xx/xx Vol. Jxx A No. xx 1 Fig. 1 PAL(Panoramic Annular Lens) PAL(Panoramic Annular Lens) PAL (2) PAL PAL 2 PAL 3 2 PAL 1 PAL 3 PAL PAL 2. 1 PAL
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1 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 PAL DP PAL (1) (2) (3) [1] PAL (3) PAL Panoramic Annular Lens 1985 Greguss [2] PAL [3] (perspective projection) PAL 1, Faculty of Engineering, Toyama University, Gofuku 3190, Toyama-shi, Toyama, Japan, Toshiba Corporation, 2-9,Suehiro-cho,Ome, Tokyo, Japan, Department of Information Engineering, Graduate School of Engineering, Nagoya university, Furou-cho Chigusa-ku, Nagoya-shi, Japan 1 PAL 1 2 PAL (1)2 [3] [4] (2) 2 [5] (3)1 [6] [4] [8] 2 [5] 3 A Vol. Jxx A No. xx pp. 1 8 xxxx xx 1
2 xx/xx Vol. Jxx A No. xx 1 Fig. 1 PAL(Panoramic Annular Lens) PAL(Panoramic Annular Lens) PAL (2) PAL PAL 2 PAL 3 2 PAL 1 PAL 3 PAL PAL 2. 1 PAL PAL Panoramic Annular Lens PAL C PAL ( 3) PAL PAL 2 PAL 2. 2 PAL 1 LED 12 4 LED LED 2 PAL Fig. 2 Optical structure of PAL 3 PAL Fig. 3 PAL image LED α( ) PAL LED r α r 5 5 (X, Z) PAL (x, z) p r = (X X C ) 2 +(Z Z C ) 2 R I (1) 2
3 PAL 4 Fig. 4 Measurement scheme Fig. 6 6 Elevation angle and mapping position 5 PAL Fig. 5 Coodinates of PAL image (X C,Z C) PAL R I 5 PAL x z 5 θ 5 r α (pixel) α( ) 2 PAL 2 2 (2) 6 α = r r (2) 2. 3 PAL 1 PAL [7] PAL [8] 1 PAL PAL (2) 7 8(a) 3 8(b) (2) 2 0 (2) PAL PAL PAL 2 PAL 2 PAL 9 PAL 2 α β (3) d 3
4 xx/xx Vol. Jxx A No. xx 9 Fig. 9 Simulation scheme tan β =(h + b)/d d α θ 3 (4) x = d cos θ y = d tan α z = d sin θ (4) d = 7 Fig. 7 Cylindrical coordinate (a) Distortion free image (b) Correction by a linear function 8 Fig. 8 b tan β tan α 3 Corrected view(a part of Fig.3) (3) b tan α = h/d A 2 PAL PAL θ =0 2 2 PAL (2) 2 h 1m 0.5m 0m 0.15m 0.3m 0.7m 6 A B 0.3m 2m 100m 10(a)(b)(c) (a) h =1, 0.5 (b) h =0, 0.15 (c) h = 0.3, m 20m % 4
5 PAL (a) Measurement error for h =1.0, 0.5 Fig % 10% error vs. distance between two cameras 4. (b) Measurement error for h =0, 0.15 (c) Measurement error for h = 0.3, Fig. 10 Measurement error 0.2m 0.3m 0.4m 0.5m PAL CCD 4. 1 PAL DP DP DP PAL PAL PAL DP DP PAL f1 K (i) f2 K (j) 12 K {R, G, B} (5) (i, j) g g(i, j) =F 1 + F 2 + F 3 (5) F 1 F 2 F 3 F 1 =max [f 1 K (i) f2 K (j)] 2 K F 2 =max[(f1 K (i +1) f1 K (i)) K 5
6 xx/xx Vol. Jxx A No. xx (a) Top camera image 12 Fig. 12 Intensity functions on an epi-polar line (f K 2 (j +1) f K 2 (j))] 2 F 3 =min[g(i 1,s)+max[(f1 K (i) f1 K (i 1)) s K (f2 K (j) f2 K (s))] 2 +((j s 1) 10) 2 ] F 1 R, G, B F 2 F 3 i 1 DP PAL 2 PAL m 2 NIKON COOLPIX900 (b) Bottom camera image (c) Perspective projection image (d) Distance image 6
7 PAL (e) 3D image (f) Displacement of the view point 13 3 Fig. 13 Example of 3D data RGB PAL DP , m 8m 2.5m (a)(b) PAL 3 (c) (c) 11m (d) (a) (b) (d) 1m 0.5 (d) (window) (pillar) DP DP 1 (sink) AV (AV) (chair) 3 (e) (f) (d) (c)(e)(f) PC CPU 1.1GHz Celeron 512MB PAL PAL Panoramic Annular Lens PAL 1 PAL 2 2 PAL 30cm 20m 10 PAL 3 DP [1] Y. Yagi; Omnidirectional sensing and its application, IEICE Trans. Inform. and Systems, Vol.3 7
8 xx/xx Vol. Jxx A No. xx pp [2] P. Greguss; PAL-optic based instruments for space research and robotics, Laser and Optoelektronik, Vol.28, pp43 49, [3] ;, D-II, Vol.J79-D-II, No.5, pp , [4] ;,, PRMU200-69, pp.57-62, [5] ; 3, Vol.51, No.10, pp , [6] ;, D-II Vol.J84-D-II, No.9, pp , [7] F. Ekpar, H.Hase, M.Yoneda; Constructing Arbitary Perspective-Corrected Views From Panoramic Images Using Neural Networks, Proceeding of the 7th International Conference on Neural Information Processing, pp , [8] ; 3, PRMU pp x xx xx IEEE CENPARMI (2002 ) IEEE Computer Society
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
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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,
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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.
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 +
![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 +](/thumbs/81/83701592.jpg "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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IP IIS Construction of Overhead View Images by Estimating Intrinsic and Extrinsic Camera Parameters of Multiple Fish-Eye Cameras Shota Kas
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28 Horizontal angle correction using straight line detection in an equirectangular image
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2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server
![2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server](/thumbs/91/107223763.jpg "2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server")
a) Change Detection Using Joint Intensity Histogram Yasuyo KITA a) 2 (0 255) (I 1 (x),i 2 (x)) I 2 = CI 1 (C>0) (I 1,I 2 ) (I 1,I 2 ) 2 1. [1] 2 [2] [3] [5] [6] [8] Intelligent Systems Research Institute,
24 21 21115025 i 1 1 2 5 2.1.................................. 6 2.1.1........................... 6 2.1.2........................... 7 2.2...................................... 8 2.3............................
VRSJ-SIG-MR_okada_79dce8c8.pdf
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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Computer Security Symposium October ,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) [1] 1 Meiji U
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1 (1) ( i ) 60 (ii) 75 (iii) 315 (2) π ( i ) (ii) π (iii) 7 12 π ( (3) r, AOB = θ 0 < θ < π ) OAB A 2 OB P ( AB ) < ( AP ) (4) 0 < θ < π 2 sin θ
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Fig. 3 Flow diagram of image processing. Black rectangle in the photo indicates the processing area (128 x 32 pixels).
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(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
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