IPSJ SIG Technical Report Vol.2017-MUS-116 No /8/24 MachineDancing: 1,a) 1,b) 3 MachineDancing MachineDancing MachineDancing 1 MachineDan
|
|
|
- あいね やすこ
- 4 years ago
- Views:
Transcription
![MachineDancing [1] 1 305 0058 1-1-1 a) s.fukayama@aist.go.](/docs-images/101/151511068/images/1-1.jpg "jp b) m.got")
1 MachineDancing: 1,a) 1,b) 3 MachineDancing MachineDancing MachineDancing 1 MachineDancing MachineDancing [1] a) s.fukayama@aist.go.jp b) m.goto@aist.go.jp 1 MachineDancing 3 CG c 2017 Information Processing Society of Japan 1
2 1 1 MachineDancing 2 3 Kinect [2], [3] [4], [5], [6] [7] [8] 2.2 [9] 1 [10] CG CG CG MikuMikuDance *1 vmd xyz x CG *1 c 2017 Information Processing Society of Japan 2
![0.0 1 2 2 3.2 and and Dynamic Bayesian Network [11] Madmom [12] 2 3.3 [13] (Motion Rhythm Feature) 1.0 3.](/docs-images/101/151511068/images/3-1.jpg "4 Mini-batch k-means [14] [13] 3 3 3.5 t = 1,..., T t x t s t x 1:T = x 1.")
3 and and Dynamic Bayesian Network [11] Madmom [12] [13] (Motion Rhythm Feature) Mini-batch k-means [14] [13] t = 1,..., T t x t s t x 1:T = x 1... x T c 2017 Information Processing Society of Japan 3
4 s 1:T = s 1... s T P (s 1:T ) = x 1:T P (s 1:T x 1:T ) P (x 1:T ) (1) s 1:T x 1:T s 1:T x 1:T P (x 1:T s 1:T ) x 1:T = argmax x 1:T P (x 1:T s 1:T ) (2) P (x 1:T s 1:T ) = P (s 1:T x 1:T ) P (x 1:T ) P (s 1:T ) (3) (3) x 1:T x 1:T = argmax x 1:T P (s 1:T x 1:T ) P (x 1:T ) (4) s t t 3 (3) P (x 1 ) T P (s t x t+1, x t, x t 1 ) P (x t x t 1 ) (5) t=2 P (x t x t 1 ) Viterbi 3 P (s t x t 1, x t, x t+1 ) N Viterbi O ( N 2 T ) O ( N 6 T ) Viterbi t Mini-batch k-means [14] (chorus) Kinect (4) t ˆx t (4) x 1:T = argmax x 1:T P (s 1:T, ˆx 1:T x 1:T ) P (x 1:T ) (6) Viterbi c 2017 Information Processing Society of Japan 4
5 1 x x U x L (6) P ( s 1:T, ˆx 1:T x U 1:T, x L ( 1:T) P x U 1:T, x L ) 1:T (7) 2 2 [15], [16] x U x L h (x) x 2 f (x) g (x) h (x) = f (x) + g (x) y u h (x) ĥ (x, y) ĥ (x, y, u) = f (x) + g (y) + u (x y) (8) 3 x y max x h (x) ) min u (max x,y ĥ (x, y, u) ĥ (x, y, u) = {f (x) + ux} + {g (y) uy} (9) x y x y x = arg max{f (x) + ux} x (10) y = arg max{g (y) uy} y (11) max ĥ (x, y, u) = f (x ) + g (y ) + u (x y ) (12) x,y u u u x y α u := u α (x y ) (13) u u 2 Viterbi 1 2 u z (1) z (2) 2 t t 1 ( ) 2 p 1 z (1) t z (1) t 1 ( ) p 2 z (2) t z (2) t 1 ( ( q t,r t,s t ( ) p q q t z (1) t ) p r r t z (2) t ) p s s t z (1) t, z (2) t q t r t s t z (1) z (2) 10 z (1), z (2) {0, 1, 2, 3, 4, 5, 6, 7, 8, 9} p 1, p q 1:50 r 1:50 s 1:50 s t = q t + r t ( ) ( ) p q q t z (1) t = N z (1) t, 1 (14) ( ) ( ) p r r t z (2) t = N z (2) t, 1 (15) ( ) ( ) p s s t z (1) t, z (2) t = N z (1) t + z (2) t, 1 (16) 100 α = 0.1 z (1) Viterbi z (2) c 2017 Information Processing Society of Japan 5
![JST ACCEL (JPMJAC1602) 3 z (1) Viterbi z (2) Viterbi 4 Viterbi 3 z (1), z (2) 0 4 5. MachineDancing 2 [1] MachineDancing: Vol. 2014, No. 14, pp. 1 7 (2014). [2] Chen, K. M., Shen, S. T. and Prior, S.](/docs-images/101/151511068/images/6-1.jpg "D.: Using music and motion analysis to construct 3D animations and visualisations, Digital Creativity, Vol. 19, No. 2 (2008). [3] Panagiotakis, C., Holzapfel, A.")
6 JST ACCEL (JPMJAC1602) 3 z (1) Viterbi z (2) Viterbi 4 Viterbi 3 z (1), z (2) MachineDancing 2 [1] MachineDancing: Vol. 2014, No. 14, pp. 1 7 (2014). [2] Chen, K. M., Shen, S. T. and Prior, S. D.: Using music and motion analysis to construct 3D animations and visualisations, Digital Creativity, Vol. 19, No. 2 (2008). [3] Panagiotakis, C., Holzapfel, A., Michel, D. and Argyros, A. A.: Beat Synchronous Dance Animation based on Visual Analysis of Human Motion and Audio Analysis of Music Tempo, Proceedings of ISVC 2013, pp (2013). [4] Shiratori, T., Nakazawa, A. and Ikeuchi, K.: Synthesizing dance performance using musical and motion features, Proceedings of ICRA 2006, pp (2006). [5] Kim, J. W., Fouad, H., Sibert, J. L. and Hahn, J. K.: Perceptually motivated automatic dance motion generation for music, Computer Animation and Virtual Worlds 2009, Vol. 20, pp (2009). [6] Alankus, G., Bayazit, A. A. and Bayazit, O. B.: Computer Animation and Virtual Worlds, No. 16, pp (2005). [7] Fan, R., Xu, S. and Geng, W.: Example-Based Automatic Music-Driven Conventional Dance Motion Synthesis, IEEE Transactions on Visualization and Computer Graphics, Vol. 18, No. 3 (2012). [8] Ofli, F., Erzin, E., Yemez, Y. and Tekalp, A. M.: Learn2Dance: Learning Statistical Music-to-Dance Mappings for Choreography Synthesis, IEEE Transactions on Multimedia, Vol. 14, No. 3 (2012). [9] Lee, M., Lee, L. and Park, J.: Music similarity-based approach to generating dance motion sequence, Multimedia Tools and Applications, Vol. 62, No. 3, pp (2013). [10] Oore, S. and Akiyama, Y.: Learning to Synthesize Arm Motion to Music By Example, Proceedings of WSCG 2006, pp (2006). [11] Böck, S., Krebs, F. and Widmer, G.: Joint Beat and Downbeat Tracking with Recurrent Neural Networks, Proc. ISMIR, ISMIR 16 (2016). [12] Böck, S., Korzeniowski, F., Schlüter, J., Krebs, F. and Widmer, G.: madmom: a new Python Audio and Music Signal Processing Library, Proceedings of the 24th ACM International Conference on Multimedia, Amsterdam, The Netherlands, pp (online), DOI: / (2016). [13] Shiratori, T.: Synthesis of Dance Performance Based on Analyses of Human Motion and Music, Doctoral Dissertation (2006). [14] Sculley, D.: Web-Scale K-Means Clustering, WWW2010, pp. 1 2 (2010). [15] Vol. 2013, pp. 1 6 (2013). [16] Vol. 2014, No. 8, pp. 1 7 (2014). c 2017 Information Processing Society of Japan 6
[2][3][4][5] 4 ( 1 ) ( 2 ) ( 3 ) ( 4 ) 2. Shiratori [2] Shiratori [3] [4] GP [5] [6] [7] [8][9] Kinect Choi [10] 3. 1 c 2016 Information Processing So
![[2][3][4][5] 4 ( 1 ) ( 2 ) ( 3 ) ( 4 ) 2. Shiratori [2] Shiratori [3] [4] GP [5] [6] [7] [8][9] Kinect Choi [10] 3. 1 c 2016 Information Processing So](/thumbs/76/74386745.jpg "[2][3][4][5] 4 ( 1 ) ( 2 ) ( 3 ) ( 4 ) 2. Shiratori [2] Shiratori [3] [4] GP [5] [6] [7] [8][9] Kinect Choi [10] 3. 1 c 2016 Information Processing So")
1,a) 2 2 1 2,b) 3,c) A choreographic authoring system reflecting a user s preference Ryo Kakitsuka 1,a) Kosetsu Tsukuda 2 Satoru Fukayama 2 Naoya Iwamoto 1 Masataka Goto 2,b) Shigeo Morishima 3,c) Abstract:
WISS 2018 [2 4] [5,6] Query-by-Dancing Query-by- Dancing Cao [1] OpenPose 2 Ghias [7] Query by humming Chen [8] Query by rhythm Jang [9] Query-by-tapp
![WISS 2018 [2 4] [5,6] Query-by-Dancing Query-by- Dancing Cao [1] OpenPose 2 Ghias [7] Query by humming Chen [8] Query by rhythm Jang [9] Query-by-tapp](/thumbs/91/107555259.jpg "WISS 2018 [2 4] [5,6] Query-by-Dancing Query-by- Dancing Cao [1] OpenPose 2 Ghias [7] Query by humming Chen [8] Query by rhythm Jang [9] Query-by-tapp")
Query-by-Dancing: WISS 2018. Query-by-Dancing Query-by-Dancing 1 OpenPose [1] Copyright is held by the author(s). DJ DJ DJ WISS 2018 [2 4] [5,6] Query-by-Dancing Query-by- Dancing Cao [1] OpenPose 2 Ghias
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
IPSJ SIG Technical Report Vol.2015-MUS-106 No.10 Vol.2015-EC-35 No /3/2 BGM 1,4,a) ,4 BGM. BGM. BGM BGM. BGM. BGM. BGM. 1.,. YouTube 201
BGM 1,4,a) 1 2 2 3,4 BGM. BGM. BGM BGM. BGM. BGM. BGM. 1.,. YouTube 2015 1 100.. Web.. BGM.BGM [1]. BGM BGM 1 Waseda University, Shinjuku, Tokyo 169-8555, Japan 2 3 4 JST CREST a) ha-ru-ki@asagi.waseda.jp.
IPSJ SIG Technical Report Vol.2011-MUS-91 No /7/ , 3 1 Design and Implementation on a System for Learning Songs by Presenting Musical St
1 2 1, 3 1 Design and Implementation on a System for Learning Songs by Presenting Musical Structures based on Phrase Similarity Yuma Ito, 1 Yoshinari Takegawa, 2 Tsutomu Terada 1, 3 and Masahiko Tsukamoto
動画コンテンツ 動画 1 動画 2 動画 3 生成中の映像 入力音楽 選択された素片 テンポによる伸縮 音楽的構造 A B B B B B A C C : 4) 6) Web Web 2 2 c 2009 Information Processing S
1 2 2 1 Web An Automatic Music Video Creation System by Reusing Dance Video Content Sora Murofushi, 1 Tomoyasu Nakano, 2 Masataka Goto 2 and Shigeo Morishima 1 This paper presents a system that automatically
IPSJ SIG Technical Report Vol.2015-MUS-107 No /5/23 HARK-Binaural Raspberry Pi 2 1,a) ( ) HARK 2 HARK-Binaural A/D Raspberry Pi 2 1.
HARK-Binaural Raspberry Pi 2 1,a) 1 1 1 2 3 () HARK 2 HARK-Binaural A/D Raspberry Pi 2 1. [1,2] [2 5] () HARK (Honda Research Institute Japan audition for robots with Kyoto University) *1 GUI ( 1) Python
「hoge」
ICS-06M-404 255 1 7 1.1................................... 7 1.1.1........................... 7 1.1.2........................ 8 1.1.3............................ 9 1.2..................................
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
3.1 Thalmic Lab Myo * Bluetooth PC Myo 8 RMS RMS t RMS(t) i (i = 1, 2,, 8) 8 SVM libsvm *2 ν-svm 1 Myo 2 8 RMS 3.2 Myo (Root
1,a) 2 2 1. 1 College of Information Science, School of Informatics, University of Tsukuba 2 Faculty of Engineering, Information and Systems, University of Tsukuba a) oharada@iplab.cs.tsukuba.ac.jp 2.
2008 : 80725872 1 2 2 3 2.1.......................................... 3 2.2....................................... 3 2.3......................................... 4 2.4 ()..................................
DEIM Forum 2012 E Web Extracting Modification of Objec
DEIM Forum 2012 E4-2 670 0092 1 1 12 E-mail: nd11g028@stshse.u-hyogo.ac.jp, {dkitayama,sumiya}@shse.u-hyogo.ac.jp Web Extracting Modification of Objects for Supporting Map Browsing Junki MATSUO, Daisuke
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
25 2014 2 i 1 Kinect 17% 0% 100% iii iii v vii 1 1 1.1............................... 1 1.2............................... 2 1.3.............................. 2 2 3 2.1.................................
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
(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
IPSJ SIG Technical Report 1,a) 1,b) 1,c) 1,d) 2,e) 2,f) 2,g) 1. [1] [2] 2 [3] Osaka Prefecture University 1 1, Gakuencho, Naka, Sakai,
![IPSJ SIG Technical Report 1,a) 1,b) 1,c) 1,d) 2,e) 2,f) 2,g) 1. [1] [2] 2 [3] Osaka Prefecture University 1 1, Gakuencho, Naka, Sakai,](/thumbs/94/119099134.jpg "IPSJ SIG Technical Report 1,a) 1,b) 1,c) 1,d) 2,e) 2,f) 2,g) 1. [1] [2] 2 [3] Osaka Prefecture University 1 1, Gakuencho, Naka, Sakai,")
1,a) 1,b) 1,c) 1,d) 2,e) 2,f) 2,g) 1. [1] [2] 2 [3] 1 599 8531 1 1 Osaka Prefecture University 1 1, Gakuencho, Naka, Sakai, Osaka 599 8531, Japan 2 565 0871 Osaka University 1 1, Yamadaoka, Suita, Osaka
Vol. 43 No. 2 Feb. 2002,, MIDI A Probabilistic-model-based Quantization Method for Estimating the Position of Onset Time in a Score Masatoshi Hamanaka
Vol. 43 No. 2 Feb. 2002,, MIDI A Probabilistic-model-based Quantization Method for Estimating the Position of Onset Time in a Score Masatoshi Hamanaka, Masataka Goto,, Hideki Asoh and Nobuyuki Otsu, This
IPSJ SIG Technical Report Vol.2014-MBL-70 No.49 Vol.2014-UBI-41 No /3/15 2,a) 2,b) 2,c) 2,d),e) WiFi WiFi WiFi 1. SNS GPS Twitter Facebook Twit
2,a) 2,b) 2,c) 2,d),e) WiFi WiFi WiFi 1. SNS GPS Twitter Facebook Twitter Ustream 1 Graduate School of Information Science and Technology, Osaka University, Japan 2 Cybermedia Center, Osaka University,
2 3, 4, 5 6 2. [1] [2] [3]., [4], () [3], [5]. Mel Frequency Cepstral Coefficients (MFCC) [9] Logan [4] MFCC MFCC Flexer [10] Bogdanov2010 [3] [14],,,
![2 3, 4, 5 6 2. [1] [2] [3]., [4], () [3], [5]. Mel Frequency Cepstral Coefficients (MFCC) [9] Logan [4] MFCC MFCC Flexer [10] Bogdanov2010 [3] [14],,,](/thumbs/40/20501376.jpg "2 3, 4, 5 6 2. [1] [2] [3]., [4], () [3], [5]. Mel Frequency Cepstral Coefficients (MFCC) [9] Logan [4] MFCC MFCC Flexer [10] Bogdanov2010 [3] [14],,,")
DEIM Forum 2016 E1-4 525-8577 1 1-1 E-mail: is0111rs@ed.ritsumei.ac.jp, oku@fc.ritsumei.ac.jp, kawagoe@is.ritsumei.ac.jp 373 1.,, itunes Store 1, Web,., 4,300., [1], [2] [3],,, [4], ( ) [3], [5].,,.,,,,
ipod touch 1 2 Apple ipod touch ipod touch 3 ( ) ipod touch ( 1 ) Apple ( 2 ) Web 1),2) 3. ipod touch 1 2 ipod touch x y z i
ipod touch 1 1 ipod touch. 1) 6 2) 3) A library for detecting movements of an ipod touch by 3D acceleration Akira Kotaki 1 and Mariko Sasakura 1 The aim of this study is to develop a library for detecting
Convolutional Neural Network A Graduation Thesis of College of Engineering, Chubu University Investigation of feature extraction by Convolution
Convolutional Neural Network 2014 3 A Graduation Thesis of College of Engineering, Chubu University Investigation of feature extraction by Convolutional Neural Network Fukui Hiroshi 1940 1980 [1] 90 3
1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15. 1. 2. 3. 16 17 18 ( ) ( 19 ( ) CG PC 20 ) I want some rice. I want some lice. 21 22 23 24 2001 9 18 3 2000 4 21 3,. 13,. Science/Technology, Design, Experiments,
IPSJ SIG Technical Report Vol.2014-CG-155 No /6/28 1,a) 1,2,3 1 3,4 CG An Interpolation Method of Different Flow Fields using Polar Inter
,a),2,3 3,4 CG 2 2 2 An Interpolation Method of Different Flow Fields using Polar Interpolation Syuhei Sato,a) Yoshinori Dobashi,2,3 Tsuyoshi Yamamoto Tomoyuki Nishita 3,4 Abstract: Recently, realistic
Gaze Head Eye (a) deg (b) 45 deg (c) 9 deg 1: - 1(b) - [5], [6] [7] Stahl [8], [9] Fang [1], [11] Itti [12] Itti [13] [7] Fang [1],
![Gaze Head Eye (a) deg (b) 45 deg (c) 9 deg 1: - 1(b) - [5], [6] [7] Stahl [8], [9] Fang [1], [11] Itti [12] Itti [13] [7] Fang [1],](/thumbs/92/110933465.jpg "Gaze Head Eye (a) deg (b) 45 deg (c) 9 deg 1: - 1(b) - [5], [6] [7] Stahl [8], [9] Fang [1], [11] Itti [12] Itti [13] [7] Fang [1],")
1 1 1 Structure from Motion - 1 Ville [1] NAC EMR-9 [2] 1 Osaka University [3], [4] 1 1(a) 1(c) 9 9 9 c 216 Information Processing Society of Japan 1 Gaze Head Eye (a) deg (b) 45 deg (c) 9 deg 1: - 1(b)
1 1 CodeDrummer CodeMusician CodeDrummer Fig. 1 Overview of proposal system c
CodeDrummer: 1 2 3 1 CodeDrummer: Sonification Methods of Function Calls in Program Execution Kazuya Sato, 1 Shigeyuki Hirai, 2 Kazutaka Maruyama 3 and Minoru Terada 1 We propose a program sonification
Lyra 2 2 2 X Y X Y ivis Designer Lyra ivisdesigner Lyra ivisdesigner 2 ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) (1) (2) (3) (4) (5) Iv Studio [8] 3 (5) (4) (1) (
![Lyra 2 2 2 X Y X Y ivis Designer Lyra ivisdesigner Lyra ivisdesigner 2 ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) (1) (2) (3) (4) (5) Iv Studio [8] 3 (5) (4) (1) (](/thumbs/42/23098909.jpg "Lyra 2 2 2 X Y X Y ivis Designer Lyra ivisdesigner Lyra ivisdesigner 2 ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) (1) (2) (3) (4) (5) Iv Studio [8] 3 (5) (4) (1) (")
1,a) 2,b) 2,c) 1. Web [1][2][3][4] [5] 1 2 a) ito@iplab.cs.tsukuba.ac.jp b) misue@cs.tsukuba.ac.jp c) jiro@cs.tsukuba.ac.jp [6] Lyra[5] ivisdesigner[6] [7] 2 Lyra ivisdesigner c 2012 Information Processing
OngaCREST [10] A 3. Latent Dirichlet Allocation: LDA [11] Songle [12] Pitman-Yor (VPYLM) [13] [14,15] n n n 3.1 [16 18] PreFEst [19] F
![OngaCREST [10] A 3. Latent Dirichlet Allocation: LDA [11] Songle [12] Pitman-Yor (VPYLM) [13] [14,15] n n n 3.1 [16 18] PreFEst [19] F](/thumbs/77/74495478.jpg "OngaCREST [10] A 3. Latent Dirichlet Allocation: LDA [11] Songle [12] Pitman-Yor (VPYLM) [13] [14,15] n n n 3.1 [16 18] PreFEst [19] F")
1,a) 2,b) 1,c) LPMCC MFCC Fluctuation Pattern (LDA) Songle Pitman-Yor (VPYLM) 3278 1. (MIR: Music Information Retrieval) [1 5] [6 8] 1 National Institute of Advanced Industrial Science and Technology (AIST)
Wikipedia YahooQA MAD 4)5) MAD Web 6) 3. YAMAHA 7) 8) 2 3 4 5 6 2. Vocaloid2 2006 1 PV 2009 1 1100 200 YouTube 1 minato minato ussy 3D MAD F EDis ussy
1, 2 3 1, 2 Web Fischer Social Creativity 1) Social Creativity CG Network Analysis of an Emergent Massively Collaborative Creation Community Masahiro Hamasaki, 1, 2 Hideaki Takeda 3 and Takuichi Nishimura
IPSJ SIG Technical Report Vol.2012-CG-149 No.13 Vol.2012-CVIM-184 No /12/4 3 1,a) ( ) DB 3D DB 2D,,,, PnP(Perspective n-point), Ransa
3,a) 3 3 ( ) DB 3D DB 2D,,,, PnP(Perspective n-point), Ransac. DB [] [2] 3 DB Web Web DB Web NTT NTT Media Intelligence Laboratories, - Hikarinooka Yokosuka-Shi, Kanagawa 239-0847 Japan a) yabushita.hiroko@lab.ntt.co.jp
2). 3) 4) 1.2 NICTNICT DCRA Dihedral Corner Reflector micro-arraysdcra DCRA DCRA DCRA 3D DCRA PC USB PC PC ON / OFF Velleman K8055 K8055 K8055
1 1 1 2 DCRA 1. 1.1 1) 1 Tactile Interface with Air Jets for Floating Images Aya Higuchi, 1 Nomin, 1 Sandor Markon 1 and Satoshi Maekawa 2 The new optical device DCRA can display floating images in free
IPSJ SIG Technical Report Vol.2017-HCI-173 No.5 Vol.2017-EC-44 No /6/1 1,a) 1,2,b) 3,c) 1,d) 3D * 1* Graduate School of Engineerin
1,a) 1,2,b) 3,c) 1,d) 3D 1. 2.5 * 1*2 2.5 1 Graduate School of Engineering, Kobe University 2 PRESTO, Japan Science and Technology Agency 3 School of Information Science and Engineering, Ritsumeikan University
Dynamic Time Warping( DTW DTW 30 k-d tree Forebes [1] 2. DTW[2] DTW DTW DTW Forbes[1] k-d tree DTW Hsu[3] DTW Zhu[4] K-SVD Sun[5] Self-S
![Dynamic Time Warping( DTW DTW 30 k-d tree Forebes [1] 2. DTW[2] DTW DTW DTW Forbes[1] k-d tree DTW Hsu[3] DTW Zhu[4] K-SVD Sun[5] Self-S](/thumbs/92/107948823.jpg "Dynamic Time Warping( DTW DTW 30 k-d tree Forebes [1] 2. DTW[2] DTW DTW DTW Forbes[1] k-d tree DTW Hsu[3] DTW Zhu[4] K-SVD Sun[5] Self-S")
情報処理学会インタラクション 2015 IPSJ Interaction 2015 A62 2015/3/5 1,a) Natapon Pantuwong 2 1 1 1 Dynamic Time Warping 2 DTW DTW 30 k-d tree [1] A Rapid Motion Retrieval Technique using Simple and Discrete Representation
IPSJ SIG Technical Report Vol.2009-DPS-141 No.20 Vol.2009-GN-73 No.20 Vol.2009-EIP-46 No /11/27 1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Spe
1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Speech Visualization System Based on Augmented Reality Yuichiro Nagano 1 and Takashi Yoshino 2 As the spread of the Augmented Reality(AR) technology and service,
1(a) (b),(c) - [5], [6] Itti [12] [13] gaze eyeball head 2: [time] [7] Stahl [8], [9] Fang [1], [11] 3 -
![1(a) (b),(c) - [5], [6] Itti [12] [13] gaze eyeball head 2: [time] [7] Stahl [8], [9] Fang [1], [11] 3 -](/thumbs/89/100816452.jpg "1(a) (b),(c) - [5], [6] Itti [12] [13] gaze eyeball head 2: [time] [7] Stahl [8], [9] Fang [1], [11] 3 -")
Vol216-CVIM-22 No18 216/5/12 1 1 1 Structure from Motion - 1 8% Tobii Pro TX3 NAC EMR ACTUS Eye Tribe Tobii Pro Glass NAC EMR-9 Pupil Headset Ville [1] EMR-9 [2] 1 Osaka University Gaze Head Eye (a) deg
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
[2] 2. [3 5] 3D [6 8] Morishima [9] N n 24 24FPS k k = 1, 2,..., N i i = 1, 2,..., n Algorithm 1 N io user-specified number of inbetween omis
![[2] 2. [3 5] 3D [6 8] Morishima [9] N n 24 24FPS k k = 1, 2,..., N i i = 1, 2,..., n Algorithm 1 N io user-specified number of inbetween omis](/thumbs/76/74386791.jpg "[2] 2. [3 5] 3D [6 8] Morishima [9] N n 24 24FPS k k = 1, 2,..., N i i = 1, 2,..., n Algorithm 1 N io user-specified number of inbetween omis")
1,a) 2 2 2 1 2 3 24 Motion Frame Omission for Cartoon-like Effects Abstract: Limited animation is a hand-drawn animation style that holds each drawing for two or three successive frames to make up 24 frames
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
IPSJ SIG Technical Report iphone iphone,,., OpenGl ES 2.0 GLSL(OpenGL Shading Language), iphone GPGPU(General-Purpose Computing on Graphics Proc
iphone 1 1 1 iphone,,., OpenGl ES 2.0 GLSL(OpenGL Shading Language), iphone GPGPU(General-Purpose Computing on Graphics Processing Unit)., AR Realtime Natural Feature Tracking Library for iphone Makoto
IPSJ SIG Technical Report Vol.2014-HCI-158 No /5/22 1,a) 2 2 3,b) Development of visualization technique expressing rainfall changing conditions
1,a) 2 2 3,b) Development of visualization technique expressing rainfall changing conditions with a still picture Yuuki Hyougo 1,a) Hiroko Suzuki 2 Tadanobu Furukawa 2 Kazuo Misue 3,b) Abstract: In order
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
Honda 3) Fujii 4) 5) Agrawala 6) Osaragi 7) Grabler 8) Web Web c 2010 Information Processing Society of Japan
1 1 1 1 2 Geographical Feature Extraction for Retrieval of Modified Maps Junki Matsuo, 1 Daisuke Kitayama, 1 Ryong Lee 1 and Kazutoshi Sumiya 1 Digital maps available on the Web are widely used for obtaining
40.9 22.6 18.1 4.9 1.0 15.3 10.0 (%) 3.5 5.8 6.0 23.5 29.5 70.5 100.0 (%) 267 447 456 1,788 2,244 5,364 7,609 0.3 6.5 975 5.6 386 (%) (%) (%) 3.6 6.3 100.0 6.6 5.3 27.2 32.8 67.2 100.0 22.5 538 452 10.1
2.2 (a) = 1, M = 9, p i 1 = p i = p i+1 = 0 (b) = 1, M = 9, p i 1 = 0, p i = 1, p i+1 = 1 1: M 2 M 2 w i [j] w i [j] = 1 j= w i w i = (w i [ ],, w i [
![2.2 (a) = 1, M = 9, p i 1 = p i = p i+1 = 0 (b) = 1, M = 9, p i 1 = 0, p i = 1, p i+1 = 1 1: M 2 M 2 w i [j] w i [j] = 1 j= w i w i = (w i [ ],, w i [](/thumbs/93/111684107.jpg "2.2 (a) = 1, M = 9, p i 1 = p i = p i+1 = 0 (b) = 1, M = 9, p i 1 = 0, p i = 1, p i+1 = 1 1: M 2 M 2 w i [j] w i [j] = 1 j= w i w i = (w i [ ],, w i [")
RI-002 Encoding-oriented video generation algorithm based on control with high temporal resolution Yukihiro BANDOH, Seishi TAKAMURA, Atsushi SHIMIZU 1 1T / CMOS [1] 4K (4096 2160 /) 900 Hz 50Hz,60Hz 240Hz
3 2 2 (1) (2) (3) (4) 4 4 AdaBoost 2. [11] Onishi&Yoda [8] Iwashita&Stoica [5] 4 [3] 3. 3 (1) (2) (3)
![3 2 2 (1) (2) (3) (4) 4 4 AdaBoost 2. [11] Onishi&Yoda [8] Iwashita&Stoica [5] 4 [3] 3. 3 (1) (2) (3)](/thumbs/68/59204737.jpg "3 2 2 (1) (2) (3) (4) 4 4 AdaBoost 2. [11] Onishi&Yoda [8] Iwashita&Stoica [5] 4 [3] 3. 3 (1) (2) (3)")
(MIRU2012) 2012 8 820-8502 680-4 E-mail: {d kouno,shimada,endo}@pluto.ai.kyutech.ac.jp (1) (2) (3) (4) 4 AdaBoost 1. Kanade [6] CLAFIC [12] EigenFace [10] 1 1 2 1 [7] 3 2 2 (1) (2) (3) (4) 4 4 AdaBoost
3 Hill Fig. 3 Hill s muscle model Fig. 1 Mainly the lower limb musculoskeletal model 2 Fig. 2 Link structure of the bone OpenSim 4)5) 2.2 Open
CPG 1 1 CG CPG(Central Pattern Generator) Research on Generating Human Motion Using Musculoskeletal Model and CPG Akira Okazaki 1 and DongSheng Cai 1 Animation is one of the key elements of the CG, and
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.
Microsoft Word - toyoshima-deim2011.doc
DEIM Forum 2011 E9-4 252-0882 5322 252-0882 5322 E-mail: t09651yt, sashiori, kiyoki @sfc.keio.ac.jp CBIR A Meaning Recognition System for Sign-Logo by Color-Shape-Based Similarity Computations for Images
IPSJ SIG Technical Report Vol.2010-MPS-77 No /3/5 VR SIFT Virtual View Generation in Hallway of Cybercity Buildings from Video Sequen
VR 1 1 1 1 1 SIFT Virtual View Generation in Hallway of Cybercity Buildings from Video Sequences Sachiyo Yoshida, 1 Masami Takata 1 and Joe Kaduki 1 Appearance of Three-dimensional (3D) building model
IPSJ SIG Technical Report Vol.2013-CE-122 No.16 Vol.2013-CLE-11 No /12/14 Android 1,a) 1 1 GPS LAN 2 LAN Android,,, Android, HTML5 LAN 1. ICT(I
Android 1,a) 1 1 GPS LAN 2 LAN Android,,, Android, HTML5 LAN 1. ICT(Information and Communication Technology) (Google [2] [5] ) 2. Google 2.1 Google Google [2]( 1) Google Web, Google Web Google Chrome
_314I01BM浅谷2.indd
587 ネットワークの表現学習 1 1 1 1 Deep Learning [1] Google [2] Deep Learning [3] [4] 2014 Deepwalk [5] 1 2 [6] [7] [8] 1 2 1 word2vec[9] word2vec 1 http://www.ai-gakkai.or.jp/my-bookmark_vol31-no4 588 31 4 2016
[6] DoN DoN DDoN(Donuts DoN) DoN 4(2) DoN DDoN 3.2 RDoN(Ring DoN) 4(1) DoN 4(3) DoN RDoN 2 DoN 2.2 DoN PCA DoN DoN 2 DoN PCA 0 DoN 3. DoN
![[6] DoN DoN DDoN(Donuts DoN) DoN 4(2) DoN DDoN 3.2 RDoN(Ring DoN) 4(1) DoN 4(3) DoN RDoN 2 DoN 2.2 DoN PCA DoN DoN 2 DoN PCA 0 DoN 3. DoN](/thumbs/89/99418350.jpg "[6] DoN DoN DDoN(Donuts DoN) DoN 4(2) DoN DDoN 3.2 RDoN(Ring DoN) 4(1) DoN 4(3) DoN RDoN 2 DoN 2.2 DoN PCA DoN DoN 2 DoN PCA 0 DoN 3. DoN")
3 1,a) 1,b) 3D 3 3 Difference of Normals (DoN)[1] DoN, 1. 2010 Kinect[2] 3D 3 [3] 3 [4] 3 [5] 3 [6] [7] [1] [8] [9] [10] Difference of Normals (DoN) 48 8 [1] [6] DoN DoN 1 National Defense Academy a) em53035@nda.ac.jp
情報処理学会研究報告 IPSJ SIG Technical Report Vol.2011-MBL-57 No.27 Vol.2011-UBI-29 No /3/ A Consideration of Features for Fatigue Es
1 1 1 1 1 5 1 2 1 A Consideration of Features for Fatigue Estimation by Gait Analysis Using Accelerometer Hidekazu Higashi, 1 Tadashi Shigeoka, 1 Tsuyoshi Itokawa, 1 Teruaki Kitasuka 1 and Masayoshi Aritsugi
20mm 63.92% ConstantZoom U 5
29 30 2 13 16350926 20mm 63.92% ConstantZoom U 5 1 3 1.1...................................... 3 1.2................................. 4 2 8 2.1............... 8 2.2............................ 8 2.3..
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
2013 M
2013 M0110213 2013 : M0110213 3DCG 3DCG 3DCG 2D 3DCG 2D 1 1 3DCG 3D 1 1 1.1............................ 1 1.2............................... 10 2 11 2.1......................... 11 3 16 3.1..........................
2014 2
2014 2 Web Web 1 1 1 1.1................................... 1 1.2.................................... 1 1.3.............................. 1 1.4.................................... 2 1.5...................................
untitled
2007 55 2 235 254 c 2007 1 2 3 3 2007 6 12 2007 11 1 20 8 2 1. 2004 Sakata et al. 2004 1 610 0394 1 3 2 176 8525 2 42 1 3 525 8577 1 1 1 236 55 2 2007 2003 2004 Camurri et al. 1999 2002 2005 CG 1987 1
理工ジャーナル ２３‐１☆／１．外村
Yoshinobu TONOMURA Professor, Department of Media Informatics 1 10 YouTube 2 1900 100 1 3 2 3 3 3 1 2 3 4 90 1 90 MIT Project Athena 1983 1991 2 3 4 5 6 7 8 9 10 2 90 11 12 7 13 14 15 16 17 18 19 390 5
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
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
Microsoft Word - deim2011_new-ichinose-20110325.doc
DEIM Forum 2011 B7-4 252-0882 5322 E-mail: {t08099ai, kurabaya, kiyoki}@sfc.keio.ac.jp A Music Search Database System with a Selector for Impressive-Sections of Continuous Data Aya ICHINOSE Shuichi KURABAYASHI
(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
IPSJ SIG Technical Report Vol.2013-CVIM-187 No /5/30 1,a) 1,b), 1,,,,,,, (DNN),,,, 2 (CNN),, 1.,,,,,,,,,,,,,,,,,, [1], [6], [7], [12], [13]., [
![IPSJ SIG Technical Report Vol.2013-CVIM-187 No /5/30 1,a) 1,b), 1,,,,,,, (DNN),,,, 2 (CNN),, 1.,,,,,,,,,,,,,,,,,, [1], [6], [7], [12], [13]., [](/thumbs/94/121682292.jpg "IPSJ SIG Technical Report Vol.2013-CVIM-187 No /5/30 1,a) 1,b), 1,,,,,,, (DNN),,,, 2 (CNN),, 1.,,,,,,,,,,,,,,,,,, [1], [6], [7], [12], [13]., [")
,a),b),,,,,,,, (DNN),,,, (CNN),,.,,,,,,,,,,,,,,,,,, [], [6], [7], [], [3]., [8], [0], [7],,,, Tohoku University a) omokawa@vision.is.tohoku.ac.jp b) okatani@vision.is.tohoku.ac.jp, [3],, (DNN), DNN, [3],
Web Social Networking Service Virtual Private Network 84
Promising business utilized five senses information media through the Next Generation Network Toshio ASANO Next Generation Network 2004 11 2010 6,000 3,000 2006 12 2008 83 Web Social Networking Service
DEIM Forum 2014 B Twitter Twitter Twitter 2006 Twitter 201
DEIM Forum 2014 B2-4 305 8550 1 2 305 8550 1 2 E-mail: {yamaguchi,yamahei,satoh}@ce.slis.tsukuba.ac.jp Twitter Twitter 2 1 1. Twitter 2006 Twitter 2012 5 [1]Twitter RT RT Twitter Twitter RT Twitter 2 1
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
untitled
IT E- IT http://www.ipa.go.jp/security/ CERT/CC http://www.cert.org/stats/#alerts IPA IPA 2004 52,151 IT 2003 12 Yahoo 451 40 2002 4 18 IT 1/14 2.1 DoS(Denial of Access) IDS(Intrusion Detection System)
2010 : M0107189 3DCG 3 (3DCG) 3DCG 3DCG 3DCG S
2010 M0107189 2010 : M0107189 3DCG 3 (3DCG) 3DCG 3DCG 3DCG S 1 1 1.1............................ 1 1.2.............................. 4 2 5 2.1............................ 5 2.2.............................
Haiku Generation Based on Motif Images Using Deep Learning Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura Scho
Haiku Generation Based on Motif Images Using Deep Learning 1 2 2 2 Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura 2 1 1 School of Engineering Hokkaido University 2 2 Graduate
Real AdaBoost HOG 2009 3 A Graduation Thesis of College of Engineering, Chubu University Efficient Reducing Method of HOG Features for Human Detection based on Real AdaBoost Chika Matsushima ITS Graphics
Silhouette on Image Object Silhouette on Images Object 1 Fig. 1 Visual cone Fig. 2 2 Volume intersection method Fig. 3 3 Background subtraction Fig. 4
Image-based Modeling 1 1 Object Extraction Method for Image-based Modeling using Projection Transformation of Multi-viewpoint Images Masanori Ibaraki 1 and Yuji Sakamoto 1 The volume intersection method
本文6(599) (Page 601)
(MIRU2008) 2008 7 525 8577 1 1 1 E-mail: matsuzaki@i.ci.ritsumei.ac.jp, shimada@ci.ritsumei.ac.jp Object Recognition by Observing Grasping Scene from Image Sequence Hironori KASAHARA, Jun MATSUZAKI, Nobutaka
IPSJ SIG Technical Report Vol.2009-CVIM-167 No /6/10 Real AdaBoost HOG 1 1 1, 2 1 Real AdaBoost HOG HOG Real AdaBoost HOG A Method for Reducing
Real AdaBoost HOG 1 1 1, 2 1 Real AdaBoost HOG HOG Real AdaBoost HOG A Method for Reducing number of HOG Features based on Real AdaBoost Chika Matsushima, 1 Yuji Yamauchi, 1 Takayoshi Yamashita 1, 2 and
GUI(Graphical User Interface) GUI CLI(Command Line Interface) GUI
24 GUI(Graphical User Interface) GUI CLI(Command Line Interface) GUI 1 1 1.1 GUI................................... 1 1.2 GUI.................... 1 1.2.1.......................... 1 1.2.2...........................
Vol.55 No (Jan. 2014) saccess 6 saccess 7 saccess 2. [3] p.33 * B (A) (B) (C) (D) (E) (F) *1 [3], [4] Web PDF a m
![Vol.55 No (Jan. 2014) saccess 6 saccess 7 saccess 2. [3] p.33 * B (A) (B) (C) (D) (E) (F) *1 [3], [4] Web PDF a m](/thumbs/92/108648363.jpg "Vol.55 No (Jan. 2014) saccess 6 saccess 7 saccess 2. [3] p.33 * B (A) (B) (C) (D) (E) (F) *1 [3], [4] Web PDF a m")
Vol.55 No.1 2 15 (Jan. 2014) 1,a) 2,3,b) 4,3,c) 3,d) 2013 3 18, 2013 10 9 saccess 1 1 saccess saccess Design and Implementation of an Online Tool for Database Education Hiroyuki Nagataki 1,a) Yoshiaki
IPSJ-MUS
Vol.29-MUS-81 No.2 29/7/29 1 2 1 ground-truth RWC 22 16 Method for Calculating the Subjective-based Music Similarity Measure Yusuke Hiraga, 1 Yasunori Ohishi 2 and Kazuya Takeda 1 In this paper, we propose
DEIM Forum 2013 B6-3 MAP Web MAP Implementation and Ev
DEIM Forum 2013 B6-3 MAP 815 8540 4-9-1 815 8540 4-9-1 E-mail: 2ds11094e@s.kyushu-u.ac.jp, ushiama@design.kyushu-u.ac.jp Web MAP Implementation and Evaluation of A Browsing Interface for A Novel Using
. S T T [1][15] suffix tree BGM MIDI beat gather[10] Any 1 BANANA suffix tree[9] Fig. 1 Suffix tree of BANANA [9]. Beats[11
![. S T T [1][15] suffix tree BGM MIDI beat gather[10] Any 1 BANANA suffix tree[9] Fig. 1 Suffix tree of BANANA [9]. Beats[11](/thumbs/50/26157033.jpg ". S T T [1][15] suffix tree BGM MIDI beat gather[10] Any 1 BANANA suffix tree[9] Fig. 1 Suffix tree of BANANA [9]. Beats[11")
(EC2015) 2015 9 - -,a),b),c),,..,suffix tree Frequency-Based Key Component Extraction -Automatic Generation of Instruction Scores for Music Video Games- Kagawa Toshimune,a) Tezuka Hiroshi,b) Inaba Mari,c)
sigmusdemo.dvi
V IT Demonstrations: Introduction of Research by Young Researchers V Masatoshi Hamanaka Akira Nishimura Hiroshi Takaesu Shigeyuki Hirai Katsutoshi Itoyama Akiyuki Yoshino Shohei Kajiwara Nozomi Kigimoto
,,, 2 ( ), $[2, 4]$, $[21, 25]$, $V$,, 31, 2, $V$, $V$ $V$, 2, (b) $-$,,, (1) : (2) : (3) : $r$ $R$ $r/r$, (4) : 3
![,,, 2 ( ), $[2, 4]$, $[21, 25]$, $V$,, 31, 2, $V$, $V$ $V$, 2, (b) $-$,,, (1) : (2) : (3) : $r$ $R$ $r/r$, (4) : 3](/thumbs/91/104771717.jpg ",,, 2 ( ), $[2, 4]$, $[21, 25]$, $V$,, 31, 2, $V$, $V$ $V$, 2, (b) $-$,,, (1) : (2) : (3) : $r$ $R$ $r/r$, (4) : 3")
1084 1999 124-134 124 3 1 (SUGIHARA Kokichi),,,,, 1, [5, 11, 12, 13], (2, 3 ), -,,,, 2 [5], 3,, 3, 2 2, -, 3,, 1,, 3 2,,, 3 $R$ ( ), $R$ $R$ $V$, $V$ $R$,,,, 3 2 125 1 3,,, 2 ( ), $[2, 4]$, $[21, 25]$,
b n m, m m, b n 3
13th Annual Worlds of Flavor International Conference & Festival z x c v z x c v 2 b n m, m m, b n 3 . 0 1 2 3 4 5. 0 1 2 3 4 4 5 6 7 8 6 7 8 5 9 0 1 2 3 9 0 1 2 3 6 4 5 6 7 8 4 5 6 7 8 9 7 0 1 2 3 9 0
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.2013-HCI-152 No /3/13 1,a) 1,b) 2,c) / GPS Bluetooth(BT) WiFi BT WiFi 1. Bluetooth WiFi 1 / 1 2 a)
1,a) 1,b) 2,c) / GPS Bluetooth(BT) WiFi BT WiFi 1. Bluetooth WiFi 1 / 1 2 a) rtokuami@kwansei.ac.jp b) kono@kwansei.ac.jp c) nakamura@dl.kuis.kyoto-u.ac.jp / 2. Apple iphoto Google Picasa GPS GPS GPS [1][2]
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?
Run-Based Trieから構成される 決定木の枝刈り法
Run-Based Trie 2 2 25 6 Run-Based Trie Simple Search Run-Based Trie Network A Network B Packet Router Packet Filtering Policy Rule Network A, K Network B Network C, D Action Permit Deny Permit Network
KinecV2 2.2 Kinec Kinec [8] Kinec Kinec [9] KinecV1 3D [10] Kisikidis [11] Kinec Kinec Kinec 3 KinecV2 PC 1 KinecV2 Kinec PC Kinec KinecV2 PC KinecV2
![KinecV2 2.2 Kinec Kinec [8] Kinec Kinec [9] KinecV1 3D [10] Kisikidis [11] Kinec Kinec Kinec 3 KinecV2 PC 1 KinecV2 Kinec PC Kinec KinecV2 PC KinecV2](/thumbs/91/104918347.jpg "KinecV2 2.2 Kinec Kinec [8] Kinec Kinec [9] KinecV1 3D [10] Kisikidis [11] Kinec Kinec Kinec 3 KinecV2 PC 1 KinecV2 Kinec PC Kinec KinecV2 PC KinecV2")
Kinec Developmen of Moion Capure Sysem using Muliple Kinecs 1 1 1 Miyaake Jumpei 1 Ohubo Masakazu 1 Yoshida Kaori 1 1 1 Graduae School of Life Science and Sysems Enginnering, Kyushu Insiue of echnology
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
Vol. 48 No. 3 Mar Evaluation of Music-noise Assimilation Playback for Portable Audio Players Akifumi Inoue, Shohei Bise, Satoshi Ichimura and
Vol. 48 No. 3 Mar. 2007 1 Evaluation of Music-noise Assimilation Playback for Portable Audio Players Akifumi Inoue, Shohei Bise, Satoshi Ichimura and Yutaka Matsushita Though the population of portable
HASC2012corpus HASC Challenge 2010,2011 HASC2011corpus( 116, 4898), HASC2012corpus( 136, 7668) HASC2012corpus HASC2012corpus
HASC2012corpus 1 1 1 1 1 1 2 2 3 4 5 6 7 HASC Challenge 2010,2011 HASC2011corpus( 116, 4898), HASC2012corpus( 136, 7668) HASC2012corpus HASC2012corpus: Human Activity Corpus and Its Application Nobuo KAWAGUCHI,
[2] OCR [3], [4] [5] [6] [4], [7] [8], [9] 1 [10] Fig. 1 Current arrangement and size of ruby. 2 Fig. 2 Typography combined with printing
![[2] OCR [3], [4] [5] [6] [4], [7] [8], [9] 1 [10] Fig. 1 Current arrangement and size of ruby. 2 Fig. 2 Typography combined with printing](/thumbs/93/112070099.jpg "[2] OCR [3], [4] [5] [6] [4], [7] [8], [9] 1 [10] Fig. 1 Current arrangement and size of ruby. 2 Fig. 2 Typography combined with printing")
1,a) 1,b) 1,c) 2012 11 8 2012 12 18, 2013 1 27 WEB Ruby Removal Filters Using Genetic Programming for Early-modern Japanese Printed Books Taeka Awazu 1,a) Masami Takata 1,b) Kazuki Joe 1,c) Received: November
独立行政法人情報通信研究機構 Development of the Information Analysis System WISDOM KIDAWARA Yutaka NICT Knowledge Clustered Group researched and developed the infor
独立行政法人情報通信研究機構 KIDAWARA Yutaka NICT Knowledge Clustered Group researched and developed the information analysis system WISDOM as a research result of the second medium-term plan. WISDOM has functions that
B HNS 7)8) HNS ( ( ) 7)8) (SOA) HNS HNS 4) HNS ( ) ( ) 1 TV power, channel, volume power true( ON) false( OFF) boolean channel volume int
SOA 1 1 1 1 (HNS) HNS SOA SOA 3 3 A Service-Oriented Platform for Feature Interaction Detection and Resolution in Home Network System Yuhei Yoshimura, 1 Takuya Inada Hiroshi Igaki 1, 1 and Masahide Nakamura
(a) (b) 1 JavaScript Web Web Web CGI Web Web JavaScript Web mixi facebook SNS Web URL ID Web 1 JavaScript Web 1(a) 1(b) JavaScript & Web Web Web Webji
Webjig Web 1 1 1 1 Webjig / Web Web Web Web Web / Web Webjig Web DOM Web Webjig / Web Web Webjig: a visualization tool for analyzing user behaviors in dynamic web sites Mikio Kiura, 1 Masao Ohira, 1 Hidetake
LL 2
1 LL 2 100 1990 3 4 í().. 1986 1992.. 5 õ?? / / / /=/ / / Ì / 77/ / / / / / / ûý7/..... /////////////Ì 7/ / 7/ / / / / ûý7/..... / / / / / / / / Ì / Í/ / / / / / / / / ûý7/.. / : Ì / Í/ / / / / / / / /
& 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
Computer Security Symposium October ,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) [1] 1 Meiji U
![Computer Security Symposium October ,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) [1] 1 Meiji U](/thumbs/101/150229680.jpg "Computer Security Symposium October ,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) [1] 1 Meiji U")
Computer Security Symposium 017 3-5 October 017 1,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) 1. 017 5 [1] 1 Meiji University Graduate School of Advanced Mathematical Science