情報処理学会研究報告 IPSJ SIG Technical Report Vol.2014-GN-90 No.6 Vol.2014-CDS-9 No.6 Vol.2014-DCC-6 No /1/23 Bullet Time 1,a) 1 Bullet Time Bullet Time
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1 Bullet Time 1,a) 1 Bullet Time Bullet Time Generation Technique and Eveluation on High-Resolution Bullet-Time Camera Work Ryuuki Sakamoto 1,a) Ding Chen 1 Abstract: The multi-camera environment have been used in movie studio when they intent to apply the Bullet Time camera work to a scene. The camera work is realized with lipping through rames at same moment taken by multi cameras surrounding an object at even distances. For making outcome rames o the camera work, the Homography transormation is adapted or rectiying inaccurate camera poses. Thereore the Homography transormation, however, makes some blank spaces during distorting the rame, the scale up transormation should be applied ater that. The scaling up, however, makes the quality o the outcame down. In this paper, we proposed a method to calculate Homography matrices or keeping the quality and naturality o outcome rames o the camera work. For measuaring the eectiveness o the method, we also describe the result o a user evaluation Bullet Time 10 Bullet Time [1] 1 Yahoo Japan Corp. Mid9-7-1 Akasaka, Minato-ku a) ryusakam@yahoo-corp.jp c 2014 Inormation Processing Society o Japan 1
2 ps XGA VGA 1 3 FullHD TV PC Fig. 1 Conversion by eq.3 Bullet Time 3 Web [3], [4] Bullet Time H k k = 1,...,N Bullet Time k A k [2] R k T k A k Bullet Time k 0 u0 k A k = 0 k v0 k (1) 2. Bullet Time xyz xz y = 0 y k u0 k v0 k N C k = R 1 k Bullet Time T k G e z e z y 3 G x e x e z e x z e z G R k G xz G k C k k = 1,...,N e x / e x C k C k R k = e y / e y (2) G e z / e z H k H k = A k R kr 1 k 3 A 1 k (3) C k G G 1 G c 2014 Inormation Processing Society o Japan 2
3 H k = A kr kr 1 k A 1 k (5) 4. 2 Fig. 2 Conversion by eq.3 2 v αk (α = 0,1,2,3) H k v βk (β = 0,1,2,3) [ ] [ ] v βk λ = H v αk k (6) G 1 1 g k G 2 v αk v βk 2 v βk S k S0 S0 / S k S k k (u0 k,v0 k ) S0 H k 3. S k = (v(β mod 4)k 8 v αk) (7) α=0β=0 (v (β+1 mod 4)k v αk ) 1 k E C k G N k argmax E( k,u0 k,v0 k ) = S0 (8) C k G G S k G C k G G C k E k (u0 k,v0 k ) [1] Bullet Time (u0 k,v0 k ) C k R k 1: (u0 k,v0 k ) (u0 k,v0 k ) G G g k k N E k=1 g k/n (u0 k,v0 k ) A k 2: (u0 k,v0 k ) H k 1 (u0 k,v0 k ) g k k 0 u0 k k A k = 0 k v0 k (4) N g k k=1 5. c 2014 Inormation Processing Society o Japan 3
4 g k 1 1 E 3: k 1 A B k 2 C D k C k G = average (9) z average N k average = N z average = k=1 N k=1 C k G N 1 Eye Bullet Time Vision[12] Eye Vision G 4: g k 2 Bullet Time C k G C k C k [1] k Eye Vision α k k α = C k G k = α(c k G) (10) 2 1 (u0 k,v0 k ) α 2 3 A k 3 k k E 3 E (1) G (2) 1 2 (u0 k,v0 k ) (3) 3 4 k (5) H k B 3 k A B C D Table 1 Combination o each technique [5], [6], [7], [8], [9], [10], [11] Bullet Time 3 TV (4) (7) k 1 3 A 2 Bullet Time c 2014 Inormation Processing Society o Japan 4
5 3 Bullet Time Fig. 3 Bullet Time camera work on each scene 2 E Table 2 E values o each technique. A B C D E α A B α B α C A 1 2 (u0 k,v0 k ) A B α Fig. 4 α on combination A and B E (u0 k,v0 k ) A C (u0 k,v0 k ) C 5 k (u0 k,v0 k ) D E A C (u0 k,v0 k ) Fig. 5 (u0 k,v0 k ) on combination A and C 8. Bullet Time E k (u0 k,v0 k ) 8.1 Bullet Time Point Grey Research Flea2 Web c 2014 Inormation Processing Society o Japan 5
6 30 90 A D http Web HTML5 JavaScript JavaScript 80 Bullet Time 6 4 A Bullet Time 7 Web Fig. 7 Screenshot o an evaluation page 3 5 Table 3 Result o telepresence. Web A B C D Bullet Time n T T Bullet Time Bullet Time Bullet Time Bullet Time Bullet Time 7 5 Web A c 2014 Inormation Processing Society o Japan 6
7 6 Bullet Time Fig. 6 Bullet Time camera work on each scene Bullet Time A D 1% Bullet Time 4 t Bullet Time 1% F(3,566)=4.23, p<.01 Tukey 5% A D A B A D 5 Web 1 D c 2014 Inormation Processing Society o Japan 7
8 4 4 Table 4 Results o each technique. A B C D 4 E A Bullet Time B C D A Bullet Time 1 1 Bullet Time 4 1 [1] : 2 Vol. 106, No. 429, pp (2006). 1 [2] Nakanishi, H., Murakami, Y. and Kato, K.: Movable cameras enhance social telepresence in media spaces, Proceedings o the SIGCHI, ACM, pp (2009). 1 [3] Hartley, R. and Zisserman, A.: Multiple view geometry in computer vision, Cambridge Univ Press (2000). [4] Szeliski, R.: Computer vision: algorithms and applications, Springer (2011). 2 [5] Kanade, T., Rander, P. and Narayanan, P.: Virtualized reality: Constructing virtual worlds rom real scenes, MultiMedia, Vol. 4, No. 1, pp (1997). [6] Kitahara, I. and Ohta, Y.: Scalable 3D representation D or 3D video in a large-scale space, Presence: Teleoperators and Virtual Environments, Vol. 13, No. 2, pp. E E (2004). [7] Inamoto, N. and Saito, H.: Intermediate view generation 1 o soccer scene rom multiple videos, Proc. on 16th International Conerence on Pattern Recognition, Vol. 2, IEEE, pp (2002). (F(3,135)=0.48, n.s.) 2 [8] Hilton, A., Guillemaut, J., Kilner, J., Grau, O. and Thomas, G.: 3d-tv production rom conventional cameras or sports broadcast, Broadcasting, IEEE Transac- (F(3,135)=1.58, n.s.) tions on, Vol. 57, No. 2, pp (2011). [9] Hashimoto, T., Uematsu, Y. and Saito, H.: Generation o see-through baseball movie rom multi-camera views, IEEE International Workshop on Multimedia Signal Processing, IEEE, pp (2010). Bullet Time [10] Kimura, K. and Saito, H.: Video synthesis at tennis player viewpoint rom multiple view videos, Proceedings. VR 2005., IEEE, pp (2005). 9. [11] Tomiyama, K., Miyagawa, I. and Iwadate, Y.: Prototyping o HD Multi-Viewpoint Image Generating Bullet Time System-Live broadcasting use at gymnastics competition (60 th National Championships)-, IEIC Technical Report, Vol. 106, No. 429, pp (2006). 4 [12] Kanade, T. et al.: EyeVision, Web, pvi-inc. com/eyevision. 1 c 2014 Inormation Processing Society o Japan 8
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23 FPGA CUDA Performance Comparison of FPGA Array with CUDA on Poisson Equation (lijiang@sekine-lab.ei.tuat.ac.jp), (kazuki@sekine-lab.ei.tuat.ac.jp), (takahashi@sekine-lab.ei.tuat.ac.jp), (tamukoh@cc.tuat.ac.jp),
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NAIST-IS-MT9951117 2001 2 9 ( ) 3 CG, VR.,,,.,,,,,.,, 2, 3 3,.,, 2, 3.,,,,,.,,,.,,.,,, 3, NAIST-IS- MT9951117, 2001 2 9. i Intaractive terrain generation within Immersive Modeling System 3 Ryutarou Morimoto
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4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q
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Barro Regression Does social capital improve regional economic growth? - Investigation using prefectural cross-sectional data in Japan - Abstract The purpose of this research is to empirically examine
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九州大学学術情報リポジトリ Kyushu University Institutional Repository 多視点動画像処理による 3 次元モデル復元に基づく自由視点画像生成のオンライン化 : PC クラスタを用いた実現法 上田, 恵九州大学システム情報科学研究院知能システム学部門 有田, 大作九州大学システム情報科学研究院知能システム学部門 谷口, 倫一郎九州大学システム情報科学研究院知能システム学部門
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THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS HCG HUMAN COMMUNICATION GROUP SYMPOSIUM. UbiCode 243 0292 1030 E-mail: {ubicode,koide}@shirai.la, {otsuka,shirai}@ic.kanagawa-it.ac.jp
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Cami-log: 1,a) 1,b) 1,c) 1,d),,,.,,.,,,.,, Cami-log,. Cami-log : Proposal of Application to Improve Daily Chewing Activities using Myoelectric Information Hiroki Kurosawa 1,a) Sho Mitarai 1,b) Nagisa Munekata
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Transactions of the Operations Research Society of Japan Vol. 58, 215, pp. 148 165 c ( 215 1 2 ; 215 9 3 ) 1) 2) :,,,,, 1. [9] 3 12 Darroch,Newell, and Morris [1] Mcneil [3] Miller [4] Newell [5, 6], [1]
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