14) Ogihara ATM 15) ATM 10 16) 17),18) 1 4) 1 8),9) 10) 12) realadaboost 13) % 12) 2. 3 Gluhchev 19) 1 19) 2 10) 12) 3. 2 ID 1 8) 9),20) 2 2
|
|
- たしろう やまがた
- 6 years ago
- Views:
Transcription
1 1, realadaboost % Biometric Person Authentication Method Using Pen Holding Feature Daigo Muramats, 1, 1 Yuki Hashimoto 1 and Hiroyuki Ogata 1 We focus on a biometric person authentication method using features of pen holding style. The manner of holding pen can be distinctive among persons and be useful modality for person authentication, because the manner is affected by both the physical features and habitual behavior. In order to evaluate the efficiency, we extract several features from the pen-holding image, and fuse them for verification. In this paper, realadaboost algorithm is used for the fusion, and user-dependent threshold is applied for a decision making. The developed algorithm is evaluated using the database collected dorm 30 persons. The algorithm achieved an EER of 4.0% against the impersonation attacks. 1. 1) ATM 2) 1) 3) 4) 5) 6) 7) 1 1 Department of Electrical and Mechanical Engineering, Seikei University 1 Presently with The Institute of Science and Industrial Research, Osaka University 2 Graduate School of Science and Technology, Seikei University 1 1
2 14) Ogihara ATM 15) ATM 10 16) 17),18) 1 4) 1 8),9) 10) 12) realadaboost 13) % 12) 2. 3 Gluhchev 19) 1 19) 2 10) 12) 3. 2 ID 1 8) 9),20) 2 2
3 情報処理学会研究報告 䊕䊮㗔 Ꮐ 㗔 ฝ 㗔 ਅ 䊕䊮㗔 䊕䊮 ᜰ㗔 図 4 取得画像 図 3 撮影設定 図 2 ペン持ち方認証アルゴリズム 徴を抽出し 参照データとして登録する また本研究では 登録された本人データと 他人 データとのスコアを計算し これらのスコアを利用して認証時に必要なパラメータの学習 (a) (b) (c) (d) (e) (f) (h) (i) 設定を行っている 認証フェーズでは データ取得によって得られた画像から登録フェーズ同様特徴を抽出 し 抽出した特徴を用いて非類似度を計算する その後計算された非類似度を統合して認証 スコアを計算し しきい値と比較することで認証を行う 本章の残りの部分では これらの フェーズを構成する個々の処理について説明を行う 3.1 データ取得 カメラを用いてペンを持った手を撮影する 撮影方向はいくつか考えられるが ペン持ち 方の違いがわかりやすいよう本研究では図 3 に示すように ペンを持つ手を親指側から撮 影する これにより図 5 のような画像が取得される 親指側から撮影することにより 親指 の位置や人差指の曲げ方の違いに関係する特徴を画像から取得できる 3.2 特 徴 抽 出 (g) 取得された画像データから 個人性が現れると思われる特徴を抽出する 図 5 は異なる 9 図5 様々なペン持ち方 人から取得したペン持ち方の画像である これらの図よりペン持ち方にはかなり個人差があ 3 c 2011 Information Processing Society of Japan
4 ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) ( 7 ) ( 8 ) x y ( 9 ) 2 ( 10 ) - ( 11 ) x y ( 12 ) x y ( 13 ) x y ( 14 ) x y ( 15 ) x y ( 16 ) x y ( 17 ) ( 18 ) ( 19 ) ( 20 ) x y ( 1 ) ( 2 ) A B F a = (fa 1, fa 2,...fa Nv ), F b = (fb 1, fb 2,..., fb Nv ) n dis n(a, B) = fa n fb n, n = 1, 2,..., N v (1) N v = 31 I r (i, j), 1 i W,1 j H I v(i, j), 1 i WF,1 j H F (H < H F, W < W F ) dis thumb = 1 W H min (x,y) Region W i=1 j=1 H I r (i, j) I v (x + i, y + j) (2) Resion (x g, y g ) (x g W/2 10, y g H/2 10) (x g + W/2 + 10, y g + H/2 + 10) N dim = 33 4
5 id {1, 2,..., N ID } R (id) Q Dis(R (id) Step 1, Q) = {disn(r(id), Q)}N n=1 Dis(R (id), Q) Dis(R (id), Q ) = (dis 1 (R (id), Q ),..., dis Ndim (R (id), Q )) Step 2 dis n (R (id), Q ) = disn(r(id), Q) norm (id) n, n = 1, 2,..., N dim (3) [ 1, 1] S S(R (id), Q ); a, b) = (s 1 (R (id), Q ; a, b),..., s Ndim (R (id), Q ; a, b) g(s; a, b) g(s; a, b) = 1 s n(r (id), Q; a, b) = g(dis(r(id), Q); a, b) (4) exp( a(s b)) a, b [ 1, 1] Step 3 Step2 S s n n s n Score Ndim Score(R (id), Q; Θ) = n=1 (5) α ns n(r (id), Q; a, b) (6) Θ = {α n} N dim n=1 α n n Θ realadaboost 13) ) ) T hreshod id (c) = T h id + c dev id (7) T h id dev id id Z-norm 3) 3.6 Q id X X = { Accepted Rejected if Score(R (id), Q ) > T hreshold id (c) otherwise [step 1] [step 2] 1 [step 3] [step 4] [step 5] [step 6] step (30 8) = (8) 5
6 1 No. T h id dev id EER [%] id Gd (id) l, 1 id 30, 1 l 10 id Ad (id) k, 1 k K id, K id {40, 45} Gd (id) 1 R (id), 2 l 5 Gd (m) Gd (id) l l = Gd(id) 1 ; m id, 1 l 10 Q id Gd (id) j, 6 j 10 Ad (id) k, 1 k K i N G = 150 N I = False Reject Rate FRR False Accept Rage FAR Equal Error Rate EER F RR(T hreshold(c)) = 1 N G F AR(T hreshold(c)) = 1 N I id=1 l=6 K 10 i δ i=1 k=1 ( ) δ Score(Gd (id) 1, Gd (id) ; Θ) < T hreshold(c) ( Score(Gd (id) l ) 1, Ad (id) ; Θ) T hreshold(c) k δ(x) { 1 if x is true δ(x) =. (9) 0 otherwise EER : EER = F AR(T hreshold(c )) + F RR(T hreshold(c ))) 2 (10) where c = argmin F AR(T hreshold(c)) F RR(T hreshold(c)) (11) c % FAR 6
7 realadoboost 9 8 realadaboost 4.1% Znorm 1-3 Znorm ) 12) EER=5.6% 1 7 1) Jain, A. K., Flynn, P. and Ross, A. A.: Handbook of Biometrics, Springer Science+Business Media, LLC. (2008). 2) Vol.207, No.3, pp (2006). 3) Ross, A. A., Nandakumar, K. and Jain, A. K.: Handbook of Multibiometrics, Springer Science+Business Media, LLC. (2006). 4) Vol.21, No.1, pp (2010). 5) Brunelli, R. and Falavigna, D.: Person identification using multiple cues, Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol.17, No.10, pp (1995). 6) D Vol.J92-D, No.8, pp (2009). 7) Zhou, X., Bhanu, B. and Han, J.: Human Recognition at a Distance in Video by Integrating Face Profile and Gait (2005). 8) Munich, M.E. and Perona, P.: Visual Identification by Signature Tracking, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.25, No.2, pp (2003). 9) Yasuda, K., Muramatsu, D., Shirato, S. and Matsumoto, T.: Visual-based online signature verification using features extracted from video, Journal of Network and Computer Applications, Vol.33, pp (2010). 10) A Vol.J92-A, No.5, pp (2009). 11) Hashimoto, Y., Muramatsu, D. and Ogata, H.: Biometric person authentication method using features extracted from pen- holding style, Proc. SPIE, Vol ) Hashimoto, Y., Muramatsu, D. and Ogata, H.: 7
8 Vol.34, No.54, pp.1 4 (2010). 13) Schapire, R.E. and Singer, Y.: Improved Boosting Algorithms Using Confidencerated Predictions, Machine Learning, Vol.37, No.3, pp (1999). 14) Sidlauskas, D.P. and Tamer, S.: Hand Geometry Recognition, Handbook of Biometrics (Jain, A.K., Flynn, P. and Ross, A.A., eds.), Springer Science+Business Media, LLC., chapter5 (2008). 15) Ogihara, A., Matsumura, H. and Shiozaki, A.: Biometric verification using keystroke motion and key press timing for ATM user authentication, pp (2006). 16) : (2009) 17) Hangai, S. and Higuchi, T.: Writer Identification Using Finger-Bend in Writing Signature, Proc. BioAW2004, LNCS, Vol.3087, pp (2004). 18) Kamel, N., Sayeed, S. and Ellis, G.: Glove-Based Approach to Online Signature Verification, Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol.30, No.6, pp (2008). 19) Gluhchev, G., Savov, M., Boumbarov, O. and Vasileva, D.: A New Approach to Signature-Based Authentication, ICB, Lecture Notes in Computer Science, Vol.4642, Springer, pp (2007). 20) Shirato, S., Muramatsu, D. and Matsumoto, T.: Camera-based online signature verification system: effects of camera positions, World Automation Congress (WAC), 2010, pp.1 6 (2010). 21) Jain, A.K., Griess, F.D. and Connell, S.D.: On-line signature verification, Pattern Recognition, Vol.35, pp (2002). 8
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
More information(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
More information4. 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
More information3 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
More informationReal 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
More information第 1 回バイオメトリクス研究会 ( 早稲田大学 ) THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS Proceedings of Biometrics Workshop,169
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS Proceedings of Biometrics Workshop,169-8555 3-4-1,169-8555 3-4-1 E-mail: s hayashi@kom.comm.waseda.ac.jp, ohki@suou.waseda.jp Wolf
More informationIPSJ 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
More information1 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
More information(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
More information1 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
More information,,.,.,,.,.,.,.,,.,..,,,, 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
More informationIPSJ 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.
More informationMicrosoft 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
More information258 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.
More information28 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
More informationB 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
More informationIPSJ 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
More information& 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
More information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
More informationSobel Canny i
21 Edge Feature for Monochrome Image Retrieval 1100311 2010 3 1 3 3 2 2 7 200 Sobel Canny i Abstract Edge Feature for Monochrome Image Retrieval Naoto Suzue Content based image retrieval (CBIR) has been
More informationIPSJ 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
More informationa) 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
More information2). 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
More information顔画像を用いた個人認証システムの性能検討に関する研究
12 Research on performance examination of individual attestation system using face image 1010429 2001 2 5 1997 FaceIt The age using various biometrics for the attestation with the computer to attest the
More information1 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
More information情報処理学会研究報告 IPSJ SIG Technical Report Vol.2013-CVIM-186 No /3/15 EMD 1,a) SIFT. SIFT Bag-of-keypoints. SIFT SIFT.. Earth Mover s Distance
EMD 1,a) 1 1 1 SIFT. SIFT Bag-of-keypoints. SIFT SIFT.. Earth Mover s Distance (EMD), Bag-of-keypoints,. Bag-of-keypoints, SIFT, EMD, A method of similar image retrieval system using EMD and SIFT Hoshiga
More information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
More informationIPSJ SIG Technical Report Vol.2010-SLDM-144 No.50 Vol.2010-EMB-16 No.50 Vol.2010-MBL-53 No.50 Vol.2010-UBI-25 No /3/27 Twitter IME Twitte
Twitter 1 1 1 IME Twitter 2009 12 15 2010 2 1 13590 4.83% 8.16% 2 3 Web 10 45% Relational Analysis between User Context and Input Word on Twitter Yutaka Arakawa, 1 Shigeaki Tagashira 1 and Akira Fukuda
More information人工知能学会研究会資料 SIG-KBS-B Analysis of Voting Behavior in One Night Werewolf 1 2 Ema Nishizaki 1 Tomonobu Ozaki Graduate School of Integrated B
人工知能学会研究会資料 SIG-KBS-B508-09 Analysis of Voting Behavior in One Night Werewolf 1 2 Ema Nishizaki 1 Tomonobu Ozaki 2 1 1 Graduate School of Integrated Basic Sciences, Nihon University 2 2 College of Humanities
More information29 jjencode JavaScript
Kochi University of Technology Aca Title jjencode で難読化された JavaScript の検知 Author(s) 中村, 弘亮 Citation Date of 2018-03 issue URL http://hdl.handle.net/10173/1975 Rights Text version author Kochi, JAPAN http://kutarr.lib.kochi-tech.ac.jp/dspa
More informationQ [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
More informationVol.53 No (Mar. 2012) 1, 1,a) 1, 2 1 1, , Musical Interaction System Based on Stage Metaphor Seiko Myojin 1, 1,a
1, 1,a) 1, 2 1 1, 3 2 1 2011 6 17, 2011 12 16 Musical Interaction System Based on Stage Metaphor Seiko Myojin 1, 1,a) Kazuki Kanamori 1, 2 Mie Nakatani 1 Hirokazu Kato 1, 3 Sanae H. Wake 2 Shogo Nishida
More informationIPSJ 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,
More informationFig. 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
More informationIPSJ 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
More informationTHE 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
More informationDPA,, 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
More information,,,,,,,,,,,,,,,,,,, 976%, i
20 Individual Recognition using positions of facial parts 1115081 2009 3 5 ,,,,,,,,,,,,,,,,,,, 976%, i Abstract Individual Recognition using positions of facial parts YOSHIHIRO Arisawa A facial recognition
More informationIPSJ 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
More information知能と情報, Vol.30, No.5, pp
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
More information2. CABAC CABAC CABAC 1 1 CABAC Figure 1 Overview of CABAC 2 DCT 2 0/ /1 CABAC [3] 3. 2 値化部 コンテキスト計算部 2 値算術符号化部 CABAC CABAC
H.264 CABAC 1 1 1 1 1 2, CABAC(Context-based Adaptive Binary Arithmetic Coding) H.264, CABAC, A Parallelization Technology of H.264 CABAC For Real Time Encoder of Moving Picture YUSUKE YATABE 1 HIRONORI
More informationIPSJ SIG Technical Report Secret Tap Secret Tap Secret Flick 1 An Examination of Icon-based User Authentication Method Using Flick Input for
1 2 3 3 1 Secret Tap Secret Tap Secret Flick 1 An Examination of Icon-based User Authentication Method Using Flick Input for Mobile Terminals Kaoru Wasai 1 Fumio Sugai 2 Yosihiro Kita 3 Mi RangPark 3 Naonobu
More informationIPSJ 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
More information2006 [3] Scratch Squeak PEN [4] PenFlowchart 2 3 PenFlowchart 4 PenFlowchart PEN xdncl PEN [5] PEN xdncl DNCL 1 1 [6] 1 PEN Fig. 1 The PEN
PenFlowchart 1,a) 2,b) 3,c) 2015 3 4 2015 5 12, 2015 9 5 PEN & PenFlowchart PEN Evaluation of the Effectiveness of Programming Education with Flowcharts Using PenFlowchart Wataru Nakanishi 1,a) Takeo Tatsumi
More informationDuplicate Near Duplicate Intact Partial Copy Original Image Near Partial Copy Near Partial Copy with a background (a) (b) 2 1 [6] SIFT SIFT SIF
Partial Copy Detection of Line Drawings from a Large-Scale Database Weihan Sun, Koichi Kise Graduate School of Engineering, Osaka Prefecture University E-mail: sunweihan@m.cs.osakafu-u.ac.jp, kise@cs.osakafu-u.ac.jp
More informationIPSJ 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
More information(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
More informationxx/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
More informationVol.54 No (July 2013) [9] [10] [11] [12], [13] 1 Fig. 1 Flowchart of the proposed system. c 2013 Information
Vol.54 No.7 1937 1950 (July 2013) 1,a) 2012 11 1, 2013 4 5 1 Similar Sounds Sentences Generator Based on Morphological Analysis Manner and Low Class Words Masaaki Kanakubo 1,a) Received: November 1, 2012,
More informationID 3) 9 4) 5) ID 2 ID 2 ID 2 Bluetooth ID 2 SRCid1 DSTid2 2 id1 id2 ID SRC DST SRC 2 2 ID 2 2 QR 6) 8) 6) QR QR QR QR
Vol. 51 No. 11 2081 2088 (Nov. 2010) 2 1 1 1 which appended specific characters to the information such as identification to avoid parity check errors, before QR Code encoding with the structured append
More informationInput 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
More informationIPSJ 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
More information7,, i
23 Research of the authentication method on the two dimensional code 1145111 2012 2 13 7,, i Abstract Research of the authentication method on the two dimensional code Karita Koichiro Recently, the two
More informationGaze 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)
More informationIPSJ SIG Technical Report Vol.2014-GN-90 No.16 Vol.2014-CDS-9 No.16 Vol.2014-DCC-6 No /1/24 1,a) 2,b) 2,c) 1,d) QUMARION QUMARION Kinect Kinect
1,a) 2,b) 2,c) 1,d) QUMARION QUMARION Kinect Kinect Using a Human-Shaped Input Device for Remote Pose Instruction Yuki Tayama 1,a) Yoshiaki Ando 2,b) Misaki Hagino 2,c) Ken-ichi Okada 1,d) Abstract: There
More informationIPSJ SIG Technical Report Vol.2014-CDS-10 No /5/ Intuitive appliance control method based on high-accurate indoor localization system
1 1 1 1 Intuitive appliance control method based on high-accurate indoor localization system Jun Komeda 1 Yutaka Arakawa 1 Morihiko Tamai 1 Keiichi Yasumoto 1 Abstract: In our home, the increase of appliances
More information12) NP 2 MCI MCI 1 START Simple Triage And Rapid Treatment 3) START MCI c 2010 Information Processing Society of Japan
1 1, 2 1, 2 1 A Proposal of Ambulance Scheduling System Based on Electronic Triage Tag Teruhiro Mizumoto, 1 Weihua Sun, 1, 2 Keiichi Yasumoto 1, 2 and Minoru Ito 1 For effective life-saving in MCI (Mass
More informationIPSJ 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
More informationActionScript Flash Player 8 ActionScript3.0 ActionScript Flash Video ActionScript.swf swf FlashPlayer AVM(Actionscript Virtual Machine) Windows
ActionScript3.0 1 1 YouTube Flash ActionScript3.0 Face detection and hiding using ActionScript3.0 for streaming video on the Internet Ryouta Tanaka 1 and Masanao Koeda 1 Recently, video streaming and video
More information21 Key Exchange method for portable terminal with direct input by user
21 Key Exchange method for portable terminal with direct input by user 1110251 2011 3 17 Diffie-Hellman,..,,,,.,, 2.,.,..,,.,, Diffie-Hellman, i Abstract Key Exchange method for portable terminal with
More information20 Method for Recognizing Expression Considering Fuzzy Based on Optical Flow
20 Method for Recognizing Expression Considering Fuzzy Based on Optical Flow 1115084 2009 3 5 3.,,,.., HCI(Human Computer Interaction),.,,.,,.,.,,..,. i Abstract Method for Recognizing Expression Considering
More information1(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
More information( ) [1] [4] ( ) 2. [5] [6] Piano Tutor[7] [1], [2], [8], [9] Radiobaton[10] Two Finger Piano[11] Coloring-in Piano[12] ism[13] MIDI MIDI 1 Fig. 1 Syst
情報処理学会インタラクション 2015 IPSJ Interaction 2015 15INT014 2015/3/7 1,a) 1,b) 1,c) Design and Implementation of a Piano Learning Support System Considering Motivation Fukuya Yuto 1,a) Takegawa Yoshinari 1,b) Yanagi
More information生体認証システムにおける情報漏洩対策技術の研究動向
ATM 1 IC ATM ATM IC IC ATM E-mail: masataka.suzuki@boj.or.jp E-mail: inuma.manabu@aist.go.jp E-mail: a-otsuka@aist.go.jp //2010.4 229 1. 2004 ATM PC ATM FISC FISC [2009] FISC [2009] 35-1 ATM IC ATM ATM
More information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
More informationA 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,
More informationIPSJ 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
More informationOptical 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
More informationVol.11-HCI-15 No. 11//1 Xangle 5 Xangle 7. 5 Ubi-WA Finger-Mount 9 Digitrack 11 1 Fig. 1 Pointing operations with our method Xangle Xa
Vol.11-HCI-15 No. 11//1 GUI 1 1 1, 1 GUI Graphical User Interface Xangle Xangle A Pointing Method Using Accelerometers for Graphical User Interfaces Tatsuya Horie, 1 Takuya Katayama, 1 Tsutomu Terada 1,
More informationHP cafe HP of A A B of C C Map on N th Floor coupon A cafe coupon B Poster A Poster A Poster B Poster B Case 1 Show HP of each company on a user scree
LAN 1 2 3 2 LAN WiFiTag WiFiTag LAN LAN 100% WiFi Tag An Improved Determination Method with Multiple Access Points for Relative Position Estimation Using Wireless LAN Abstract: We have proposed a WiFiTag
More information1 4 4 [3] SNS 5 SNS , ,000 [2] c 2013 Information Processing Society of Japan
SNS 1,a) 2 3 3 2012 3 30, 2012 10 10 SNS SNS Development of Firefighting Knowledge Succession Support SNS in Tokyo Fire Department Koutarou Ohno 1,a) Yuki Ogawa 2 Hirohiko Suwa 3 Toshizumi Ohta 3 Received:
More informationIPSJ SIG Technical Report Vol.2017-MUS-116 No /8/24 MachineDancing: 1,a) 1,b) 3 MachineDancing MachineDancing MachineDancing 1 MachineDan
MachineDancing: 1,a) 1,b) 3 MachineDancing 2 1. 3 MachineDancing MachineDancing 1 MachineDancing MachineDancing [1] 1 305 0058 1-1-1 a) s.fukayama@aist.go.jp b) m.goto@aist.go.jp 1 MachineDancing 3 CG
More information1 7.35% 74.0% linefeed point c 200 Information Processing Society of Japan
1 2 3 Incremental Linefeed Insertion into Lecture Transcription for Automatic Captioning Masaki Murata, 1 Tomohiro Ohno 2 and Shigeki Matsubara 3 The development of a captioning system that supports the
More information1 Table 1: Identification by color of voxel Voxel Mode of expression Nothing Other 1 Orange 2 Blue 3 Yellow 4 SSL Humanoid SSL-Vision 3 3 [, 21] 8 325
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
More informationIPSJ SIG Technical Report PIN(Personal Identification Number) An Examination of Icon-based User Authentication Method for Mobile Terminals Fum
1 2 1 3 PIN(Personal Identification Number) An Examination of Icon-based User Authentication Method for Mobile Terminals Fumio Sugai, 1 Masami Ikeda, 2 Naonobu Okazaki 1 and Mi RangPark 3 In recent years,
More informationIPSJ SIG Technical Report Vol.2013-GN-87 No /3/ Research of a surround-sound field adjustmen system based on loudspeakers arrangement Ak
1 1 3 Research of a surround-sound field adjustmen system based on loudspeakers arrangement Akiyama Daichi 1 Kanai Hideaki 1 Abstract: In this paper, we propose a presentation method that does not depend
More informationIPSJ 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
More information[1] SBS [2] SBS Random Forests[3] Random Forests ii
Random Forests 2013 3 A Graduation Thesis of College of Engineering, Chubu University Proposal of an efficient feature selection using the contribution rate of Random Forests Katsuya Shimazaki [1] SBS
More informationIPSJ SIG Technical Report Vol.2016-CE-137 No /12/ e β /α α β β / α A judgment method of difficulty of task for a learner using simple
1 2 3 4 5 e β /α α β β / α A judgment method of difficulty of task for a learner using simple electroencephalograph Katsuyuki Umezawa 1 Takashi Ishida 2 Tomohiko Saito 3 Makoto Nakazawa 4 Shigeichi Hirasawa
More informationgengo.dvi
4 97.52% tri-gram 92.76% 98.49% : Japanese word segmentation by Adaboost using the decision list as the weak learner Hiroyuki Shinnou In this paper, we propose the new method of Japanese word segmentation
More information& 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
More informationVol.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
More informationIPSJ SIG Technical Report Vol.2015-CVIM-196 No /3/6 1,a) 1,b) 1,c) U,,,, The Camera Position Alignment on a Gimbal Head for Fixed Viewpoint Swi
1,a) 1,b) 1,c) U,,,, The Camera Position Alignment on a Gimbal Head for Fixed Viewpoint Swiveling using a Misalignment Model Abstract: When the camera sets on a gimbal head as a fixed-view-point, it is
More information(a) (b) 2 2 (Bosch, IR Illuminator 850 nm, UFLED30-8BD) ( 7[m] 6[m]) 3 (PointGrey Research Inc.Grasshopper2 M/C) Hz (a) (b
(MIRU202) 202 8 AdrianStoica 89 0395 744 89 0395 744 Jet Propulsion Laboratory 4800 Oak Grove Drive, Pasadena, CA 909, USA E-mail: uchino@irvs.ait.kyushu-u.ac.jp, {yumi,kurazume}@ait.kyushu-u.ac.jp 2 nearest
More informationNo. 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?
More informationIS1-09 第 回画像センシングシンポジウム, 横浜,14 年 6 月 2 Hough Forest Hough Forest[6] Random Forest( [5]) Random Forest Hough Forest Hough Forest 2.1 Hough Forest 1 2.2
IS1-09 第 回画像センシングシンポジウム, 横浜,14 年 6 月 MI-Hough Forest () E-mail: ym@vision.cs.chubu.ac.jphf@cs.chubu.ac.jp Abstract Hough Forest Random Forest MI-Hough Forest Multiple Instance Learning Bag Hough Forest
More informationModal Phrase MP because but 2 IP Inflection Phrase IP as long as if IP 3 VP Verb Phrase VP while before [ MP MP [ IP IP [ VP VP ]]] [ MP [ IP [ VP ]]]
30 4 2016 3 pp.195-209. 2014 N=23 (S)AdvOV (S)OAdvV 2 N=17 (S)OAdvV 2014 3, 2008 Koizumi 1993 3 MP IP VP 1 MP 2006 2002 195 Modal Phrase MP because but 2 IP Inflection Phrase IP as long as if IP 3 VP Verb
More informationThe 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.
More information21 e-learning Development of Real-time Learner Detection System for e-learning
21 e-learning Development of Real-time Learner Detection System for e-learning 1100349 2010 3 1 e-learning WBT (Web Based training) e-learning LMS (Learning Management System) LMS WBT e-learning e-learning
More information(2003) (Suzuki, T. and Goto, Y., 2006) 2006
3 400-8511 4-3-11 153-8505 4-6-1 3 004-8585 1 5 4-1 16 7 3 (2003) 14 15 3 16 7 3 (Suzuki, T. and Goto, Y., 2006) 2006 1) 16 7.13 16 43,679 2006 12 31 77.96km 2 11.5km 14.7km 300m 2007 134 16 7.13 2005
More information1: A/B/C/D Fig. 1 Modeling Based on Difference in Agitation Method artisoc[7] A D 2017 Information Processing
1,a) 2,b) 3 Modeling of Agitation Method in Automatic Mahjong Table using Multi-Agent Simulation Hiroyasu Ide 1,a) Takashi Okuda 2,b) Abstract: Automatic mahjong table refers to mahjong table which automatically
More information100 SDAM SDAM Windows2000/XP 4) SDAM TIN ESDA K G G GWR SDAM GUI
30 99 112 2006 SDAM SDAM SDAM SDAM 1950 1960 1970 SPSS SAS Microsoft Excel ArcView GIS 2002 ArcExplorer 1) MANDARA 2) GIS 2000 TNTLite 3) GIS 100 SDAM SDAM Windows2000/XP 4) SDAM TIN ESDA K G G GWR SDAM
More informationA 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
More informationVol. 48 No. 4 Apr LAN TCP/IP LAN TCP/IP 1 PC TCP/IP 1 PC User-mode Linux 12 Development of a System to Visualize Computer Network Behavior for L
Vol. 48 No. 4 Apr. 2007 LAN TCP/IP LAN TCP/IP 1 PC TCP/IP 1 PC User-mode Linux 12 Development of a System to Visualize Computer Network Behavior for Learning to Associate LAN Construction Skills with TCP/IP
More information2 ( ) i
25 Study on Rating System in Multi-player Games with Imperfect Information 1165069 2014 2 28 2 ( ) i ii Abstract Study on Rating System in Multi-player Games with Imperfect Information Shigehiko MORITA
More informationIPSJ 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.
More informationIPSJ 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
More information2.2 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
More informationJFE.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
More informationIPSJ 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,
More information