, ( 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
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1 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 problems of computer vision. Bundle adjustment and maxium likelihood estimation are well used for geometric fitting. In this paper, we present a maximum likelihood estimation to the data which are constrained linear in the model parameters. We describe the geometric meanings of maximum likelihood and its reliability evaluation. We also show geometric fitting examples. 1.,.,,, 1 Department of Information and Computer Sciences, Toyohasi University of Technology.,., 8). 2. x, = 1,..., N u. F (x; u) = 0 (1) F (x ; u) 0, = 1,..., N (2) u., 2,. (1) F (x; u) x, u,. (1). (ξ(x), u) = 0 (3), a, b (a, b). ξ(x) ξ i(x) (1) u i x. (1) x, 1, 1 u., (3) u., u., u = 1.,. (3) (2), u. 3. (ξ(x ), u) 0, = 1,..., N () () u,. N (ξ, u) 2 = (u, ξ ξ u) = (u, M LS u) (5) 1
2 , ( 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 (8) ξ, u., ξ ξ. x x 0, V [x ], ξ = ξ(x ) V [ξ ]. ( ) ( ξ ξ V [ξ ] = V [x ] (9). (9) ξ, x x x, x 2 ξ.. ξ M, ξ M N, (u, ξ) = 0.,, 1. (8) ξ,,. ξ ξ = ξ ξ (10), ξ., (7). J = ( ξ, V [ξ ] 1 ξ ) (11) (8). (ξ ξ, u) = 0 (12) (8), λ, ( ξ, V [ξ ] 1 ξ ) λ ((ξ ξ, u)) (13), ξ 0,. (1),. 2V [ξ ] 1 ξ λ u = 0 (1) ξ = λ 2 V [ξ ]u (15) (12), λ. λ = 2(u, ξ ) (u, V [ξ ]u) (16) 1 ξ, (9) V [ξ ]. (7) V [ξ ] 1. 2
3 (15) (11),. J = 1 λ 2 (V [ξ ]u, V [ξ ] 1 V [ξ ]u) = 1 λ 2 (u, V [ξ ]u) (16), u. (u, ξ J = ) 2 (u, V [ξ ]u) 5. (17) (18) ( x, y ) 2 6.,,,. (18), Chojnacki 2) FNS, Leedan 7) HEIV, 9). Chojnacki FNS. (18) u,. uj = = 2(u, ξ )ξ (u, V [ξ ]u) 2ξ (ξ u) (u, V [ξ ]u) = 2(M L)u,. ξ M ξ (u, V [ξ ]u), L 2(u, ξ )V [ξ ]u (u, V [ξ ]u) 2 2(u, ξ )V [ξ ]u (u, V [ξ ]u) 2 (19) (u, ξ )V [ξ ] (u, V [ξ ]u) 2 (20) (18) (19) 0 u., u, u = 1. Chojnacki FNS. ( 1 ) u. ( 2 ), λ u. Xu = λu, X M L (21) ( 3 ) u u u., u u 2. 1 : (x, y ), = 1,..., N 2. ξ(x, y), u 1, (22), (3). Ax + By + Cf 0 = 0 (22) ξ(x, y) = (x y f 0, u = (A B C (23) x, y, = 1,..., N 0, σ, x = (x y f 0 V [x ], ( ξ/), V [x ] = σ σ 2 0, ( ) ξ = ξ, V [ξ ] = σ (2) σ 2 V 0 [ξ ] (25), V 0 [ξ ]. (18) σ 2 1 f 0.,,. 3
4 3 ( x, y ),, (20) V [ξ ] V 0[ξ ]. 2 : (x, y ), = 1,..., N Ax 2 + 2Bxy + Cy 2 + 2(Dx + Ey)f 0 + F f 2 0 = 0 (26) 3. ξ(x, y), u ξ(x, y) = (x 2 2xy y 2 2xf 0 2yf 0 f 2 0, u = (A B C D E F (27), (26), (3). x, y, = 1,..., N 0, σ, ( ξ/), ( ) 2x 2y 0 2f ξ = 0 2x 2y 0 2f ξ,. x 2 x y 0 x 0 0 x y x 2 + y 2 x y y x 0 V [ξ ] = σ 2 0 x y y 2 0 y 0 x y 0 f σ 2 V 0 [ξ ] (29) 0 x y 0 f : 2, 1 (x, y) 2 (x, y ), (28). ( x y f 0, F x y f 0 ) = 0 (30) F 2,. 2 N (x, y ), (x, y ), = 1,..., N. ξ(x, y), u ξ(x, y, x, y ) = (xx xy xf 0 yx yy yf 0 f 0 x f 0 y f 2 0, u = (F 11 F 12 F 13 F 21 F 22 F 23 F 31 F 32 F 33 (31), (30), (3). (x, y ), (x, y ), = 1,..., N x y 0, σ, ( ξ/), x y f ( ) ξ x y f = x 0 0 y 0 0 f ξ,. 0 x 0 0 y 0 0 f 0 0 V [ξ ] = σ 2 x 2 + x 2 x y f 0 x x y 0 0 f 0 x 0 0 x y x 2 + y 2 f 0 y 0 x y 0 0 f 0 x 0 f 0x f 0y f x y 0 0 y 2 + x 2 x y f 0 x f 0 y x y 0 x y y 2 + y 2 f 0 y 0 f 0 y f 0 x f 0 y f f 0x 0 0 f 0y 0 0 f f 0 x 0 0 f 0 y 0 0 f0 2 0 σ 2 V 0[ξ ] (32) (33)
5 7. x ( x, y ) ( x, y ) ξ,., (x, y )., ξ., x x 0, V [x ], x., (ξ( x ), u) = 0 (3), E = (x x, V [x ] 1 (x x )) (35) x, u. x x = x x (36), x., (35). E = ( x, V [x ] 1 x ) (37) (3). (ξ(x x ), u) = 0 (38) ξ = ξ(x ), ξ(x x ) = ξ ( ξ/) x +, 1 x 2,. ( ) ξ ( x, u) = (ξ, u) (39) (9), ( ξ/) ξ(x) x = x. (38), λ, ( ) ) ( x, V [x ] 1 ξ x ) λ (( x, u) (ξ, u) ( ) = ( x, V [x ] 1 ξ ) (0) x ) λ (( x,, u) (ξ, u), x 0,. 2V [x ] 1 x λ ( ξ u = 0 (1) (1),. x = λ ( ) ξ 2 V [x ] u (39). (2) ( ) ( ) λ ξ ξ 2 ( V [x ] u, u) = (ξ, u) (3) (9), λ. λ = 2(u, ξ ) (u, V [ξ ]u) (2) (37),. E = 1 ( ) λ 2 ξ ( ξ (V [x ] u, = 1 ( ) ( λ 2 ξ ξ (u, V [x ] = λ 2 (u, V [ξ ]u) (), u. (u, ξ E = ) 2 (u, V [ξ ]u) u) u) (18)., x 1 ξ., FNS () (5) (6) 5
6 u. û, (36), (2), () x 1 ˆx. ˆx = x (u, ξ )V [x ] (u, V [ξ ]u) ( ξ u (7) (7) 1,, ). u O u u^ u 8. 5,.,. u, ū., u u ū 5. u u ū,. u = u (u, ū)u = P u u, P u I ūū (8) P u.,. V [u] = E[ u u ] = E[(P u u)(p u u ] (9), E[ ] u. (3) D = trv [u] = E[ u 2 ] = E[ P u u 2 ] (50) RMS,., u,, V [u],. ξ 0, V [ξ ], u 15),6). V [u] M 1, M N ξ ξ (ū, V [ ξ ]ū),. Chernov 1) (51) 1 KCR(Kantani-Cramer-Rao). u σ 0 u ū, O(σ ) (51). (50), K. D = 1 K P uu K (a) 2 (52) a=1, u (a) a., KCR RMS, RMS M 1. 9.,,,,. (51) 1 ξ, (37). M. 6
7 (a) σ (b) (a) σ (b) 6 (c), (a), (b) RMS, :FNS, :, :KCR, (c) σ = 1, (d) σ = 5. : 3x + 6y f 0 = 0, f 0 = N, ( x, ȳ ), = 1,..., N 0, σ (x, y ), RMS KCR. N = 30, 6(a). 0, σ = 0,..., 5, 0.1, FNS. σ 1000, RMS 6(b). FNS RMS, RMS, KCR. 6(c), (d) σ = 1, 5 FNS., FNS., u = 1, (6) (18) 10). 5 : (0, 0), 200, 100 (d) 7 (c), (a), (b) RMS, :FNS, :, :KCR, (c) σ = 1, (d) σ = 3. N, ( x, ȳ ), = 1,..., N 0, σ (x, y ), RMS KCR. N = 10, 7(a). 0, σ = 0,..., 3, 0.1, FNS. σ 1000, RMS 7(b). FNS RMS, RMS, KCR. 7(c), (d) σ = 1, 3 FNS. RMS,, FNS (d) 7
8 情報処理学会研究報告 (a) σ 5 (b) (a) 図 8 直線当てはめの精度評価, (a) シミュレーションデータ, (b) RMS 誤差, 実線:FNS 法, 破線:最小二乗法, 点 線:KCR 下界 図 9 直線当てはめの精度評価, (a) シミュレーションデータ, (b) RMS 誤差, 実線:FNS 法, 破線:最小二乗法, 点 線:KCR 下界 とがわかる. また, 得られた解による楕円の描画結果を見ると, FNS 法の方が真の楕円に近 参 い楕円が得られていることがわかる. 0 ), = 1,..., N 例 6 基礎行列の精度評価: 異なる 2 画像間の N 組の対応点 (x, y ), (x 0, y に期待値 0, 標準偏差 σ の正規分布に従うノイズを付加したデータ (x, y ), 0 (x0, y ) (b) 考 文 献 1) Chernov, N. and Lesort, C.: Statistical efficiency of vurve fitting algrithms, Comput. Stat. Data Anal., 7-, pp (200). 2) Chojnacki, M., Brooks, M. J., van den Hengel, A. and Gawley, D.: On the fitting on surfaces to data with covariances, IEEE Trans. Patt. Anal. Mach. Intell., Vol.22, No.11, pp (2000). 3) 金谷健一: これならわかる最適化数学 基礎原理から計算方法まで, 共立出版 (2005). ) 金谷健一, 菅谷保之: 幾何学的当てはめの厳密な最尤推定の統一的計算法, 情報処理学 会論文誌, コンピュータビジョンとイメージメディア, Vol.2, No.1, pp (2009). 5) Kanatani, K.: Statisitical Optimization for Geometric Computation: Theory and Practice, Elsevier Science, Amsterdam, The Netherlands (1996); Dover, New York (2005). 6) 金谷健一: 最尤推定の最適性と KCR 下界, 情報処理学会研究報告, 2005-CVIM , pp.59 6 (2005). 7) Leedan, Y. and Meer, P.: Heteroscedastic regression in computrer vision: Problems with bilinear constraint, Int. J. Comput Vision, Vol.37, No.2, pp (2000). 8) 岡谷貴之: バンドルアジャストメント, 情報処理学会研究報告, 2009-CVIM , pp.1 16 (2009). 9) 菅谷保之, 金谷健一: 基礎行列の高精度計算法とその性能比較, 情報処理学会研究報告, 2006-CVIM , pp (2006). 10) 菅谷保之, 金谷健一: 画像の三次元理解のための最適化計算 [I] 直線当てはめ, 情報 処理学会誌, Vol.92, No.3, pp (2009). 11) 山田健人, 金澤 靖, 金谷健一, 菅谷保之: 2 画像からの 3 次元復元の最新アルゴリズ ム, 情報処理学会研究報告, 2009-CVIM , (2009). から 最尤推定によって基礎行列を計算し, その RMS 誤差と KCR 下界を比較する. 図 8(a) のシミュレーションデータに対して, 期待値 0, 標準偏差 σ = 0,..., 5, 刻み幅 0.1 の正規分布に従うノイズを付加し, FNS 法を用いて基礎行列を計算する. 各 σ に対して異な るノイズを付加して 1000 回の試行を行い, RMS 誤差を計算した結果が図 8(b) である. 実 線が FNS 法の RMS 誤差, 破線が最小二乗法の RMS 誤差, 点線が KCR 下界である. 最小 二乗法では, ノイズの増加に従って精度が低下するが, FNS 法では KCR 下界にほぼ一致す るような高精度な解が得られている. 図 9 は実画像から手動で 100 点の特徴点を抽出して, FNS 法により基礎行列をして, 山田 ら11) らの方法で 3 次元復元を行った結果である. 10. お わ り に 本稿では幾何学的当てはめの最尤推定法について解説した. コンピュータビジョンの分野 でよく現れる幾何学的当てはめの問題は, データとモデルパラメータが線形拘束条件で表さ れる場合が多く, 本稿ではこの場合についてのみ取り扱った. 本稿の内容は歴史的背景など には触れずに, 実際の問題ですぐに活用できることを目的として, 最尤推定法の具体的な計 算方法や推定値の信頼性評価について述べた. また, これらについて直線当てはめや楕円当 てはめなどの具体例を示した. 8 c 2009 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
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![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 =")
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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
IPSJ SIG Technical Report Vol.2010-CVIM-172 No /5/ Object Tracking Based on Generative Appearance Model 1. ( 1 ) ( 2 ) ( 3 ) 1 3) T
1 2 2 3 1 Objec Tracking Based on Generaive Appearance Model 1. ( 1 ) ( 2 ) ( 3 ) 1 3) Tasuya YONEKAWA, 1 Kazuhiko KAWAMOTO, 2 Asushi IMIYA 2 and Akihiro SUGIMOTO 3 We propose a mehod for racking objecs
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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),
kubostat2017b p.1 agenda I 2017 (b) probability distribution and maximum likelihood estimation :
kubostat2017b p.1 agenda I 2017 (b) probabilit distribution and maimum likelihood estimation kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2017 11 14 : 2017 11 07 15:43 1 : 2 3? 4 kubostat2017b (http://goo.gl/76c4i)
川崎医会誌一般教, 32 号 : (2006) 39 非心ベキ正規分布のパラメータの推定 川崎医科大学 教材教具センター *, 情報科学教室 ** 格和勝利 * 近藤芳朗 ** ( 平成 18 年 11 月 208 受理 ) On Estimation of Parameters in
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& 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
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
, 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
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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
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
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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
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.
IPSJ SIG Technical Report Vol.2012-MUS-96 No /8/10 MIDI Modeling Performance Indeterminacies for Polyphonic Midi Score Following and
MIDI 1 2 3 2 1 Modeling Performance Indeterminacies for Polyphonic Midi Score Following and Its Application to Automatic Accompaniment Nakamura Eita 1 Yamamoto Ryuichi 2 Saito Yasuyuki 3 Sako Shinji 2
DPA,, ShareLog 3) 4) 2.2 Strino Strino STRain-based user Interface with tacticle of elastic Natural ObjectsStrino 1 Strino ) PC Log-Log (2007 6)
1 2 1 3 Experimental Evaluation of Convenient Strain Measurement Using a Magnet for Digital Public Art Junghyun Kim, 1 Makoto Iida, 2 Takeshi Naemura 1 and Hiroyuki Ota 3 We present a basic technology
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
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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
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,, 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
1,a) 1,b) TUBSTAP TUBSTAP Offering New Benchmark Maps for Turn Based Strategy Game Tomihiro Kimura 1,a) Kokolo Ikeda 1,b) Abstract: Tsume-shogi and Ts
JAIST Reposi https://dspace.j Title ターン制戦略ゲームにおけるベンチマークマップの提 案 Author(s) 木村, 富宏 ; 池田, 心 Citation ゲームプログラミングワークショップ 2016 論文集, 2016: 36-43 Issue Date 2016-10-28 Type Conference Paper Text version author
2. ICA ICA () (Blind Source Separation BBS) 2) Fig. 1 Model of Optical Topography. ( ) ICA 2.2 ICA ICA 3) n 1 1 x 1 (t) 2 x 2 (t) n x(t) 1 x(t
ICA 1 2 2 (Independent Component Analysis) Denoising Method using ICA for Optical Topography Yamato Yokota, 1 Tomoyuki Hiroyasu 2 and Hisatake Yokouchi 2 Optical topography is one of the promising ways
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
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1 1 3 2 3 Efficient Projector Calibration Method using Plane with Checkerboard Pattern Shota Kiyota, 1 Hiroshi Kawasaki, 1 Ryo Furukawa 3 and Ryusuke Sagawa 2 In recent years, development on 3D measurement
IPSJ SIG Technical Report Vol.2012-IS-119 No /3/ Web A Multi-story e-picture Book with the Degree-of-interest Extraction Function
1 2 2 3 4 2 Web A Multi-story e-picture Book with the Degree-of-interest Extraction Function Kunimichi Shibata, 1 Masakuni Moriyama, 2 Kazuhide Yukawa, 2 Koji Ueno, 3 Kazuo Takahashi 4 and Shigeo Kaneda
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1, Adobe Illustrator Photoshop [1] [2] [3] Initital Values Assignment of Parameters Using Onomatopoieas for Interactive Design Tool Tsuyoshi NAKAMURA, Yuki SAWAMURA, Masayoshi KANOH, and Koji YAMADA Graduate
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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,
IPSJ SIG Technical Report Vol.2009-CVIM-167 No /6/10 1 bundle 10) A 90 Bundle Adjustment TAKAYUKI OKATANI 1 Bundle adjustment is a general meth
1 bundle 10) A 90 Bundle Adjustment TAKAYUKI OKATANI 1 Bundle adjustment is a general method for the problem of estimating parameters of a geometric model from images. It is characterized by that nonlinear
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28 TCG SURF Card recognition using SURF in TCG play video
28 TCG SURF Card recognition using SURF in TCG play video 1170374 2017 3 2 TCG SURF TCG TCG OCG SURF Bof 20 20 30 10 1 SURF Bag of features i Abstract Card recognition using SURF in TCG play video Haruka
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The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). The material has been made available on the website
The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). The material has been made available on the website by the author(s) under the agreement with the IPSJ.
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]
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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
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パターン認識早稲田大学講義 平成 7 年度 独 産業技術総合研究所栗田多喜夫 赤穂昭太郎 統計的特徴抽出 パターン認識過程 特徴抽出 認識対象から何らかの特徴量を計測 抽出 する必要がある 認識に有効な情報 特徴 を抽出し 次元を縮小した効率の良い空間を構成する過程 文字認識 : スキャナ等で取り込んだ画像から文字の識別に必要な本質的な特徴のみを抽出 例 文字線の傾き 曲率 面積など 識別 与えられた未知の対象を
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Mover Design and Performance Analysis of Linear Synchronous Reluctance Motor with Multi-flux Barrier Masayuki Sanada, Member, Mitsutoshi Asano, Student Member, Shigeo Morimoto, Member, Yoji Takeda, Member
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社団法人人工知能学会 Japanese Society for Artificial Intelligence 人工知能学会研究会資料 JSAI Technical Report SIG-Challenge-B3 (5/5) RoboCup SSL Humanoid A Proposal and its Application of Color Voxel Server for RoboCup SSL
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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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