情報処理学会研究報告 IPSJ SIG Technical Report Vol.2011-CVIM-178 No /9/ Model-based Human Torso 3D Shape Estimation Shunta Saito, 1 Makiko
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1 Model-based Human Torso 3D Shape Estimation Shunta Saito, 1 Makiko Kochi, 2 Masaaki Mochimaru 2 and Yoshimitsu Aoki 1 This paper attempts to estimate a 3D shape of human torso from 2 pictures which are front and side silhouette of a subject. Our method assumes that input pictures have accurate contours of a subject s body. And regions of torsos are extracted manually from input pictures. We use a deformable body model for estimation. This model is generated by Principal Component Analysis to a homologous model database which is made of whole body scanned data. A result shape is computed by optimizing some deformation parameters of the model with comparing model s silhouettes and input silhouettes. 1 School of Integrated Design Engineering, Graduate School of Science and Technology, Keio University 2 National Institute of Advanced Industrial Science and Technology Digital Human Research Center 1. 1) 2) 3) 3 (PHR) 4) 5) c 2011 Information
2 2. Input pictures ( front and side silhouettes ) 3 6) 7),8) 3 Extract regions of torso PCA Feedback the comparing result and Optimize model 9) 3 1 Active Shape Model 13) compare to side to front Deformable Model orthogonal projection Optimize the model s deformation parameters by nonlinear least-squares method Estimation result Fig. 1 Framework of our method ) 2 c 2011 Information
3 ) 19) 4.2 Side neck point Axilla point Shoulder point Gluteal sulcus 2?? Gluteal sulcus Side neck point Axilla point Shoulder point 4.3 n 3 x = ( x 1 y 1 z 1 x 2 y 2 z 2 x n y n z n ) t (1) m x 1, x 2,, x m p 1, p 2,, p k k x Side neck point Shoulder poit Axilla point Fig. 2 Side neck point Gluteal Sulcus 2 Torso extraction from whole body model Extracted Torso m p i R 3 (1 i k) n x = 1 m m i=1 x i x = x + k s i p i (2) i=1 s = ( s 1 s 2 s k ) t x s s i 0 x 3 4 s i p i p 1, p 2,, p k 1 95% 95% c 2011 Information
4 Accumulative Contribution Rate Fig. 3 Principal Component Order 3 Accumulative contribution rate 5. s ( 1 ) ( 2 ) ( 3 ) ( 4 ) s s s s S input S model d d = D(S model ) + D(Ŝmodel) (3) 4 Fig. 4 Deformable model 5 95% S model Ŝmodel S model D 5 L 2 0 f(s) = d(u, v)dudv (4) A A = {(u, v) L input(u, v) = white} (5) 4 c 2011 Information
5 20) l E(s) = (F i (s)) 2 (7) i= Fig. 5 Euclidean distance from model s silhouette contour 3 S model 5 L input 4 f front (s) f side (s) F (s) = f front (s) + f side (s) (6) l 2 s s i = 0(l < i k) 1 i l s i 6 l s i E(s) Levenberg-Marquardt mm 9.361mm 5 c 2011 Information
6 6 Fig. 6 Error distribution 6 MacOS X GHz Intel Core 2 Duo 4GB RAM sec mm 1 6 1) Masaaki M, Dhaiba, The Digital Human Software to Represent Human Functions and Individual Variations, Journal of the Society of Instrument and Control Engineers 45(12), , ) Martin R., und R. Knussmanb, 1988: Anthropologie: Handbuch der vergleichenden Biologie de Menschen, Band I. Gustav Fischer. 3) digital fashion ltd., HAOREBA R, DRESSTA R, available from 4) Kaelber, David; Pan, Eric (2008). The Value of Personal Health Record (PHR) Systems. AMIA Annu Symp Proc: 343?347. 5), 3D, available from 6) M. A. Brunsman, H. A. Daanen and K. M. Robinette, Optimal postures and positioning for human body scanning, Proceedings of International Conference on Recent Advances in 3-D Digital Imaging and Modeling (3DIM 97), pp (1997) 7) L. Zhang, N. Snavely, B. Curless and S. M. Seitz, Spacetime faces: High Resolution Capture for Modeling and Animation, ACM Transaction on Graphics, 23, 3, pp (2004) 6 c 2011 Information
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16
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26 Development of Learning Support System for Fixation of Basketball Shoot Form
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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