c 1 s 2 c 3 t y c 2 s 3 0s 2 c 2 c 3 t z (5), s 1 =sin; s 2 =sin; s 3 =sin c 1 =cos; c 2 = cos ; c 3 = cos (6), 6 [x W ;y W ;z W ] T, [x C ;y C ;z C ] T, c. 16
26 2 6 4 x C y C z C = M x W y W z W (7),, 3 ( f, ( 1 ; 2 ), (C x ;C y ), s x ) ( c Tc, Rc) ,.,., 7., 3. 8,,,.,.,,.,.,, ,, 1. 17
27 ªª zw ( x u, yu ) ( x d, yd ) x I xc z C O I O C O W y W x W y I y C s M ªª 6 3 Tc(t x ;t y ;t z ) Rc(; ; ) f C x ; C y 1 ; 2 s x 18
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29 ªª d 9 c(c =0; 1; :::; 5) Tc Rc, Mc. Mc, m(m = 1; 2;:::) xm c(c = 0; 1; :::; 5) um, [36] M 0 c. M 0 c 12(r 1 ;r 2 ;:::;r 9 ;t x ;t y ;t z ) Rc.,, 6 (; ; ; t x ;t y ;t z ) Mc., vm um (, ) X E c = (um 0 vm) 2 (8) m Mc. 20
30 3.3,., cos 4,, ,,., cos 4 [37] [38],, cos 4. Horn[37], 10 l, I I 0,,. I 0 = l2 cos 4 f 2 I (9) l f, cos J L J = al + b, c c 0 I c I c 0 I c 0 = a c I c + b c (10). a c ;b c., 21
31 I l θ I' f ˆ ªªª 10 cos 4, c a c ;b c.,, RGB., 2 a c ; b c,.,,,., c c 0 i h c (i) h c0 (i) a c ;b c. RGB e(a c ;b c )= X i ( h c0 (i) 0 1 a c h c i 0 b c a c!) 2 (11), a c ;b c.,,., (10), RGB. 22
32 3.4 Ladybug, Ladybug, 50cm, , 561.,, 170, LEICA TCR1105 XR,.,,. 12,., , 5., 13.,., 14.,.,,., 5,,. 14 Ladybug, 5,. 15.,, 23
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![06 3 4 5 1 [1/mm 2 ] 0.0263 0.0308 0.0357 2 [1/mm 4 ] 0.0131 0.0126 0.0109 f [mm] 2.5317 2.5221 2.](/docs-images/104/162024314/images/36-1.jpg "5654 s x 0.9987 0.9989 0.9980 C x [pixel] 353.87 344.04 365.02 C y [pixel] 482.26 478.54 524.")
36 [1/mm 2 ] [1/mm 4 ] f [mm] s x C x [pixel] C y [pixel] [1/mm 2 ] [1/mm 4 ] f [mm] s x C x [pixel] C y [pixel] (a) (b) 13 27
37 (a) 2 (b) (c)
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38 5 [pixel] (a) (b) 15 29
39 Š (a) Š (b) 16 (R ) 30
40 Š (a) Š (b) 17 (G ) 31
41 Š (a) Š (b) 18 (B ) 32
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43 4., ( ). 4.1, 20,,,.,,.,.,,,.,,,.,, S. G., S., S s I S (s),. c s u c c, s I S (s), s C(s). I S (s) = P c2c(s) c I c (u c ) Pc2C (s) c (12),, (; ) 34
44 yt ªª jwt ˆ ªªªªªªªªª { ªªªª jwªªªªª ªªªªªªª ªªªª 20, 21,,. 4.2, S, x 2 c; c 0 u c ;u c 0, S s c ;s c 0, 2. S N, 6 s c Gs c 0 < 2 N, 1. 6 s c Gs c 0! 6 s c xs c 0, 6 T c xt c 0 < 2 N x. 2 d, x, 1. d > 2tan N (13), Ladybug, 40mm,
45 x z φ θ y φ θ ˆ ªªªª 21 ˆ S ªª c s c x u c T c G s c u c T c ªª c 22 36
46 , 1, 24m , 6.,, , 23 Ladybug. 24 ( : )., 25., 25,.,,, 768pixel pixel.,, 1920pixel. 25,,., 26,. 25, 27,,,.,,. 37
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54 5.1, 7, 2.,,., , JPEG. Ladybug 15fps.,.,,.,,,. 5.2, 8, 2.,, 7 Olympus Eye-Trek Polhemus Fastrack CPU: Intel Pentium4 1.7GHz, : 1GB Nvidia Geforce4 45
55 ªªªª ªª ª ªªª ªªª «ªªª«ªªª «ªªªª«30,., ,. 15fps.,.,,.,,.,. 5.3, 8 CYLINDRA 2.,, 32 46
56 8 Elumens VisionStaion Microsoft SideWinder Game Pad Pro CPU: Intel Pentium4 1.7GHz, : 1GB Nvidia Geforce
57 ., , , 15fps.,,.,,.,,. 48
58 9 CYLINDRA SGI Onyx2: CPU:MIPS R MHz 8 InniteReality ( ) 33 49
59 6.,, Ladybug.,.,,,.,.,,.,. Ladybug,., Ladybug,.,, 3. 3,.,,,.,.,.,,.,,, 50
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64 [20] Y. Yagi, Y. Nishizawa and M. Yachida: \Estimating Location and Avoiding Collision Against Unknown Obstacle for the Mobile Robot Using Omnidirectional Image Sensor COPIS", Proc. Int. Workshop on Intelligent Robots and Systems, pp. 909{914 (1991). [21],, \ HyperOmni Vision ",, J79-D-II, 5, pp. 698{707 (1996). [22] S. K. Nayar: \Catadioptric Omnidirectional Camera", Proc. Computer Vision and Pattern Recognition, pp. 482{488 (1997). [23] T. Kawanishi, K. Yamazawa, H. Takemura and N. Yokoya: \Generation of Hight-resolution Stereo Panoramic Images by Omnidirectional Imageing Sensor Using Hexagonal Pyramidal Mirrors", Proc. 14th Int. Conf. on Pattern Recognition, 1, pp. 445{489 (1998). [24] : \FC2", (2000). [25] J. Shimamura, H. Takemura, N. Yokoya and K. Yamazawa: \Construction of an Immersive Mixed Environment Using an Omnidirectional Stereo Image Sensor", Proc. IEEE Workshop on Omnidirectional Vision, pp. 62{69 (2000). [26],,, \ ",, MVE99-82 (2000). [27] H. Tanahashi, K. Yamamoto, C. Wang and Y. Niwa: \Development of a Stereo Omnidirectional Imageing System (SOS)", Proc. IEEE Int. Conf. on Industrial Electronics, Control and Instrumentation (IECON2000), pp. 289{ 294 (2000). 55
65 [28],,, \ ", 4, pp. 211{212 (1999). [29],,,, \ (SOS) ",, 56, 4, pp. 603{610 (2002). [30] T. Yamada, M. Hirose and Y. Iida: \Development of Complete Immersive Display", Proc. 4th Int. Conf. on Virtual Systems and Multimedia (VSMM'98), 2, pp. 522{527 (1998). [31] \ ", NAIST-IS-DT (2003). [32] R. Y. Tsai: \A Versatile Camera Calibration Technique for High-accuracy 3D Machine Vision Metrology Using O-the-shelf TV Cameras and Lenses", IEEE Journal of Robotics and Automation, RA-3, 4, pp. 323{344 (1987). [33] J. Z. C. Lai: \On the Sensitivity of Camera Calibration", Image and Vision Computing, 11, 10, pp. 656{664 (1993). [34] \ 2 ",, 4, J63-D, pp. 349{356 (1980). [35] T. Sato, M. Kanbara, N. Yokoya and H. Takemura: \Dense 3D Reconstruction of an Outdoor Scene by Hundreds-Baseline Stereo Using a Hand- Held Video Camera", Int. Journal of Computer Vision, 47, 1-3, pp. 110{129 (2002). [36] \ PnP ", '90, 90, 20, pp. 41{50 (1990). 56
66 [37] B. K. P. Horn: \Robot Vision", chapter 10, pp. 206{209, Mit Press (1986). [38] N. Asada, A. Amano and M. Baba: \Photometric Calibration of Zoom Lens Systems", Proc. Int. Conf. Pattern Recognition, A, pp. 186{190 (1996). 57
1 Kinect for Windows M = [X Y Z] T M = [X Y Z ] T f (u,v) w 3.2 [11] [7] u = f X +u Z 0 δ u (X,Y,Z ) (5) v = f Y Z +v 0 δ v (X,Y,Z ) (6) w = Z +
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3 3D 1,a) 1 1 Kinect (X, Y) 3D 3D 1. 2010 Microsoft Kinect for Windows SDK( (Kinect) SDK ) 3D [1], [2] [3] [4] [5] [10] 30fps [10] 3 Kinect 3 Kinect Kinect for Windows SDK 3 Microsoft 3 Kinect for Windows
28 Horizontal angle correction using straight line detection in an equirectangular image
28 Horizontal angle correction using straight line detection in an equirectangular image 1170283 2017 3 1 2 i Abstract Horizontal angle correction using straight line detection in an equirectangular image
& Vol.5 No (Oct. 2015) TV 1,2,a) , Augmented TV TV AR Augmented Reality 3DCG TV Estimation of TV Screen Position and Ro
TV 1,2,a) 1 2 2015 1 26, 2015 5 21 Augmented TV TV AR Augmented Reality 3DCG TV Estimation of TV Screen Position and Rotation Using Mobile Device Hiroyuki Kawakita 1,2,a) Toshio Nakagawa 1 Makoto Sato
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PAL On the Precision of 3D Measurement by Stereo PAL Images Hiroyuki HASE,HirofumiKAWAI,FrankEKPAR, Masaaki YONEDA,andJien KATO PAL 3 PAL Panoramic Annular Lens 1985 Greguss PAL 1 PAL PAL 2 3 2 PAL DP
2003/3 Vol. J86 D II No.3 2.3. 4. 5. 6. 2. 1 1 Fig. 1 An exterior view of eye scanner. CCD [7] 640 480 1 CCD PC USB PC 2 334 PC USB RS-232C PC 3 2.1 2
![2003/3 Vol. J86 D II No.3 2.3. 4. 5. 6. 2. 1 1 Fig. 1 An exterior view of eye scanner. CCD [7] 640 480 1 CCD PC USB PC 2 334 PC USB RS-232C PC 3 2.1 2](/thumbs/42/22941906.jpg "2003/3 Vol. J86 D II No.3 2.3. 4. 5. 6. 2. 1 1 Fig. 1 An exterior view of eye scanner. CCD [7] 640 480 1 CCD PC USB PC 2 334 PC USB RS-232C PC 3 2.1 2")
Curved Document Imaging with Eye Scanner Toshiyuki AMANO, Tsutomu ABE, Osamu NISHIKAWA, Tetsuo IYODA, and Yukio SATO 1. Shape From Shading SFS [1] [2] 3 2 Department of Electrical and Computer Engineering,
光学
Fundamentals of Projector-Camera Systems and Their Calibration Methods Takayuki OKATANI To make the images projected by projector s appear as desired, it is e ective and sometimes an only choice to capture
Fig Measurement data combination. 2 Fig. 2. Ray vector. Fig (12) 1 2 R 1 r t 1 3 p 1,i i 2 3 Fig.2 R 2 t 2 p 2,i [u, v] T (1)(2) r R 1 R 2
![Fig Measurement data combination. 2 Fig. 2. Ray vector. Fig (12) 1 2 R 1 r t 1 3 p 1,i i 2 3 Fig.2 R 2 t 2 p 2,i [u, v] T (1)(2) r R 1 R 2](/thumbs/93/113464882.jpg "Fig Measurement data combination. 2 Fig. 2. Ray vector. Fig (12) 1 2 R 1 r t 1 3 p 1,i i 2 3 Fig.2 R 2 t 2 p 2,i [u, v] T (1)(2) r R 1 R 2")
IP 06 16 / IIS 06 32 3 3-D Environment Modeling from Images Acquired with an Omni-Directional Camera Mounted on a Mobile Robot Atsushi Yamashita, Tomoaki Harada, Ryosuke Kawanishi, Toru Kaneko (Shizuoka
光学
Range Image Sensors Using Active Stereo Methods Kazunori UMEDA and Kenji TERABAYASHI Active stereo methods, which include the traditional light-section method and the talked-about Kinect sensor, are typical
(a) 1 (b) 3. Gilbert Pernicka[2] Treibitz Schechner[3] Narasimhan [4] Kim [5] Nayar [6] [7][8][9] 2. X X X [10] [11] L L t L s L = L t + L s
![(a) 1 (b) 3. Gilbert Pernicka[2] Treibitz Schechner[3] Narasimhan [4] Kim [5] Nayar [6] [7][8][9] 2. X X X [10] [11] L L t L s L = L t + L s](/thumbs/75/72066128.jpg "(a) 1 (b) 3. Gilbert Pernicka[2] Treibitz Schechner[3] Narasimhan [4] Kim [5] Nayar [6] [7][8][9] 2. X X X [10] [11] L L t L s L = L t + L s")
1 1 1, Extraction of Transmitted Light using Parallel High-frequency Illumination Kenichiro Tanaka 1 Yasuhiro Mukaigawa 1 Yasushi Yagi 1 Abstract: We propose a new sharpening method of transmitted scene
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![(3.6 ) (4.6 ) 2. [3], [6], [12] [7] [2], [5], [11] [14] [9] [8] [10] (1) Voodoo 3 : 3 Voodoo[1] 3 ( 3D ) (2) : Voodoo 3D (3) : 3D (Welc](/thumbs/65/54056306.jpg "(3.6 ) (4.6 ) 2. [3], [6], [12] [7] [2], [5], [11] [14] [9] [8] [10] (1) Voodoo 3 : 3 Voodoo[1] 3 ( 3D ) (2) : Voodoo 3D (3) : 3D (Welc")
1,a) 1,b) Obstacle Detection from Monocular On-Vehicle Camera in units of Delaunay Triangles Abstract: An algorithm to detect obstacles by using a monocular on-vehicle video camera is developed. Since
258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS 2 3 4 5 2. 2.1 3 1) GPS Global Positioning System
Vol. 52 No. 1 257 268 (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver.
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THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. 630-0192 8916-5 E-mail: {kaduya-o,takafumi-t,goshiro,uranishi,miyazaki,kato}@is.naist.jp,.,,.,,,.,,., CG.,,,
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22 A person recognition using color information 1110372 2011 2 13 ,,.,.,,.,.,.,.,,.,..,,,, i Abstract A person recognition using color information Tatsumo HOJI Recently, for the purpose of collection of
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36 Kitchen of the Future: Kitchen of the Future Kitchen of the Future A kitchen is a place of food production, education, and communication. As it is more active place than other parts of a house, there
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12 Exploration Method of Various Routes with Genetic Algorithm 1010369 2001 2 5 ( Genetic Algorithm: GA ) GA 2 3 Dijkstra Dijkstra i Abstract Exploration Method of Various Routes with Genetic Algorithm
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