本文6(599) (Page 601)
|
|
|
- まいえ たみや
- 5 years ago
- Views:
Transcription
1 (MIRU2008) 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 SHIMADA, and Hiromi TANAKA College of Information Science and Engineering, Ritsumeikan University Nojihigashi 1 1 1, Kusatsu-shi, Shiga, Japan matsuzaki@i.ci.ritsumei.ac.jp, shimada@ci.ritsumei.ac.jp Abstract The aim of this paper is the simultaneous restoration of incorrectly segmented the grasped object and grasping hand from the complex background and the recognition of the grasping method class. Key words Object recognition, Grasping method, Image interpolation 1. [1] SIFT [2] HoG [3] ( ) Sato [4] [5] [6] [7] [8] Napier 1 Power grasp() Presicion() 2 [7] 1 Napier () [8] 623
![[9] [10] (BPLP ) [11] [12] ( PCA) BPLP](/docs-images/94/118251234/images/2-0.jpg "2. 2. 1 [8] 15 6 6 [8] a) - (Power")
2 [9] [10] (BPLP ) [11] [12] ( PCA) BPLP [8] [8] a) - (Power Grip-Standard Type,PoS) (Power Grip-Index Extension Type,PoI) c) - (Parallel Mild Flexion Grip,PMF) 4 - (Parallel Mild Flexion Grip,PMF) d) - (Tip Grip,Tip) 5 - (Tip Grip,Tip) e) -- (Tripod Grip-Standard Type,Tpd) 6 -- (Tripod Grip-Standard Type,Tpd) f) - (Lateral Grip,Lat) 7 - (Lateral Grip,Lat) 2 - (Power Grip-Standard Type,PoS) b) - (Power Grip-Index Extension Type,PoI)
3 8 1. : 2. : : v h 4. : 2 v 2h 2. 3 PCA X =(x 1 x 2 x 3 x p) p = v h ((1) ). ( 60 ) 2. 4 (BPLP [11] (2) ). ˆp = argmin (E ˆp x) T Σ(E ˆp x) (1) ˆp:p: x: Σ: [12] BPLP x 2 x i E i, E i ˆp k 3 E i ˆp k x 4 ( R k ) 0 x ˆx k+1 5 ˆx k+1 E i, E i ˆp k 6 (3) (5) (2) (3) ˆp k = E T ˆx k (2) ˆx k+1 = R k E ip k+1 +(I R k ) x (3) 9 ˆx k :k ˆp k :k E i:i I: R k :k ( 1 0 ) 625
4 2. 5 D D D(C k )= Σ p i= 1 ( x C i ˆx k i ) 2 (4) 10 ˆp k (1) Σ=I R k ˆp BPLP () :: : : : : BPLP C k :k x i: i C ˆx k i :k i [0,1] () (5) D D (C k )= Σ p i= 1 ( xi ˆxiC k ) 2 /A i(c k ) 2 (5) A i(c k ): C k i (8) D D (C k )= Σ p i=1,j=1 (S 1 ij ( x (C i ˆx k ) (C i )( x j ˆx k ) j ) (6) S 1 ij : 2 (i j) 626
5 2. 6 D( D D ) ( ) if : else if: * (PoS) - (PoI) (PMF) (Tip) (Lat) ( ) ( ) ( ) - (Tpd) ( ) ( ) 1 15 : : : : 1 ( ) 2 BPLP (4) ( ) 3 (5) ( ) 4 (6) ( )
![(1)(2) PoS PoI PMF Tip Tpd Lat (3) PoI PMF Tip PoS Tpd Lat 18 (4) PoS PoI Tpd Lat 4. 6 24 3 [1] P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, Proc.](/docs-images/94/118251234/images/6-1.jpg "on IEEE Conf. on CVPR Vol.1, pp.511-518, 2001. [2] G. Csurka, C.R. Dance, L. Fan, and C. Bray, Visual categorization with bags of keypoints, Proc. of IEEE Conf. on ECCV, pp. 1-22, 2004. [3] N.")
6 2 ( ) (1) (2) (3) (4) PoS( ) 27(31) 38(44) 83(100) 45(56) PoI () 67(75) 53(75) 65(75) 89(100) PMF() 97(100) 94(100) 98(100) 97(100) Tip() 84(100) 81(94) 9(0) 55(69) Tpd() 85(94) 85(100) 78(94) 38(38) Lat( ) 44(44) 42(56) 30(31) 8(0) 2 () 2 (1)(2) (3) (3) (1)(2) PoS PoI PMF Tpd Tip Lat 18 (2)(3) 18 ( ) 18 (3) Tip Pos Tpd Lat PMF PoS Tpd Tip Lat PMF Lat 19 Lat PMF Lat PMF Lat 19 : : : () (4) (4) (1)(2) PoS PoI PMF Tip Tpd Lat (3) PoI PMF Tip PoS Tpd Lat 18 (4) PoS PoI Tpd Lat [1] P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, Proc. on IEEE Conf. on CVPR Vol.1, pp , [2] G. Csurka, C.R. Dance, L. Fan, and C. Bray, Visual categorization with bags of keypoints, Proc. of IEEE Conf. on ECCV, pp. 1-22, [3] N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, Proc. of IEEE Conf. on CVPR, pp , [4] Y. Sato and T. Nagai, Learning of Object Concept Through Function and Shape, IEEE Region 10 Conf.on TENCON 2006, pp.1-4, Nov [5],,,, PRMU , pp.13-18, Feb [6],, IE , PRMU , MVE , pp.53-58, Jul [7] J. Napier, The prehensile movements of the human hand, J. Bone and Joint Surgery, 38B,4, pp , Nov [8] Noriko Kamakura,,, May [9] A. Imai, N. Shimada and Y. Shirai, 3-D Hand Posture Recognition by Training Contour Variation, Proc. of 6th Int. Conf. on Automatic Face and Gesture Recognition, pp , May [10] Y. Hamada, N. Shimada and Y. Shirai, Hand Shape Estimation under Complex Backgrounds for Sign Language Recognition, Proc. of 6th Int. Conf. on Automatic Face and Gesture Recognition, pp , May [11], BPLP, D-II, Vol.J85-D-II, No.3, pp Mar [12],,,,, Vol. J89-D, No. 4,pp , Apr
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
(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
Duplicate Near Duplicate Intact Partial Copy Original Image Near Partial Copy Near Partial Copy with a background (a) (b) 2 1 [6] SIFT SIFT SIF
![Duplicate Near Duplicate Intact Partial Copy Original Image Near Partial Copy Near Partial Copy with a background (a) (b) 2 1 [6] SIFT SIFT SIF](/thumbs/82/86406952.jpg "Duplicate 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
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
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. E-mail: {ytamura,takai,tkato,tm}@vision.kuee.kyoto-u.ac.jp Abstract Current Wave Pattern Analysis for Anomaly
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
IS1-09 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 2 Hough Forest Hough Forest[6] Random Forest( [5]) Random Forest Hough Forest Hough Forest 2.1 Hough Forest 1 2.2](/thumbs/91/106196100.jpg "IS1-09 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
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
1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2
CHLAC 1 2 3 3,. (CHLAC), 1).,.,, CHLAC,.,. Suspicious Behavior Detection based on CHLAC Method Hideaki Imanishi, 1 Toyohiro Hayashi, 2 Shuichi Enokida 3 and Toshiaki Ejima 3 We have proposed a method for
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
A Graduation Thesis of College of Engineering, Chubu University Pose Estimation by Regression Analysis with Depth Information Yoshiki Agata
2011 3 A Graduation Thesis of College of Engineering, Chubu University Pose Estimation by Regression Analysis with Depth Information Yoshiki Agata CG [2] [3][4] 3 3 [1] HOG HOG TOF(Time Of Flight) iii
& 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
,,.,.,,.,.,.,.,,.,..,,,, 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
請求記号：DVD 70- -1 栄光のフィレンツェ・ルネサンス １ 夜明け 55分
DVD 291- -482 64 DVD 520- -434 170 DVD 520- -435 173 DVD 520- -436 178 DVD 520- -437 94 DVD 520- -438 183 DVD 602.164- -508 38 DVD 70- -1 55 DVD 70- -2 55 DVD 70- -3 55 DVD 70- -4 55 DVD 70- -5 55 DVD
2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server
![2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server](/thumbs/91/107223763.jpg "2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server")
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,
,,,,,,,,,,,,,,,,,,, 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
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 (PCA) 3 2 P.Viola 2) Viola AdaBoost 1 Viola OpenCV 3) Web OpenCV T.L.Berg PCA kpca LDA k-means 4) Berg 95% Berg Web k-means k-means
Web, Web k-means 62% Associating Faces and Names in Web Photo News Akio Kitahara and Keiji Yanai We propose a system which extracts faces and person names from news articles with photographs on the Web
kut-paper-template.dvi
26 Discrimination of abnormal breath sound by using the features of breath sound 1150313 ,,,,,,,,,,,,, i Abstract Discrimination of abnormal breath sound by using the features of breath sound SATO Ryo
1., 1 COOKPAD 2, Web.,,,,,,.,, [1]., 5.,, [2].,,.,.,, 5, [3].,,,.,, [4], 33,.,,.,,.. 2.,, 3.., 4., 5., ,. 1.,,., 2.,. 1,,
![1., 1 COOKPAD 2, Web.,,,,,,.,, [1]., 5.,, [2].,,.,.,, 5, [3].,,,.,, [4], 33,.,,.,,.. 2.,, 3.., 4., 5., ,. 1.,,., 2.,. 1,,](/thumbs/50/25936361.jpg "1., 1 COOKPAD 2, Web.,,,,,,.,, [1]., 5.,, [2].,,.,.,, 5, [3].,,,.,, [4], 33,.,,.,,.. 2.,, 3.., 4., 5., ,. 1.,,., 2.,. 1,,")
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.,, 464 8601 470 0393 101 464 8601 E-mail: matsunagah@murase.m.is.nagoya-u.ac.jp, {ide,murase,hirayama}@is.nagoya-u.ac.jp,
[1] SBS [2] SBS Random Forests[3] Random Forests ii
![[1] SBS [2] SBS Random Forests[3] Random Forests ii](/thumbs/74/70333192.jpg "[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
(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
2003/3 Vol. J86 D II No.3 2.3. 4. 5. 6. 2. 1 1 Fig. 1 An exterior view of eye scanner. CCD [7] 640 480 1 CCD PC USB PC 2 334 PC USB RS-232C PC 3 2.1 2
![2003/3 Vol. J86 D II No.3 2.3. 4. 5. 6. 2. 1 1 Fig. 1 An exterior view of eye scanner. CCD [7] 640 480 1 CCD PC USB PC 2 334 PC USB RS-232C PC 3 2.1 2](/thumbs/42/22941906.jpg "2003/3 Vol. J86 D II No.3 2.3. 4. 5. 6. 2. 1 1 Fig. 1 An exterior view of eye scanner. CCD [7] 640 480 1 CCD PC USB PC 2 334 PC USB RS-232C PC 3 2.1 2")
Curved Document Imaging with Eye Scanner Toshiyuki AMANO, Tsutomu ABE, Osamu NISHIKAWA, Tetsuo IYODA, and Yukio SATO 1. Shape From Shading SFS [1] [2] 3 2 Department of Electrical and Computer Engineering,
35_3_9.dvi
180 Vol. 35 No. 3, pp.180 185, 2017 Image Recognition by Deep Learning Hironobu Fujiyoshi and Takayoshi Yamashita Chubu University 1. 1990 2000 Scale-Invariant Feature Transform SIFT Histogram of Oriented
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
求人面接資料PPT
Hair Salon TV etc. 250" 250" 200" 200" 150" 150" 100" 100" 50" 50" 0" 0" Nov)13" Dec)13" Jan)14" Feb)14" Mar)14" Apr)14" May)14" Jun)14" Jul)14" Dec)12" Jan)13" Feb)13" Mar)13" Apr)13"
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
IPSJ SIG Technical Report 1, Instrument Separation in Reverberant Environments Using Crystal Microphone Arrays Nobutaka ITO, 1, 2 Yu KITANO, 1
1, 2 1 1 1 Instrument Separation in Reverberant Environments Using Crystal Microphone Arrays Nobutaka ITO, 1, 2 Yu KITANO, 1 Nobutaka ONO 1 and Shigeki SAGAYAMA 1 This paper deals with instrument separation
258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS 2 3 4 5 2. 2.1 3 1) GPS Global Positioning System
Vol. 52 No. 1 257 268 (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver.
Google Goggles [1] Google Goggles Android iphone web Google Goggles Lee [2] Lee iphone () [3] [4] [5] [6] [7] [8] [9] [10] :
![Google Goggles [1] Google Goggles Android iphone web Google Goggles Lee [2] Lee iphone () [3] [4] [5] [6] [7] [8] [9] [10] :](/thumbs/91/105231422.jpg "Google Goggles [1] Google Goggles Android iphone web Google Goggles Lee [2] Lee iphone () [3] [4] [5] [6] [7] [8] [9] [10] :")
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.,, 182-8585 1-5-1 E-mail: {maruya-t,akiyama-m}@mm.inf.uec.ac.jp, yanai@cs.uec.ac.jp SURF Bag-of-Features
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.,,
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.,, 464 8601 470 0393 101 464 8601 E-mail: matsunagah@murase.m.is.nagoya-u.ac.jp, {ide,murase,hirayama}@is.nagoya-u.ac.jp,
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
(MIRU2010) Geometric Context Randomized Trees Geometric Context Rand
(MIRU2010) 2010 7 Geometric Context Randomized Trees 487-8501 1200 E-mail: {fukuta,ky}@vision.cs.chubu.ac.jp, hf@cs.chubu.ac.jp Geometric Context Randomized Trees 10 3, Geometric Context, Abstract Image
paper.dvi
23 Study on character extraction from a picture using a gradient-based feature 1120227 2012 3 1 Google Street View Google Street View SIFT 3 SIFT 3 y -80 80-50 30 SIFT i Abstract Study on character extraction
bag-of-words bag-of-keypoints Web bagof-keypoints Nearest Neighbor SVM Nearest Neighbor SIFT Nearest Neighbor bag-of-keypoints Nearest Neighbor SVM 84
Bag-of-Keypoints Web G.Csurka bag-of-keypoints Web Bag-of-keypoints SVM 5.% Web Image Classification with Bag-of-Keypoints Taichi joutou and Keiji yanai Recently, need for generic image recognition is
LBP 2 LBP 2. 2 Local Binary Pattern Local Binary pattern(lbp) [6] R
![LBP 2 LBP 2. 2 Local Binary Pattern Local Binary pattern(lbp) [6] R](/thumbs/86/93333816.jpg "LBP 2 LBP 2. 2 Local Binary Pattern Local Binary pattern(lbp) [6] R")
DEIM Forum 24 F5-4 Local Binary Pattern 6 84 E-mail: {tera,kida}@ist.hokudai.ac.jp Local Binary Pattern (LBP) LBP 3 3 LBP 5 5 5 LBP improved LBP uniform LBP.. Local Binary Pattern, Gradient Local Auto-Correlations,,,,
,,,,., C Java,,.,,.,., ,,.,, i
24 Development of the programming s learning tool for children be derived from maze 1130353 2013 3 1 ,,,,., C Java,,.,,.,., 1 6 1 2.,,.,, i Abstract Development of the programming s learning tool for children
(3.6 ) (4.6 ) 2. [3], [6], [12] [7] [2], [5], [11] [14] [9] [8] [10] (1) Voodoo 3 : 3 Voodoo[1] 3 ( 3D ) (2) : Voodoo 3D (3) : 3D (Welc
![(3.6 ) (4.6 ) 2. [3], [6], [12] [7] [2], [5], [11] [14] [9] [8] [10] (1) Voodoo 3 : 3 Voodoo[1] 3 ( 3D ) (2) : Voodoo 3D (3) : 3D (Welc](/thumbs/65/54056306.jpg "(3.6 ) (4.6 ) 2. [3], [6], [12] [7] [2], [5], [11] [14] [9] [8] [10] (1) Voodoo 3 : 3 Voodoo[1] 3 ( 3D ) (2) : Voodoo 3D (3) : 3D (Welc")
1,a) 1,b) Obstacle Detection from Monocular On-Vehicle Camera in units of Delaunay Triangles Abstract: An algorithm to detect obstacles by using a monocular on-vehicle video camera is developed. Since
JFE.dvi
,, Department of Civil Engineering, Chuo University Kasuga 1-13-27, Bunkyo-ku, Tokyo 112 8551, JAPAN E-mail : atsu1005@kc.chuo-u.ac.jp E-mail : kawa@civil.chuo-u.ac.jp SATO KOGYO CO., LTD. 12-20, Nihonbashi-Honcho
橡上野先生訂正2
(SIS) NII) 101-8430 tel 03-4212-2516 E-mail ueno@nii.ac.jp 1 NII 2 (symbiosis) 2 (parasitism) 2 Knowledge Creation The Symbiotic partnership of University, Government and Industry, Proc. Information Environment
Sobel 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
VRSJ-SIG-MR_okada_79dce8c8.pdf
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. 630-0192 8916-5 E-mail: {kaduya-o,takafumi-t,goshiro,uranishi,miyazaki,kato}@is.naist.jp,.,,.,,,.,,., CG.,,,
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 4 2 3 4 3 [12] 2 3 4 5 1 1 [5, 6, 7] [5, 6] [7] 1 [8] 1 1 [9] 1 [10, 11] [10] [11] 1 [13, 14] [13] [14] [13, 14] [10, 11, 13, 14] 1 [12]
![2 4 2 3 4 3 [12] 2 3 4 5 1 1 [5, 6, 7] [5, 6] [7] 1 [8] 1 1 [9] 1 [10, 11] [10] [11] 1 [13, 14] [13] [14] [13, 14] [10, 11, 13, 14] 1 [12]](/thumbs/39/20193688.jpg "2 4 2 3 4 3 [12] 2 3 4 5 1 1 [5, 6, 7] [5, 6] [7] 1 [8] 1 1 [9] 1 [10, 11] [10] [11] 1 [13, 14] [13] [14] [13, 14] [10, 11, 13, 14] 1 [12]")
Walking Person Recognition by Matching Video Fragments Masashi Nishiyama, Mayumi Yuasa, Tomokazu Wakasugi, Tomoyuki Shibata, Osamu Yamaguchi ( ), Corporate Research and Development Center, TOSHIBA Corporation
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
% 2 3 [1] Semantic Texton Forests STFs [1] ( ) STFs STFs ColorSelf-Simlarity CSS [2] ii
![% 2 3 [1] Semantic Texton Forests STFs [1] ( ) STFs STFs ColorSelf-Simlarity CSS [2] ii](/thumbs/91/106152959.jpg "% 2 3 [1] Semantic Texton Forests STFs [1] ( ) STFs STFs ColorSelf-Simlarity CSS [2] ii")
2012 3 A Graduation Thesis of College of Engineering, Chubu University High Accurate Semantic Segmentation Using Re-labeling Besed on Color Self Similarity Yuko KAKIMI 2400 90% 2 3 [1] Semantic Texton
: : : : ) ) 1. d ij f i e i x i v j m a ij m f ij n x i =
1 1980 1) 1 2 3 19721960 1965 2) 1999 1 69 1980 1972: 55 1999: 179 2041999: 210 211 1999: 211 3 2003 1987 92 97 3) 1960 1965 1970 1985 1990 1995 4) 1. d ij f i e i x i v j m a ij m f ij n x i = n d ij
5D1 SY0004/14/ SICE 1, 2 Dynamically Consistent Motion Design of Humanoid Robots even at the Limit of Kinematics Kenya TANAKA 1 and Tomo
5D1 SY4/14/-485 214 SICE 1, 2 Dynamically Consistent Motion Design of Humanoid Robots even at the Limit of Kinematics Kenya TANAKA 1 and Tomomichi SUGIHARA 2 1 School of Engineering, Osaka University 2-1
2 Fig D human model. 1 Fig. 1 The flow of proposed method )9)10) 2.2 3)4)7) 5)11)12)13)14) TOF 1 3 TOF 3 2 c 2011 Information
1 1 2 TOF 2 (D-HOG HOG) Recall D-HOG 0.07 HOG 0.16 Pose Estimation by Regression Analysis with Depth Information Yoshiki Agata 1 and Hironobu Fujiyoshi 1 A method for estimating the pose of a human from
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
SURF,,., 55%,.,., SURF(Speeded Up Robust Features), 4 (,,, ), SURF.,, 84%, 96%, 28%, 32%.,,,. SURF, i
24 SURF Recognition of Facial Expression Based on SURF 1130402 2013 3 1 SURF,,., 55%,.,., SURF(Speeded Up Robust Features), 4 (,,, ), SURF.,, 84%, 96%, 28%, 32%.,,,. SURF, i Abstract Recognition of Facial
3D UbiCode (Ubiquitous+Code) RFID ResBe (Remote entertainment space Behavior evaluation) 2 UbiCode Fig. 2 UbiCode 2. UbiCode 2. 1 UbiCode UbiCode 2. 2
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS HCG HUMAN COMMUNICATION GROUP SYMPOSIUM. UbiCode 243 0292 1030 E-mail: {ubicode,koide}@shirai.la, {otsuka,shirai}@ic.kanagawa-it.ac.jp
初めに：
2 Copyrightc2008 JETRO. All rights reserved. FAX 03-5572-7044 ...5...6 (1)...7... 11... 11...12...14...15...15...16...17...18 (4)...21 (5)...21 (6)...23 4 Copyrightc2008 JETRO. All rights reserved. 5 Copyrightc2008
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
(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
(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
![(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](/thumbs/91/105641706.jpg "(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
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
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
ポテトサラダ 調理手順. ジャガイモの皮を剥き 茹でて柔らかくする. 茹でたジャガイモをボウルに移し つぶしながら切った材料も加えていく 材料. きゅうりは小口切りに 玉ねぎは薄切りにする ハムも細かく切っておく 5. 最後に塩胡椒 マヨネーズで味付けをして完成 ジャガイモ きゅうり ハム 玉ねぎ
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. 6 860 70 0393 0 6 860 E-mail: hayashiy@murase.m.is.nagoya-u.ac.jp, kdoman@sist.chukyo-u.ac.jp, {ide,murase}@is.nagoya-u.ac.jp,
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],
IPSJ 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
, (GPS: Global Positioning Systemg),.,, (LBS: Local Based Services).. GPS,.,. RFID LAN,.,.,.,,,.,..,.,.,,, i
25 Estimation scheme of indoor positioning using difference of times which chirp signals arrive 114348 214 3 6 , (GPS: Global Positioning Systemg),.,, (LBS: Local Based Services).. GPS,.,. RFID LAN,.,.,.,,,.,..,.,.,,,
01-譴ｶ蜴・-8.fm
No.109, 2006 * Comparative Study in the Sentence of Students' Reports Produced between PC-Mail and Mobile Phone-Mail Naomasa SASAKI and Kumiko ISHIKAWA* Accepted April 30, 2006 : 1,100 : Abstract : This
2. 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
![2. 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](/thumbs/92/108208872.jpg "2. 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
a) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a
a) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a), Tetsuo SAWARAGI, and Yukio HORIGUCHI 1. Johansson
contents
3 3 4 5 6 7 7 8 8 9 9 10 10 10 11 11 12 13 13 14 14 14 14 14 14 contents 3 3 4 5 6 7 7 8 8 9 9 10 10 10 11 11 12 13 13 14 14 14 14 14 14 01 1 22 3 3 44 studies 1 2 Hiroshima Univ. ACTIVITIES campus
dews2004-final.dvi
DEWS2004 I-10-04 606 8501 E-mail: {akahoshi,hirotanaka,tanaka}@dl.kuis.kyoto-u.ac.jp A Basic Study on Ubiquitous Hypermedia Model Yuhei AKAHOSHI, Hiroya TANAKA, and Katsumi TANAKA Graduate School of Informatics,
(MIRU2009) cuboid cuboid SURF 6 85% Web. Web Abstract Extracting Spatio-te
(MIRU2009) 2009 7 182 8585 1 5 1 E-mail: noguchi-a@mm.cs.uec.ac.jp, yanai@cs.uec.ac.jp cuboid cuboid SURF 6 85% Web. Web Abstract Extracting Spatio-temporal Local Features Considering Consecutiveness of
立命館21_松本先生.indd
2143-552010 1 2 () 1 2 3 456 78- Key Words 1 3 12007 2 3 508 19992008 1 23 4 43 2120107 4 2008 1992 2003 2005 1989 2008 4 2 2-1 10 4 4 6 5 4 5 2946 1 155 11 41 44 45 4 2-2 2003 1 21 2 1 3 4 5 6 7 3 2120107
立命館20_服部先生.indd
20 93-105 2010 2 () ' - 1 ( ) ( ' 2005) Key Words 2 1967 93 20 2010 1 94 1 ' 2002 2 2 1996 1996 1999 2 2 2 1993 1999 4 1 2 1985 1989 1986 1994 4 1 2 1 1 4 2 4 4 1 4 1966 4 10-1970 20 1993-1972 95 20 2010
1996 2000 2004 1984 2005 7150 000 9 500 9 4 13 10 95 11 11 12 20002004 9 70
14 2006 1 Key Words 2002 3 1 2 3 3 1 2 3 1969 1987 69 1996 2000 2004 1984 2005 7150 000 9 500 9 4 13 10 95 11 11 12 20002004 9 70 14 2006 1 15 71 72 1 22 6 32 9 200 6 3 1 2 2000 10 1 2003 10 2005 6 5 4
立命館16_坂下.indd
1669-792008 1 - ' 85- -108 ' Key words 1 2 2003 69 1620082 5 3 1990 1997 4 5 2001 1307 6 7 1 1 1 1996 100 2 1997 71 3 1998 71 4 1999 96 5 2000 95 6 2001 145 7 2002 191 8 2003 174 9 2004 120 10 2005 122
立命館人間科学研究No.10
1 77 5 Key words 1 23 3 11417 14310045 20022004 2 2003 20022005 20022004 2 10200511 3 2003 1152003 59 1995 3 32003 19932002 20032003 2005 20052005 4 1997 2000521986 2001 42001 3 1981 6 1 7 5 1000248 1632647
立命館21_川端先生.indd
21 119-132 2010 ( ) ' Key Words 119 21 2010 7 1962 2001 2001 2007 1982 1988 1997 2007 1997 1998 1863 1880 1 1998 1998 2001 1599 120 121 1599 1695 8 1695 1714 4 1714 1715 5 1715 100 1812 9 1812 1864 2001
立命館14_前田.indd
1499-1122007 1 ) ) ) ) ) (1) -- ) ) ) ) ) ) ( ) ) ' ) ) ) ) - 1) 2)' 3) Key words 19811994 1721 99 1420073 100 20012004 2005 2004 2002 33 34 10 1987 45 20002003 2002 1 1 2000 1 1 2001 2 200341 12004 2
立命館17_坂下.indd
1793-1052008 1 () -- -- - Key words 1 93 1720088 2 3 2003 15 1996 50 3040 2 3 4050 50 10 1980 1995 1950 1958 1968 1972 94 95 1987 4 4 70 3 3 1 2000 2001 4 1720088 96 2001 2003 2 1978 1990 1997 130 2 3
