IPSJ SIG Technical Report Taubin Ellipse Fitting by Hyperaccurate Least Squares Yuuki Iwamoto, 1 Prasanna Rangarajan 2 and Kenichi Kanatani
|
|
|
- ひでか とどろき
- 5 years ago
- Views:
Transcription
1 Taubin Ellipse Fitting by Hyperaccurate Least Squares Yuuki Iwamoto, 1 Prasanna Rangarajan 2 and Kenichi Kanatani 1 This paper presents a new method for fitting an ellipse to a point sequence extracted from images. The basic principle is the least squares, minimizing the algebraic distance. Exploiting the fact that the least-squares solution depends on the way the scale is normalized, we analyze the accuracy to high order terms with the scale normalization weight unspecified and determine the weight so that the second order bias is zero. We demonstrate by experiments that our method is superior to the Taubin method, which is also noniterative and known to be highly accurate. Although the highest accuracy is achieved by maximum likelihood, it requires iterations, which may not converge in the presence of large noise. In contrast, our method analytically computes a solution without iterations. 1 Department of Computer Science, Okayama University, Japan 2 Department of Electrical Engineering, Southern Methodist University, U.S.A ,18 17,24,25 22 FS 4 HEIV KCR ,15 2. Ax 2 + 2Bxy + Cy 2 + 2f Dx + Ey + f 2 F = 1 f x, y 1 21,22 x α, y α, α = 1,..., 1 A,..., F 1 1 f = 6 1 c 29 Information Processing Society of Japan
2 J A,..., F J = 1 2 Ax 2 α + 2Bx α y α + Cyα 2 + 2f Dx α + Ey α + f 2 F 2 2 A = = F = F = 1 3 A + C = 1 4 A 2 + B 2 + C 2 + D 2 + E 2 + F 2 = 1 5 A 2 + B 2 + C 2 + D 2 + E 2 = 1 6 A 2 + 2B 2 + C 2 = 1 7 AC B 2 = ,5,2, 4 1 7, u = A B C D E F 9 2 u, u = 1 1 Ax 2 + Bxy + Cy 2 + Dx + Ey + F = 1 A 2 + 4B 2 + C 2 + 4f 2 D2 + E 2 + f 4 F 2 = 2 Ax 2 + Bxy + Cy 2 + Dx + Ey + F = 1 A 2 + 4B 2 + C 2 + 4f 2 D2 + E 2 = 3 4 AC B 2 = AC B 2 < 1 a, b a, b u 6 ξ ξ = x 2 2xy y 2 2f x 2f y f u, ξ = 12 x α, y α ξ ξ α 1 J = 1 u, ξ α 2 = 1 u ξ α ξ α u = u, Mu M M = 1 ξ α ξ α u 2 1 u Mu = λu 15 λ λ u λ Mu λ 15 λ u 6 u u u = u, u u u, u > u, u < 6 u, u > 6 M u, Mu > u u, u < λ < u u, u λ λ 2 c 29 Information Processing Society of Japan
3 u u 5 I 15 Mu = λu 16 M Taubin Taubin 23 TB 22 x 2 1 α x α y α f x α TB = 4 x α y α x 2 α + yα 2 x α y α f y α f x α x α y α yα 2 f y α f B x α f y α f 2 f x α f y α f 2 C A x α, y α x α, ȳ α x α, y α ξ α ξ α = ξ α + 1ξ α + 2ξ α 18 ξ α, 1ξ α, 2ξ α x α, y α x 2 1 α 2 x α ȳ α ȳ ξ α = 2 α, 1 ξ 2f B x α α = 2f ȳ α A f 2 2 x α x α 2 x α y α + 2ȳ α x α 2ȳ α y α 2f x α 2f y α 1, 2 ξ α = C B x 2 α 2 x α y α yα 2 14,15 2 ξ α 2 ξ α V [ξ α ] = E[ 1ξ α 1ξ α ] E[ ] x α, y α σ E[ x α ] = E[ y α ] =, E[ x 2 α] = E[ y 2 α] = σ 2, E[ x α y α ] = V [ξ α ] = E[ 1ξ α 1ξ α ] = σ 2 V [ξ α ] 2 1 C A 19 x 2 1 α x αȳ α f x α x αȳ α x 2 α + ȳα 2 x αȳ α f ȳ α f x α x V [ξ α ] = 4 αȳ α ȳ 2 α f ȳ α f B x α f ȳ α f 2 f x α f ȳ α f 2 C A V [ξ α ] σ 2 V [ξ α ] Taubin TB = 1 V [ξ α ] 22 x α, ȳ α x α, y α M = 1 ξ α + 1 ξ α + 2 ξ α ξ α + 1 ξ α + 2 ξ α = + 1 M + 2 M , 1M, 2M = 1 1M = 1 2 M = 1 ξ α ξ α, 24 ξα 1ξ α + 1ξ α ξ α, 25 ξα 2 ξ α + 1ξ α 1 ξ α + 2ξ α ξ α 16 u, λ u = ū + 1 u + 2 u +, λ = λ + 1 λ + 2 λ , , M + 2 M + ū + 1 u + 2 u + = λ + 1 λ + 2 λ + ū + 1 u + 2 u ū = λ ū 29 1 u + 1 M ū = λ 1 u + 1 λ ū c 29 Information Processing Society of Japan
4 2u + 1M 1u + 2M ū = λ 2u + 1λ 1u + 2λ ū ξ α, ū = 24 ū = 29 λ = 25 ū, 1 M ū = 3 ū 1λ = 3 1 u = 1 M ū 32 ū = ū P ū ū θ+ 1θ+ 2θ + 2 = 1 1 θ, 1 θ = 1 θ θ λ 2λ = ū, 2M ū ū, 1 M 1 M ū ū, T ū = 33 ū, ū ū, ū T = 2M 1M 1M u ū ū 2 u ū 2 u = P ū 2 u = 2 u u = 2λ ū + 1M 1M ū 2M ū ū, T ū = ū, ū ū T ū u V [u] = E[ 1u 1u ] = E[ 1Mu 1Mu ] = 1 [ ] E ξ 2 α, u ξ α ξ β, u ξ β β=1 = 1 u, E[ ξ 2 α ξ β ]u ξ α ξ β 2 u, V [ξ α ]u ξ α ξ α, 37 = 1 ū, V [ξ α ]u ξ α ξ α ξ α α E[ 1 ξ α 1 ξ β ] = δ αβ σ 2 V [ξ α ] δ αβ 37 V [u] Taubin V [u] 1 E[ 1 u] = 33 2 E[ 2u ] 34 T 26 E[ 2 M ] E[ 2 M ] = 1 ξα E[ 2 ξ α ] + E[ 1 ξ α 1 ξ α ] + E[ 2 ξ α ] ξ α ξα e 13 + V [ξ α ] + e 13 ξ α = σ 2 TB + 2S[ ξ c e 13] 2 22 ξ c, e 13 ξ c = 1 ξ α, e 13 = S[ ] S[A] = A + A /2 E[ 1 M 1 M ] E[ 1M 1M ] tr[ V 2 [ξ α ]] ξ α ξ α + ξ α, ξα V [ξ α ] + 2S[V [ξ α ] ξα ξ α ] tr[ ] 39, T E[T ] = σ 2 TB + 2S[ ξ c e 13] S[V [ξ α ] ξα ξ α ] tr[ V [ξ α ]] ξ α ξ α + ξ α, ξα V [ξ α ] 42 4 c 29 Information Processing Society of Japan
5 36 2u E[ 2 u ] = ū, E[T ]ū ū, ū ū E[T ]ū ξ c, ū =, ξ α, ū = E[T ]ū E[T ]ū = σ 2 TB ū + A + C ξ c 1 ξ 2 α, ξα V [ξ α ]ū +ū, V [ξ α ] ξα ξ α = I 2 E[ 2u ] E[ 2u ]= ū,e[t ]ūū E[T ]ū = I ūū E[T ]ū = E[T ]ū 45 I ūū = P ū = = 46 44, 45 2 E[ 2 u ] = σ 2 TB ū + A + C ξ c 1 ξ 2 α, ξα V [ξ α ]ū +ū, V [ξ α ] ξα ξ α 8. Taubin 44 ξ c, ū =, ξ α, ū = ū, E[T ]ū ū, E[T ]ū = σ 2 ū, TBū 1 ξ 2 α, ξα ū, V [ξ α ]ū = σ 2 ū, TB ū 1 2 tr[ ξα ξ α ]ū, V [ξ α ]ū = σ 2 ū, TB ū 1 tr[ ū, V 2 [ξ α ]ū ξ α ξ α ] = σ 2 ū, TB ū σ2 tr[ ] Taubin = TB Taubin 2 E[ 2 u ] = σ 2 q TB ū + A + C ξ c 1 2 +ū, V [ξ α ] ξα ξ α ξ α, ξα V [ξ α ]ū q = 1 tr[ ] 5 ū, TB ū TBū q TBū 5 q < 1 Taubin 9. = TB + 2S[ ξ c e 13] 1 tr[ V [ξ 2 α ]] ξ α ξ α + ξ α, ξα V [ξ α ] +2S[V [ξ α ] ξα ξ α ] 42 E[T ] = σ 2 43 E[ 2 u ] = σ 2 ū, ū ū, ū ū = ξ α, xα, ȳ α x α, y α Oσ 2 1, 2 43 E[ 2u ] Oσ a x, y σ Taubin c 29 Information Processing Society of Japan
6 u u u a b O 1 a 31 b σ = Taubin Chojnacki 4 FS 25 1b σ =.5 u ū u u ū u = P ūu 53 2a P ū I ūū ū 2b, c σ 1 B D B = 1 1 u a, D = 1 a= a=1 u a 2 54 u a a 2c KCR 12,14,15 D KCR = σ ξ ] tr[ α ξ α 55 ū, V [ξ α ]ū Taubin TB u = 1/λMu M 2 λ a b c a u ū u b, c 1a a b σ Taubin c KCR 5 λ λ u 2b Taubin 38 Taubin 2c Taubin 2 2b c 2b, c Taubin Taubin Taubin 6 c 29 Information Processing Society of Japan
7 3 155 Taubin Taubin ,15 2 Taubin : C o A. Albano, Representation of digitized contours in terms of conics and straight-line segments, Comput. Graphics Image Process., , ,,,, 29-CVIM , F. J. Bookstein, Fitting conic sections to scattered data, Comput. Graphics Image Process., , W. Chojnacki, M. J. Brooks, A. van den Hengel and D. Gawley, On the fitting of surfaces to data with covariances, IEEE Trans. Patt. Anal. Mach. Intell., , D. B. Cooper and. Yalabik, On the computational cost of approximating and recognizing noise-perturbed straight lines and quadratic arcs in the plane, IEEE Trans. Computers, , A. Fitzgibbon, M. Pilu and R. B. Fisher, Direct least square fitting of ellipses, IEEE Trans. Patt. Anal. Mach. Intell., , W. Gander, H. Golub, and R. Strebel, Least-squares fitting of circles and ellipses, BIT, , R. Gnanadesikan, Methods for Statistical Data Analysis of Multivariable Observations, Wiley, ew Yori,.Y., U.S.A K. Kanatani, Geometric Computation for Machine Vision, Oxford University Press, Oxford, U.K., ,,, ,,, , K. Kanatani, Statistical Optimization for Geometric Computation: Theory and Practice, Elsevier Science, Amsterdam, The etherlands, 1996; Dover, ew York, , KCR,, 25-CVIM , ,,, 25-CVIM , K. Kanatani, Statistical optimization for geometric fitting: Theoretical accuracy analysis and high order error analysis, Int. J. Comp. Vis , Y. Leedan and P. Meer, Heteroscedastic regression in computer vision: Problems with bilinear constraint, Int. J. Comput. Vision., , ,,,,, 28-CVIM , ,,,, D-II, J85-D-II , K. A. Paton, Conic sections in chromosome analysis, Patt. Recog., , P. L. Rosin, A note on the least squares fitting of ellipses, Patt. Recog. Lett., , [I],, , c 29 Information Processing Society of Japan
8 22 [II],, , G. Taubin, Estimation of planar curves, surfaces, and non-planar space curves defined by implicit equations with applications to edge and range image segmentation, IEEE Trans. Patt. Anal. Mach. Intell., , ,,, 25-CVIM , ,,,, 26-CVIM , E[ 1M 1M ] E[ 1M 1M ] = E[ 1 ξα 1 ξ 1 α + 1 ξ α ξ α = 1 2 = ξ α ξ α = 1 2 ξβ 1 ξ β + 1 ξ β ξ β ] β=1 E[ ξ α 1 ξ α + 1 ξ α ξ α ξ β 1 ξ β + 1 ξ β ξ β ] E[ ξ α 1 ξ ξβ α 1 ξ β + ξ α 1 ξ α 1 ξ β ξ β + 1 ξ ξβ α ξ α 1 ξ β 1 ξ β ξ β ] E[ ξ α 1 ξ α, ξβ 1 ξ β + ξ α 1 ξ α, 1 ξ β ξ β + 1 ξ α ξ α, ξβ 1 ξ β + 1 ξ α ξ α, 1 ξ β ξ β ] = 1 E[ 1ξ 2 α, ξβ ξ α 1ξ β + 1ξ α, 1ξ β ξ α ξ β + ξ α, ξβ 1 ξ α 1 ξ β + 1 ξ α 1 ξ β, ξ α ξ β ] = 1 E[ ξ 2 α ξβ 1 ξ α 1 ξ β + tr[ 1 ξ β 1 ξ α ] ξ α ξ β = ξ α, ξβ 1ξ α 1ξ β + 1ξ α 1ξ ξα β ξ β ] ξα ξ β E[ 1 ξ α 1 ξ β ] + tr[ E[ 1 ξ β 1 ξ α ]] ξ α ξ β + ξ α, ξβ E[ 1 ξ α 1 ξ β ] + E[ 1ξ α 1 ξ β ] ξα ξ β 2 +δ αβ V [ξ α ] 2 2 ξα ξ β ξα ξ α δ αβ V [ξ α ]+tr[ δ αβ V [ξ α ]] ξ α ξ β + ξ α, ξβ δ αβ V [ξ α ] ξα ξ β V [ξ α ] + tr[ V [ξ α ]] ξ α ξ α + ξ α, ξα V [ξ α ] +V [ξ α ] ξα ξ α tr[ V [ξ α ]] ξ α ξ α + ξ α, ξα V [ξ α ] + 2S[V [ξ α ] ξα ξ α ] 57 8 c 29 Information Processing Society of Japan
(a) (b) (c) Canny (d) 1 ( x α, y α ) 3 (x α, y α ) (a) A 2 + B 2 + C 2 + D 2 + E 2 + F 2 = 1 (3) u ξ α u (A, B, C, D, E, F ) (4) ξ α (x 2 α, 2x α y α,
[II] Optimization Computation for 3-D Understanding of Images [II]: Ellipse Fitting 1. (1) 2. (2) (edge detection) (edge) (zero-crossing) Canny (Canny operator) (3) 1(a) [I] [II] [III] [IV ] E-mail sugaya@iim.ics.tut.ac.jp
, ( ξ/) ξ(x), ( ξ/) x = x 1,. ξ ξ ( ξ, u) = 0. M LS ξ ξ (6) u,, u M LS 3).,.. ξ x ξ = ξ(x),, 1. J = (ξ ξ, V [ξ ] 1 (ξ ξ )) (7) ( ξ, u) = 0, = 1,..., N
![, ( ξ/) ξ(x), ( ξ/) x = x 1,. ξ ξ ( ξ, u) = 0. M LS ξ ξ (6) u,, u M LS 3).,.. ξ x ξ = ξ(x),, 1. J = (ξ ξ, V [ξ ] 1 (ξ ξ )) (7) ( ξ, u) = 0, = 1,..., N](/thumbs/80/81723780.jpg ", ( ξ/) ξ(x), ( ξ/) x = x 1,. ξ ξ ( ξ, u) = 0. M LS ξ ξ (6) u,, u M LS 3).,.. ξ x ξ = ξ(x),, 1. J = (ξ ξ, V [ξ ] 1 (ξ ξ )) (7) ( ξ, u) = 0, = 1,..., N")
1,,.,.. Maximum Likelihood Estimation for Geometric Fitting Yasuyuki Sugaya 1 Geometric fitting, the problem which estimates a geometric model of a scene from extracted image data, is one of the most fundamental
Automatic Detection of Circular Objects by Ellipse Growing Mitsuo OKABE, Kenichi KANATANI, and Naoya OHTA 1. [4], [5], [18], [19] [14], [17] [28], [32
![Automatic Detection of Circular Objects by Ellipse Growing Mitsuo OKABE, Kenichi KANATANI, and Naoya OHTA 1. [4], [5], [18], [19] [14], [17] [28], [32](/thumbs/101/148861312.jpg "Automatic Detection of Circular Objects by Ellipse Growing Mitsuo OKABE, Kenichi KANATANI, and Naoya OHTA 1. [4], [5], [18], [19] [14], [17] [28], [32")
Automatic Detection of Circular Objects by Ellipse Growing Mitsuo OKABE, Kenichi KANATANI, and Naoya OHTA 1. [4], [5], [18], [19] [14], [17] [28], [32] [6], [21], [27] Fujitsu Cadtech, Ltd., Oyama-shi,
Vol.-CVIM-7 No.7 /3/8 NLPCA kernel PCA KPCA 4),) NLPCA KPCA NLPCA KPCA principle curve principle surface KPCA ) ),),6),8),),3) ) Jacobian KPCA PCA ) P
Vol.-CVIM-7 No.7 /3/8 RANSAC M Subspace fitting via robust Jacobian kernel PCA Jun Fujiki and Shotaro Akaho The subspace fitting method based on the original kernel principle component analysis (PCA),
IPSJ SIG Technical Report Vol.2009-CVIM-168 No /8/ (2003) Costeira Kanade (1998) AIC Vidal (2005) GPCA Taubin 3 2 EM Multi-stage Opt
() osteira Kanade (998) AI Vidal (5) GPA Taubin EM Multi-stage Optimization of Multi-body Motion Segmentation Using Generalized Principal omponent Analysis Shinya Tanaka, Yuuki Tanaka, Hirotaka Hara, Yasuyuki
IPSJ SIG Technical Report Vol.2009-CVIM-168 No /9/ Latest Algorithm for 3-D Reconstruction from Two Views Kento Yamada, 1 Yasu
2 3 2 3 2 3 Latest Algorithm for 3-D Reconstruction from Two Views Kento Yamada, Yasushi Kanazawa, Kenichi Kanatani 2 and Yasuyuki Sugaya 3 This paper presents a new algorithm for reconstructing the 3-D
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.
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
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
![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](/thumbs/92/110416683.jpg "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")
x-means 1 2 2 x-means, x-means k-means Bayesian Information Criterion BIC Watershed x-means Moving Object Extraction Using the Number of Clusters Determined by X-means Clustering Naoki Kubo, 1 Kousuke
A Feasibility Study of Direct-Mapping-Type Parallel Processing Method to Solve Linear Equations in Load Flow Calculations Hiroaki Inayoshi, Non-member
A Feasibility Study of Direct-Mapping-Type Parallel Processing Method to Solve Linear Equations in Load Flow Calculations Hiroaki Inayoshi, Non-member (University of Tsukuba), Yasuharu Ohsawa, Member (Kobe
E = N M α= = [( pα I α x ) 2 ( α qα + y ) 2 ] α r α r α I α α p α = P X α + P 2 Y α + P 3 Z α + P 4, q α = P 2 X α + P 22 Y α + P 23 Z α + P 24 r α =
![E = N M α= = [( pα I α x ) 2 ( α qα + y ) 2 ] α r α r α I α α p α = P X α + P 2 Y α + P 3 Z α + P 4, q α = P 2 X α + P 22 Y α + P 23 Z α + P 24 r α =](/thumbs/91/105166806.jpg "E = N M α= = [( pα I α x ) 2 ( α qα + y ) 2 ] α r α r α I α α p α = P X α + P 2 Y α + P 3 Z α + P 4, q α = P 2 X α + P 22 Y α + P 23 Z α + P 24 r α =")
3 2 3 2 3 undle Adjustment or 3-D Reconstruction: Implementation and Evaluation Yuuki Iwamoto, Yasuyuki Sugaya 2 and Kenichi Kanatani We describe in detail the algorithm o bundle adjustment or 3-D reconstruction
Fig. 3 Flow diagram of image processing. Black rectangle in the photo indicates the processing area (128 x 32 pixels).
Fig. 1 The scheme of glottal area as a function of time Fig. 3 Flow diagram of image processing. Black rectangle in the photo indicates the processing area (128 x 32 pixels). Fig, 4 Parametric representation
Optical Flow t t + δt 1 Motion Field 3 3 1) 2) 3) Lucas-Kanade 4) 1 t (x, y) I(x, y, t)
http://wwwieice-hbkborg/ 2 2 4 2 -- 2 4 2010 9 3 3 4-1 Lucas-Kanade 4-2 Mean Shift 3 4-3 2 c 2013 1/(18) http://wwwieice-hbkborg/ 2 2 4 2 -- 2 -- 4 4--1 2010 9 4--1--1 Optical Flow t t + δt 1 Motion Field
> > <., vs. > x 2 x y = ax 2 + bx + c y = 0 2 ax 2 + bx + c = 0 y = 0 x ( x ) y = ax 2 + bx + c D = b 2 4ac (1) D > 0 x (2) D = 0 x (3
13 2 13.0 2 ( ) ( ) 2 13.1 ( ) ax 2 + bx + c > 0 ( a, b, c ) ( ) 275 > > 2 2 13.3 x 2 x y = ax 2 + bx + c y = 0 2 ax 2 + bx + c = 0 y = 0 x ( x ) y = ax 2 + bx + c D = b 2 4ac (1) D >
IPSJ SIG Technical Report 1, Instrument Separation in Reverberant Environments Using Crystal Microphone Arrays Nobutaka ITO, 1, 2 Yu KITANO, 1
1, 2 1 1 1 Instrument Separation in Reverberant Environments Using Crystal Microphone Arrays Nobutaka ITO, 1, 2 Yu KITANO, 1 Nobutaka ONO 1 and Shigeki SAGAYAMA 1 This paper deals with instrument separation
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
untitled
yoshi@image.med.osaka-u.ac.jp http://www.image.med.osaka-u.ac.jp/member/yoshi/ II Excel, Mathematica Mathematica Osaka Electro-Communication University (2007 Apr) 09849-31503-64015-30704-18799-390 http://www.image.med.osaka-u.ac.jp/member/yoshi/
a) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a
a) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a), Tetsuo SAWARAGI, and Yukio HORIGUCHI 1. Johansson
IPSJ SIG Technical Report Pitman-Yor 1 1 Pitman-Yor n-gram A proposal of the melody generation method using hierarchical pitman-yor language model Aki
Pitman-Yor Pitman-Yor n-gram A proposal of the melody generation method using hierarchical pitman-yor language model Akira Shirai and Tadahiro Taniguchi Although a lot of melody generation method has been
1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2
CHLAC 1 2 3 3,. (CHLAC), 1).,.,, CHLAC,.,. Suspicious Behavior Detection based on CHLAC Method Hideaki Imanishi, 1 Toyohiro Hayashi, 2 Shuichi Enokida 3 and Toshiaki Ejima 3 We have proposed a method for
Developement of Plastic Collocation Method Extension of Plastic Node Method by Yukio Ueda, Member Masahiko Fujikubo, Member Masahiro Miura, Member Sum
Developement of Plastic Collocation Method Extension of Plastic Node Method by Yukio Ueda, Member Masahiko Fujikubo, Member Masahiro Miura, Member Summary Previously, the authors developed the plastic
<95DB8C9288E397C389C88A E696E6462>
2011 Vol.60 No.2 p.138 147 Performance of the Japanese long-term care benefit: An International comparison based on OECD health data Mie MORIKAWA[1] Takako TSUTSUI[2] [1]National Institute of Public Health,
(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
橡表紙参照.PDF
CIRJE-J-58 X-12-ARIMA 2000 : 2001 6 How to use X-12-ARIMA2000 when you must: A Case Study of Hojinkigyo-Tokei Naoto Kunitomo Faculty of Economics, The University of Tokyo Abstract: We illustrate how to
1 Web [2] Web [3] [4] [5], [6] [7] [8] S.W. [9] 3. MeetingShelf Web MeetingShelf MeetingShelf (1) (2) (3) (4) (5) Web MeetingShelf
![1 Web [2] Web [3] [4] [5], [6] [7] [8] S.W. [9] 3. MeetingShelf Web MeetingShelf MeetingShelf (1) (2) (3) (4) (5) Web MeetingShelf](/thumbs/79/80104027.jpg "1 Web [2] Web [3] [4] [5], [6] [7] [8] S.W. [9] 3. MeetingShelf Web MeetingShelf MeetingShelf (1) (2) (3) (4) (5) Web MeetingShelf")
1,a) 2,b) 4,c) 3,d) 4,e) Web A Review Supporting System for Whiteboard Logging Movies Based on Notes Timeline Taniguchi Yoshihide 1,a) Horiguchi Satoshi 2,b) Inoue Akifumi 4,c) Igaki Hiroshi 3,d) Hoshi
(1970) 17) V. Kucera: A Contribution to Matrix Ouadratic Equations, IEEE Trans. on Automatic Control, AC- 17-3, 344/347 (1972) 18) V. Kucera: On Nonnegative Definite Solutions to Matrix Ouadratic Equations,
& 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
(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
![(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
Vol.-ICS-6 No.3 /3/8 Input.8.6 y.4 Fig....5 receptive field x 3 w x y Machband w(x =
DOG(Difference of two Gaussians 8 A feedback model for the brightness illusion Shoji Nodasaka and Asaki Saito We consider mechanism of the Hermann grid. The mechanism is usually explained by effects of
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 +")
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
JFE.dvi
,, Department of Civil Engineering, Chuo University Kasuga 1-13-27, Bunkyo-ku, Tokyo 112 8551, JAPAN E-mail : atsu1005@kc.chuo-u.ac.jp E-mail : kawa@civil.chuo-u.ac.jp SATO KOGYO CO., LTD. 12-20, Nihonbashi-Honcho
2_05.dvi
Vol. 52 No. 2 901 909 (Feb. 2011) Gradient-Domain Image Editing is a useful technique to do various-type image editing, for example, Poisson Image Editing which can do seamless image composition. This
TCP/IP IEEE Bluetooth LAN TCP TCP BEC FEC M T M R M T 2. 2 [5] AODV [4]DSR [3] 1 MS 100m 5 /100m 2 MD 2 c 2009 Information Processing Society of
![TCP/IP IEEE Bluetooth LAN TCP TCP BEC FEC M T M R M T 2. 2 [5] AODV [4]DSR [3] 1 MS 100m 5 /100m 2 MD 2 c 2009 Information Processing Society of](/thumbs/86/93291802.jpg "TCP/IP IEEE Bluetooth LAN TCP TCP BEC FEC M T M R M T 2. 2 [5] AODV [4]DSR [3] 1 MS 100m 5 /100m 2 MD 2 c 2009 Information Processing Society of")
IEEE802.11 [1]Bluetooth [2] 1 1 (1) [6] Ack (Ack) BEC FEC (BEC) BEC FEC 100 20 BEC FEC 6.19% 14.1% High Throughput and Highly Reliable Transmission in MANET Masaaki Kosugi 1 and Hiroaki Higaki 1 1. LAN
JKR Point loading of an elastic half-space 2 3 Pressure applied to a circular region Boussinesq, n =
JKR 17 9 15 1 Point loading of an elastic half-space Pressure applied to a circular region 4.1 Boussinesq, n = 1.............................. 4. Hertz, n = 1.................................. 6 4 Hertz
浜松医科大学紀要
On the Statistical Bias Found in the Horse Racing Data (1) Akio NODA Mathematics Abstract: The purpose of the present paper is to report what type of statistical bias the author has found in the horse
19 Systematization of Problem Solving Strategy in High School Mathematics for Improving Metacognitive Ability
19 Systematization of Problem Solving Strategy in High School Mathematics for Improving Metacognitive Ability 1105402 2008 2 4 2,, i Abstract Systematization of Problem Solving Strategy in High School
11) 13) 11),12) 13) Y c Z c Image plane Y m iy O m Z m Marker coordinate system T, d X m f O c X c Camera coordinate system 1 Coordinates and problem
1 1 1 Posture Esimation by Using 2-D Fourier Transform Yuya Ono, 1 Yoshio Iwai 1 and Hiroshi Ishiguro 1 Recently, research fields of augmented reality and robot navigation are actively investigated. Estimating
IPSJ SIG Technical Report An Evaluation Method for the Degree of Strain of an Action Scene Mao Kuroda, 1 Takeshi Takai 1 and Takashi Matsuyama 1
1 1 1 An Evaluation Method for the Degree of of an Action Scene Mao Kuroda, 1 Takeshi Takai 1 and Takashi Matsuyama 1 The purpose of our research is to investigate structure of an action scene scientifically.
,,.,.,,.,.,.,.,,.,..,,,, i
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
2. Eades 1) Kamada-Kawai 7) Fruchterman 2) 6) ACE 8) HDE 9) Kruskal MDS 13) 11) Kruskal AGI Active Graph Interface 3) Kruskal 5) Kruskal 4) 3. Kruskal
1 2 3 A projection-based method for interactive 3D visualization of complex graphs Masanori Takami, 1 Hiroshi Hosobe 2 and Ken Wakita 3 Proposed is a new interaction technique to manipulate graph layouts
Input image Initialize variables Loop for period of oscillation Update height map Make shade image Change property of image Output image Change time L
1,a) 1,b) 1/f β Generation Method of Animation from Pictures with Natural Flicker Abstract: Some methods to create animation automatically from one picture have been proposed. There is a method that gives
Vol1-CVIM-172 No.7 21/5/ Shan 1) 2 2)3) Yuan 4) Ancuti 5) Agrawal 6) 2.4 Ben-Ezra 7)8) Raskar 9) Image domain Blur image l PSF b / = F(
Vol1-CVIM-172 No.7 21/5/27 1 Proposal on Ringing Detector for Image Restoration Chika Inoshita, Yasuhiro Mukaigawa and Yasushi Yagi 1 A lot of methods have been proposed for restoring blurred images due
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 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
1 1 Pixel 0 n 1 n=8 56 R G B RGB M RGB (1) M = 0.99R G B (1) () 4 π d 4 B = L cos φ () 4 ID B L d ID φ d / ID F R φ (3) R
I-07 404 137, Email:dsusuki@ipc.shizuoka.ac.jp When preparing the manuscript, read and observe carefully this sample as well as the instruction manual for the manuscript (1) OS of the Transaction of Japan
IPSJ SIG Technical Report Vol.2010-CVIM-170 No /1/ Visual Recognition of Wire Harnesses for Automated Wiring Masaki Yoneda, 1 Ta
1 1 1 1 2 1. Visual Recognition of Wire Harnesses for Automated Wiring Masaki Yoneda, 1 Takayuki Okatani 1 and Koichiro Deguchi 1 This paper presents a method for recognizing the pose of a wire harness
2 (March 13, 2010) N Λ a = i,j=1 x i ( d (a) i,j x j ), Λ h = N i,j=1 x i ( d (h) i,j x j ) B a B h B a = N i,j=1 ν i d (a) i,j, B h = x j N i,j=1 ν i
1. A. M. Turing [18] 60 Turing A. Gierer H. Meinhardt [1] : (GM) ) a t = D a a xx µa + ρ (c a2 h + ρ 0 (0 < x < l, t > 0) h t = D h h xx νh + c ρ a 2 (0 < x < l, t > 0) a x = h x = 0 (x = 0, l) a = a(x,
A Nutritional Study of Anemia in Pregnancy Hematologic Characteristics in Pregnancy (Part 1) Keizo Shiraki, Fumiko Hisaoka Department of Nutrition, Sc
A Nutritional Study of Anemia in Pregnancy Hematologic Characteristics in Pregnancy (Part 1) Keizo Shiraki, Fumiko Hisaoka Department of Nutrition, School of Medicine, Tokushima University, Tokushima Fetal
24 Depth scaling of binocular stereopsis by observer s own movements
24 Depth scaling of binocular stereopsis by observer s own movements 1130313 2013 3 1 3D 3D 3D 2 2 i Abstract Depth scaling of binocular stereopsis by observer s own movements It will become more usual
IPSJ SIG Technical Report Vol.2012-CG-148 No /8/29 3DCG 1,a) On rigid body animation taking into account the 3D computer graphics came
3DCG 1,a) 2 2 2 2 3 On rigid body animation taking into account the 3D computer graphics camera viewpoint Abstract: In using computer graphics for making games or motion pictures, physics simulation is
Q [4] 2. [3] [5] ϵ- Q Q CO CO [4] Q Q [1] i = X ln n i + C (1) n i i n n i i i n i = n X i i C exploration exploitation [4] Q Q Q ϵ 1 ϵ 3. [3] [5] [4]
![Q [4] 2. [3] [5] ϵ- Q Q CO CO [4] Q Q [1] i = X ln n i + C (1) n i i n n i i i n i = n X i i C exploration exploitation [4] Q Q Q ϵ 1 ϵ 3. [3] [5] [4]](/thumbs/93/112440494.jpg "Q [4] 2. [3] [5] ϵ- Q Q CO CO [4] Q Q [1] i = X ln n i + C (1) n i i n n i i i n i = n X i i C exploration exploitation [4] Q Q Q ϵ 1 ϵ 3. [3] [5] [4]")
1,a) 2,3,b) Q ϵ- 3 4 Q greedy 3 ϵ- 4 ϵ- Comparation of Methods for Choosing Actions in Werewolf Game Agents Tianhe Wang 1,a) Tomoyuki Kaneko 2,3,b) Abstract: Werewolf, also known as Mafia, is a kind of
IPSJ SIG Technical Report Vol.2017-ARC-225 No.12 Vol.2017-SLDM-179 No.12 Vol.2017-EMB-44 No /3/9 1 1 RTOS DefensiveZone DefensiveZone MPU RTOS
1 1 RTOS DefensiveZone DefensiveZone MPU RTOS RTOS OS Lightweight partitioning architecture for automotive systems Suzuki Takehito 1 Honda Shinya 1 Abstract: Partitioning using protection RTOS has high
2 1,2, , 2 ( ) (1) (2) (3) (4) Cameron and Trivedi(1998) , (1987) (1982) Agresti(2003)
3 1 1 1 2 1 2 1,2,3 1 0 50 3000, 2 ( ) 1 3 1 0 4 3 (1) (2) (3) (4) 1 1 1 2 3 Cameron and Trivedi(1998) 4 1974, (1987) (1982) Agresti(2003) 3 (1)-(4) AAA, AA+,A (1) (2) (3) (4) (5) (1)-(5) 1 2 5 3 5 (DI)
IPSJ SIG Technical Report Vol.2014-DPS-158 No.27 Vol.2014-CSEC-64 No /3/6 1,a) 2,b) 3,c) 1,d) 3 Cappelli Bazen Cappelli Bazen Cappelli 1.,,.,.,
1,a),b) 3,c) 1,d) 3 Cappelli Bazen Cappelli Bazen Cappelli 1.,,,,,.,,,,.,,.,,,,.,, 1 Department of Electrical Electronic and Communication Engineering Faculty of Science and Engineering Chuo University
J No J. J
教育科学と教育実践 2 Science of Education and Educational Practice - A Perspective on the Controversy on the Science of Education in Post-War Japan Part Takeo TANAKA 1950 E. J. E. J. E. J. Abstract In the latter
untitled
(Robot Vision) Vision ( (computer) Machine VisionComputer Vision ( ) ( ) ( ) ( ) ( ) 1 DTV 2 DTV D 3 ( ( ( ( ( DTV D 4 () 5 A B C D E F G H I A B C D E F G H I I = A + D + G - C - F - I J = A + B + C -
A Study on Throw Simulation for Baseball Pitching Machine with Rollers and Its Optimization Shinobu SAKAI*5, Yuichiro KITAGAWA, Ryo KANAI and Juhachi
A Study on Throw Simulation for Baseball Pitching Machine with Rollers and Its Optimization Shinobu SAKAI*5, Yuichiro KITAGAWA, Ryo KANAI and Juhachi ODA Department of Human and Mechanical Systems Engineering,
17 Proposal of an Algorithm of Image Extraction and Research on Improvement of a Man-machine Interface of Food Intake Measuring System
1. (1) ( MMI ) 2. 3. MMI Personal Computer(PC) MMI PC 1 1 2 (%) (%) 100.0 95.2 100.0 80.1 2 % 31.3% 2 PC (3 ) (2) MMI 2 ( ),,,, 49,,p531-532,2005 ( ),,,,,2005,p66-p67,2005 17 Proposal of an Algorithm of
IPSJ SIG Technical Report GPS LAN GPS LAN GPS LAN Location Identification by sphere image and hybrid sensing Takayuki Katahira, 1 Yoshio Iwai 1
1 1 1 GPS LAN GPS LAN GPS LAN Location Identification by sphere image and hybrid sensing Takayuki Katahira, 1 Yoshio Iwai 1 and Hiroshi Ishiguro 1 Self-location is very informative for wearable systems.
Vol. 44 No. SIG 9(CVIM 7) ) 2) 1) 1 2) 3 7) 1) 2) 3 3) 4) 5) (a) (d) (g) (b) (e) (h) No Convergence? End (f) (c) Yes * ** * ** 1
Vol. 44 No. SIG 9(CVIM 7) July 2003, Robby T. Tan, 1 Estimating Illumination Position, Color and Surface Reflectance Properties from a Single Image Kenji Hara,, Robby T. Tan, Ko Nishino, Atsushi Nakazawa,
カルマンフィルターによるベータ推定（ ）
β TOPIX 1 22 β β smoothness priors (the Capital Asset Pricing Model, CAPM) CAPM 1 β β β β smoothness priors :,,. E-mail: koiti@ism.ac.jp., 104 1 TOPIX β Z i = β i Z m + α i (1) Z i Z m α i α i β i (the
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. E-mail: {ytamura,takai,tkato,tm}@vision.kuee.kyoto-u.ac.jp Abstract Current Wave Pattern Analysis for Anomaly
yasi10.dvi
2002 50 2 259 278 c 2002 1 2 2002 2 14 2002 6 17 73 PML 1. 1997 1998 Swiss Re 2001 Canabarro et al. 1998 2001 1 : 651 0073 1 5 1 IHD 3 2 110 0015 3 3 3 260 50 2 2002, 2. 1 1 2 10 1 1. 261 1. 3. 3.1 2 1
6 2 2 x y x y t P P = P t P = I P P P ( ) ( ) ,, ( ) ( ) cos θ sin θ cos θ sin θ, sin θ cos θ sin θ cos θ y x θ x θ P
6 x x 6.1 t P P = P t P = I P P P 1 0 1 0,, 0 1 0 1 cos θ sin θ cos θ sin θ, sin θ cos θ sin θ cos θ x θ x θ P x P x, P ) = t P x)p ) = t x t P P ) = t x = x, ) 6.1) x = Figure 6.1 Px = x, P=, θ = θ P
untitled
17 5 13 1 2 1.1... 2 1.2... 2 1.3... 3 2 3 2.1... 3 2.2... 5 3 6 3.1... 6 3.2... 7 3.3 t... 7 3.4 BC a... 9 3.5... 10 4 11 1 1 θ n ˆθ. ˆθ, ˆθ, ˆθ.,, ˆθ.,.,,,. 1.1 ˆθ σ 2 = E(ˆθ E ˆθ) 2 b = E(ˆθ θ). Y 1,,Y
II
II 16 16.0 2 1 15 x α 16 x n 1 17 (x α) 2 16.1 16.1.1 2 x P (x) P (x) = 3x 3 4x + 4 369 Q(x) = x 4 ax + b ( ) 1 P (x) x Q(x) x P (x) x P (x) x = a P (a) P (x) = x 3 7x + 4 P (2) = 2 3 7 2 + 4 = 8 14 +
& 3 3 ' ' (., (Pixel), (Light Intensity) (Random Variable). (Joint Probability). V., V = {,,, V }. i x i x = (x, x,, x V ) T. x i i (State Variable),
.... Deeping and Expansion of Large-Scale Random Fields and Probabilistic Image Processing Kazuyuki Tanaka The mathematical frameworks of probabilistic image processing are formulated by means of Markov
ii 3.,. 4. F. ( ), ,,. 8.,. 1. (75% ) (25% ) =7 24, =7 25, =7 26 (. ). 1.,, ( ). 3.,...,.,.,.,.,. ( ) (1 2 )., ( ), 0., 1., 0,.
(1 C205) 4 10 (2 C206) 4 11 (2 B202) 4 12 25(2013) http://www.math.is.tohoku.ac.jp/~obata,.,,,..,,. 1. 2. 3. 4. 5. 6. 7. 8. 1., 2007 ( ).,. 2. P. G., 1995. 3. J. C., 1988. 1... 2.,,. ii 3.,. 4. F. ( ),..
23 Fig. 2: hwmodulev2 3. Reconfigurable HPC 3.1 hw/sw hw/sw hw/sw FPGA PC FPGA PC FPGA HPC FPGA FPGA hw/sw hw/sw hw- Module FPGA hwmodule hw/sw FPGA h
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),
13 0 1 1 4 11 4 12 5 13 6 2 10 21 10 22 14 3 20 31 20 32 25 33 28 4 31 41 32 42 34 43 38 5 41 51 41 52 43 53 54 6 57 61 57 62 60 70 0 Gauss a, b, c x, y f(x, y) = ax 2 + bxy + cy 2 = x y a b/2 b/2 c x
fiš„v8.dvi
(2001) 49 2 333 343 Java Jasp 1 2 3 4 2001 4 13 2001 9 17 Java Jasp (JAva based Statistical Processor) Jasp Jasp. Java. 1. Jasp CPU 1 106 8569 4 6 7; fuji@ism.ac.jp 2 106 8569 4 6 7; nakanoj@ism.ac.jp
Vol. 29, No. 2, (2008) FDR Introduction of FDR and Comparisons of Multiple Testing Procedures that Control It Shin-ichi Matsuda Department of
Vol. 29, No. 2, 125 139 (2008) FDR Introduction of FDR and Comparisons of Multiple Testing Procedures that Control It Shin-ichi Matsuda Department of Information Systems and Mathematical Sciences, Faculty
,.,. NP,., ,.,,.,.,,, (PCA)...,,. Tipping and Bishop (1999) PCA. (PPCA)., (Ilin and Raiko, 2010). PPCA EM., , tatsukaw
,.,. NP,.,. 1 1.1.,.,,.,.,,,. 2. 1.1.1 (PCA)...,,. Tipping and Bishop (1999) PCA. (PPCA)., (Ilin and Raiko, 2010). PPCA EM., 152-8552 2-12-1, tatsukawa.m.aa@m.titech.ac.jp, 190-8562 10-3, mirai@ism.ac.jp
23_02.dvi
Vol. 2 No. 2 10 21 (Mar. 2009) 1 1 1 Effect of Overconfidencial Investor to Stock Market Behaviour Ryota Inaishi, 1 Fei Zhai 1 and Eisuke Kita 1 Recently, the behavioral finance theory has been interested
[2] 1. 2. 2 2. 1, [3] 2. 2 [4] 2. 3 BABOK BABOK(Business Analysis Body of Knowledge) BABOK IIBA(International Institute of Business Analysis) BABOK 7
![[2] 1. 2. 2 2. 1, [3] 2. 2 [4] 2. 3 BABOK BABOK(Business Analysis Body of Knowledge) BABOK IIBA(International Institute of Business Analysis) BABOK 7](/thumbs/40/22240274.jpg "[2] 1. 2. 2 2. 1, [3] 2. 2 [4] 2. 3 BABOK BABOK(Business Analysis Body of Knowledge) BABOK IIBA(International Institute of Business Analysis) BABOK 7")
32 (2015 ) [2] Projects of the short term increase at present. In order to let projects complete without rework and delays, it is important that request for proposals (RFP) are written by reflecting precisely
‰gficŒõ/’ÓŠ¹
The relationship between creativity of Haiku and idea search space YOSHIDA Yasushi This research examined the relationship between experts' ranking of creative Haiku (a Japanese character poem including
(MIRU2008) HOG Histograms of Oriented Gradients (HOG)
(MIRU2008) 2008 7 HOG - - E-mail: katsu0920@me.cs.scitec.kobe-u.ac.jp, {takigu,ariki}@kobe-u.ac.jp Histograms of Oriented Gradients (HOG) HOG Shape Contexts HOG 5.5 Histograms of Oriented Gradients D Human
, 3, STUDY ON IMPORTANCE OF OPTIMIZED GRID STRUCTURE IN GENERAL COORDINATE SYSTEM 1 2 Hiroyasu YASUDA and Tsuyoshi HOSHINO
, 3, 2012 9 STUDY ON IMPORTANCE OF OPTIMIZED GRID STRUCTURE IN GENERAL COORDINATE SYSTEM 1 2 Hiroyasu YASUDA and Tsuyoshi HOSHINO 1 950-2181 2 8050 2 950-2181 2 8050 Numerical computation of river flows
IA 2013 : :10722 : 2 : :2 :761 :1 (23-27) : : ( / ) (1 /, ) / e.g. (Taylar ) e x = 1 + x + x xn n! +... sin x = x x3 6 + x5 x2n+1 + (
IA 2013 : :10722 : 2 : :2 :761 :1 23-27) : : 1 1.1 / ) 1 /, ) / e.g. Taylar ) e x = 1 + x + x2 2 +... + xn n! +... sin x = x x3 6 + x5 x2n+1 + 1)n 5! 2n + 1)! 2 2.1 = 1 e.g. 0 = 0.00..., π = 3.14..., 1
特集_03-07.Q3C
3-7 Error Detection and Authentication in Quantum Key Distribution YAMAMURA Akihiro and ISHIZUKA Hirokazu Detecting errors in a raw key and authenticating a private key are crucial for quantum key distribution
2 T ax 2 + 2bxy + cy 2 + dx + ey + f = 0 a + b + c > 0 a, b, c A xy ( ) ( ) ( ) ( ) u = u 0 + a cos θ, v = v 0 + b sin θ 0 θ 2π u = u 0 ± a
2 T140073 1 2 ax 2 + 2bxy + cy 2 + dx + ey + f = 0 a + b + c > 0 a, b, c A xy u = u 0 + a cos θ, v = v 0 + b sin θ 0 θ 2π u = u 0 ± a cos θ, v = v 0 + b tan θ π 2 < θ < π 2 u = u 0 + 2pt, v = v 0 + pt
2.2 6).,.,.,. Yang, 7).,,.,,. 2.3 SIFT SIFT (Scale-Invariant Feature Transform) 8).,. SIFT,,. SIFT, Mean-Shift 9)., SIFT,., SIFT,. 3.,.,,,,,.,,,., 1,
1 1 2,,.,.,,, SIFT.,,. Pitching Motion Analysis Using Image Processing Shinya Kasahara, 1 Issei Fujishiro 1 and Yoshio Ohno 2 At present, analysis of pitching motion from baseball videos is timeconsuming
9_18.dvi
Vol. 49 No. 9 3180 3190 (Sep. 2008) 1, 2 3 1 1 1, 2 4 5 6 1 MRC 1 23 MRC Development and Applications of Multiple Risk Communicator Ryoichi Sasaki, 1, 2 Yuu Hidaka, 3 Takashi Moriya, 1 Katsuhiro Taniyama,
A5 PDF.pwd
Average Treatment Effect; ATE attributes Randomized Factorial Survey Experiment; RFSE cues ATE ATE Hainmueller et al. 2014 Average Marginal Component Effect ATE 67 4 2017 2 845 , ;, ATE, ;, ;, W 846 67
Bulletin of JSSAC(2014) Vol. 20, No. 2, pp (Received 2013/11/27 Revised 2014/3/27 Accepted 2014/5/26) It is known that some of number puzzles ca
Bulletin of JSSAC(2014) Vol. 20, No. 2, pp. 3-22 (Received 2013/11/27 Revised 2014/3/27 Accepted 2014/5/26) It is known that some of number puzzles can be solved by using Gröbner bases. In this paper,
ver.1 / c /(13)
1 -- 11 1 c 2010 1/(13) 1 -- 11 -- 1 1--1 1--1--1 2009 3 t R x R n 1 ẋ = f(t, x) f = ( f 1,, f n ) f x(t) = ϕ(x 0, t) x(0) = x 0 n f f t 1--1--2 2009 3 q = (q 1,..., q m ), p = (p 1,..., p m ) x = (q,
1_26.dvi
C3PV 1,a) 2,b) 2,c) 3,d) 1,e) 2012 4 20, 2012 10 10 C3PV C3PV C3PV 1 Java C3PV 45 38 84% Programming Process Visualization for Supporting Students in Programming Exercise Hiroshi Igaki 1,a) Shun Saito
dvi
2017 65 2 185 200 2017 1 2 2016 12 28 2017 5 17 5 24 PITCHf/x PITCHf/x PITCHf/x MLB 2014 PITCHf/x 1. 1 223 8522 3 14 1 2 223 8522 3 14 1 186 65 2 2017 PITCHf/x 1.1 PITCHf/x PITCHf/x SPORTVISION MLB 30
平成 19 年度 ( 第 29 回 ) 数学入門公開講座テキスト ( 京都大学数理解析研究所, 平成 19 ~8 年月 72 月日開催 30 日 ) 1 PCF (Programming language for Computable Functions) PCF adequacy adequacy
1 PCF (Programming language for Computable Functions) PCF adequacy adequacy 2 N X Y X Y f (x) f x f x y z (( f x) y) z = (( f (x))(y))(z) X Y x e X Y λx. e x x 2 + x + 1 λx. x 2 + x + 1 3 PCF 3.1 PCF PCF
14 2 5
14 2 5 i ii Surface Reconstruction from Point Cloud of Human Body in Arbitrary Postures Isao MORO Abstract We propose a method for surface reconstruction from point cloud of human body in arbitrary postures.
No. 3 Oct The person to the left of the stool carried the traffic-cone towards the trash-can. α α β α α β α α β α Track2 Track3 Track1 Track0 1
ACL2013 TACL 1 ACL2013 Grounded Language Learning from Video Described with Sentences (Yu and Siskind 2013) TACL Transactions of the Association for Computational Linguistics What Makes Writing Great?
Fig. 1 The district names and their locations A dotted line is the boundary of school-districts. The district in which 10 respondents and over live is indicated in italics. Fig. 2 A distribution of rank
L P y P y + ɛ, ɛ y P y I P y,, y P y + I P y, 3 ŷ β 0 β y β 0 β y β β 0, β y x x, x,, x, y y, y,, y x x y y x x, y y, x x y y {}}{,,, / / L P / / y, P
005 5 6 y β + ɛ {x, x,, x p } y, {x, x,, x p }, β, ɛ E ɛ 0 V ɛ σ I 3 rak p 4 ɛ i N 0, σ ɛ ɛ y β y β y y β y + β β, ɛ β y + β 0, β y β y ɛ ɛ β ɛ y β mi L y y ŷ β y β y β β L P y P y + ɛ, ɛ y P y I P y,,
202
201 Presenteeism 202 203 204 Table 1. Name Elements of Work Productivity Targeted Populations Measurement items of Presenteeism (Number of Items) Reliability Validity α α 205 α ä 206 Table 2. Factors of
1) K. J. Laidler, "Reaction Kinetics", Vol. II, Pergamon Press, New York (1963) Chap. 1 ; P. G. Ashmore, "Catalysis and Inhibition of Chemical Reactio
1) K. J. Laidler, "Reaction Kinetics", Vol. II, Pergamon Press, New York (1963) Chap. 1 ; P. G. Ashmore, "Catalysis and Inhibition of Chemical Reactions", Butterworths, London (1963) Chap. 7, p. 185. 2)
koji07-01.dvi
2007 I II III 1, 2, 3, 4, 5, 6, 7 5 10 19 (!) 1938 70 21? 1 1 2 1 2 2 1! 4, 5 1? 50 1 2 1 1 2 2 1?? 2 1 1, 2 1, 2 1, 2, 3,... 3 1, 2 1, 3? 2 1 3 1 2 1 1, 2 2, 3? 2 1 3 2 3 2 k,l m, n k,l m, n kn > ml...?
EQUIVALENT TRANSFORMATION TECHNIQUE FOR ISLANDING DETECTION METHODS OF SYNCHRONOUS GENERATOR -REACTIVE POWER PERTURBATION METHODS USING AVR OR SVC- Ju
EQUIVALENT TRANSFORMATION TECHNIQUE FOR ISLANDING DETECTION METHODS OF SYNCHRONOUS GENERATOR -REACTIVE POWER PERTURBATION METHODS USING AVR OR SVC- Jun Motohashi, Member, Takashi Ichinose, Member (Tokyo
untitled
On a Correlation-based Scheme of Digital Watermarking for Images Exploiting 2-D LOT Hiromu KODA and Kyousuke KAMINUSI Abstract In this paper, we propose a correlation based scheme of digital watermarking
Part y mx + n mt + n m 1 mt n + n t m 2 t + mn 0 t m 0 n 18 y n n a 7 3 ; x α α 1 7α +t t 3 4α + 3t t x α x α y mx + n
Part2 47 Example 161 93 1 T a a 2 M 1 a 1 T a 2 a Point 1 T L L L T T L L T L L L T T L L T detm a 1 aa 2 a 1 2 + 1 > 0 11 T T x x M λ 12 y y x y λ 2 a + 1λ + a 2 2a + 2 0 13 D D a + 1 2 4a 2 2a + 2 a
kokyuroku.dvi
On Applications of Rigorous Computing to Dynamical Systems (Zin ARAI) Department of Mathematics, Kyoto University email: arai@math.kyoto-u.ac.jp 1 [12, 13] Lorenz 2 Lorenz 3 4 2 Lorenz 2.1 Lorenz E. Lorenz