Convolutional Neural Network A Graduation Thesis of College of Engineering, Chubu University Investigation of feature extraction by Convolution
|
|
- たかよし たつざわ
- 5 years ago
- Views:
Transcription
1 Convolutional Neural Network A Graduation Thesis of College of Engineering, Chubu University Investigation of feature extraction by Convolutional Neural Network Fukui Hiroshi
2 [1] ( ) Deep Learning Deep Learning [2] Drop out[3] Deep Learning Histgram of Oriented Gradients(HOG) [4] Scale Invariant Feature Transform(SIFT) [5] Deep Learning Convolutional Neural Network(CNN) [6] CNN 1989 ii
3 CNN 5 CNN Deep Learning CNN CNN CNN iii
4 Deep Learning Convolutional Neural Network CNN iv
5 40 41 v
6 CNN [7][8] CNN CNN MNIST Dataset vi
7 3.12 CNN epoch vii
8 viii
9 () w 1
10 1.1. f 1.1: x i w i f y x w d w i y (1.1) ( d ) y = f w i x i i=1 (1.1) y f x i w i X (1.2) X 2 (1.2) X 0 1 X 0-1 2
11 (X > 0) f (X) = 1 (X 0) (1.2) 1.2 (1.3) : f (X) = exp ( gx) (1.3) (1.3) g g
12 f(x) (1.4) f (X) = gf (X) (1 f (X)) (1.4) [9] s 1 θ s 2 s 3 a 1 a 2 w j s i w ij a j s d a J S A O 1.3: d J 1 w ij w j θ θ 4
13 1.3. A = [a 1, a 2,..., a j,..., a J ] T w = [w 1, w 2,..., w j ] O (1.5) J O = f w j1 a j θ (1.5) j=1 O T (1.6) (1.7) w t+1 = w t + η (T O) A (1.6) θ t+1 = θ t + η (T O) (1.7) (1.6) (1.7) t η η > 0 (1.6) (1.7) 0% x 1 x (a) 2 x 1 x 2 1.4(b) c d J 5
14 1.3. x 2 x 2 x 1 x 1 1.4: (1.9) E N = 1 N c (T k O k ) 2 (1.8) 2 n=1 k=1 6
15 1.3. θ γ s 1 s 3 a 1 o 1 o 2 s i w ij a j w ij o k a J s d o c S A O 1.5: E N (1.9) E N η w t+1 = w t η E N w t (1.9)
16 (1.10) E n = 1 c (T k O k ) 2 (1.10) 2 k=1 (1.10) E n E n η (1.11) w t+1 = w t η E n w t (1.11) mini batch mini batch mini batch 1 mini batch M (1.12) E m (1.13) E M = 1 M c (T k O k ) 2 (1.12) 2 m=1 k=1 8
17 1.3. w t+1 = w t η E M w t (1.13) θ γ i j k k d c c S A O T 1.6: d c S i A j O k T k f w ij w jk j (1.14) w ij S j θ j 9
18 1.3. d A j = f w ij S i + θ j (1.14) i= (1.10) (1.10) O k O k (1.15) T δ k E n O k = (O k T k ) = δ k (1.15) O k P k = j w jk A j + γ k O k (1.16) O k = f (P k ) (1.16) (1.16) k > 2 (1.17) (1.17) P k 1 P j O k = exp (P k) j exp (P j ) (1.17) E n E n E n (1.18) E n = E njk w jk (1.18) 10
19 1.3. E njk E njk (1.18), (1.19) E njk = E n w jk = E n O k O k w jk = E n O k O k P k P k w jk (1.19) (1.19) E njk (1.20) E njk = E n w jk = δ k O k (1 O k ) A j (1.20) E nij E nij (1.21) E nij = E n w ij = E n A j A j P j P j W ij = E n O k P k A j P j O k P k A j P j w ij ( = δ k O k (1 O k ) W jk ) A j (1 A j ) S i (1.21) k (1.20) (1.21) (1.20) (1.20) (1.11) (1.22) (1.23) w t+1 jk = w t jk η δ k O k (1 O k ) A j (1.22) γ t+1 j = γ t j η δ k O k (1 O k ) (1.23) 11
20 1.3. (1.24) (1.25) ( wij t+1 = wij t η δ k O k (1 O k ) W jk ) A j (1 A j ) S i (1.24) θ t+1 j = θ t j η k ( k δ k O k (1 O k ) W jk ) A j (1 A j ) (1.25) epoch 1epoch t t = 1 C 1 t = 0 C 2 y (1.3) y(x, w) p(c 1 x) p(c 2 x) 1 y(x, w) (1.26) p (t x, w) = y (x, w) t {1 y (x, w)} 1 t (1.26) (1.26) (1.27) N E = {t n ln y n + (1 t n ) ln (1 y n )} (1.27) n=1 12
21 1.3. Simard [10] (1.28) N C E = t nc ln y c (1.28) n=1 c=1 13
22 2 Deep Learning Deep Learning 2.1 O T O k i j j j k k d c c S A O T 2.1:
23 2.1. Convolutional Neural Network Convolutional Neural Network(CNN) CNN CNN 2.1 Convolutional Neural Network CNN CNN Hubel Wisel [11] Hubel 2.2(a) Fukushima 2.2(b) Neocognitron [8] Neocognitron CNN Neocognitron 2.2: CNN [7][8] CNN CNN 15
24 2.1. Convolutional Neural Network 2.3 CNN 2.3: CNN CNN 16
25 2.1. Convolutional Neural Network CNN Hubel n x n y n w n w n x, n y (2.1) n x = n x n w + 1 n y = n y n w + 1 (2.1) Hubel CNN 2.4 P h i h (2.2) i P h = max i P h i (2.2) 2 2 (2.3) n x = n x /2 n y = n y /2 (2.3) 17
26 2.1. Convolutional Neural Network P P 1 P 2 P 3 P 4 2.4: CNN 2.5 Full-Connect Full-Connect 18
27 2.1. Convolutional Neural Network 2.5: CNN (2.1) (2.3) n w n w n w n w Full-Connect 2.6 CNN 19
28 2.1. Convolutional Neural Network 2.6: CNN 20
29 3 CNN CNN CNN 2 2 MNIST Dataset CNN 3.1 MNIST Dataset 3.1 MNIST Dataset MNIST Dataset ,000 10,000 10, pixel 21
30 : MNIST Dataset 22
31 CNN Full-Connect CNN epoch CNN CNN
32 : 24
33 epoch 2 epoch 3.3: 3.4(a) 3.4(b) 3.4(a) 3.4(b) 25
34 Convolutional Neural Network Multi Layer Perceptron Cross entropy e epoch 100 Convolutional Neural Network Multi Layer Perceptron Miss rate[%] epoch :
35 3.3. epoch : 3.4(a) epoch
36 3.4. CNN 100 Convolutional Neural Network Multi Layer Perceptron 1000 Convolutional Neural Network Multi Layer Perceptron Miss rate[%] 10 Cross entropy epoch 1e epoch 3.6: 3.4 CNN CNN CNN CNN 28
37 3.4. CNN CNN CNN : CNN
38 3.4. CNN 3.1: (a) 3.8(c) CNN (MLP) 3.8(a) 3.8(c) CNN (a) 3.8(c) 10%
39 3.4. CNN [%] MLP CNN [pixel] [%] 15 MLP CNN [ ] [%] MLP CNN : 31
40 3.4. CNN 3.9: 32
41 3.4. CNN CNN CNN CNN 3.10: 3.11 CNN CNN 5pixel 33
42 3.4. CNN CNN : 3.13(a) CNN 3.13(b) 3.13(b) 3.13(a) 3.13(b) CNN CNN 34
43 3.4. CNN 3.2 CNN CNN 3.2 CNN 3.2: 2pixel 2pixel 20 MLP CNN CNN CNN epoch 3.13 epoch 3.13 CNN CNN epoch 3.2 CNN epoch 35
44 3.4. CNN Parallel Shift Learning CNN Rotation Learning CNN No Random Learning CNN Cross entropy e-005 1e epoch 100 Parallel Shift Learning CNN Rotation Learning CNN No Random Learning CNN 10 Miss rate[%] epoch : CNN
45 3.4. CNN 3.13: epoch 37
46 Convolutional Neural Network 1 2 Deep Learning Convolutional Neural Network Convolutional Neural Network Deep Learning Convolutional Neural Network Convolutional Neural Network 3 Convolutional Neural Network Convolutional Neural Network Convolutional Neural Network Convolutional Neural Network 38
47 Convolutional Neural Network 39
48 40
49 [1] D. Rumelhart, G. Hintont, and R. Williams, Learning representations by backpropagating errors, Nature, pp , [2] G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Improving neural networks by preventing co-adaptation of feature detectors, CoRR, vol.abs/ , [3] A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems 25, pp , [4] N. Dalal, and B. Triggs, Histograms of oriented gradients for human detection, International Conference on Computer Vision & Pattern Recognition, vol.2, pp , [5] D. G. Lowe, Object recognition from local scale-invariant features, Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2, pp.1150, IEEE Computer Society, [6] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, Backpropagation applied to handwritten zip code recognition, Neural Computation, vol.1, pp , [7] J. W. Kimball, Kimball s biology pages,, 2000, biology2/ultranet/visualprocessing.html. 41
50 [8] K. Fukushima, and S. Miyake, Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position, Pattern Recognition, vol.15, no.6, pp , [9] F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain, Psychological Review, vol.65, no.6, pp , [10] S. Patrice, V. Bernard, L. Yann, and D. John S, Tangent Prop: a formalism for specifying selected invariances in adaptive networks, NIPS, pp , [11] B. Y. D. H. Hubel, and A. D. T. N. Wiesel, Receptive fields, binocular interaction and functional architecture in the cats visual cortex, The Journal of physiology, vol.160, pp ,
51 Convolutional Neural Network ()
untitled
c ILSVRC LeNet 1. 1 convolutional neural network 1980 Fukushima [1] [2] 80 LeCun (back propagation) LeNet [3, 4] LeNet 2. 2.1 980 8579 6 6 01 okatani@vision.is.tohoku.ac.jp (simple cell) (complex cell)
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 informationA 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
More informationIPSJ 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],
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 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 information18 2 20 W/C W/C W/C 4-4-1 0.05 1.0 1000 1. 1 1.1 1 1.2 3 2. 4 2.1 4 (1) 4 (2) 4 2.2 5 (1) 5 (2) 5 2.3 7 3. 8 3.1 8 3.2 ( ) 11 3.3 11 (1) 12 (2) 12 4. 14 4.1 14 4.2 14 (1) 15 (2) 16 (3) 17 4.3 17 5. 19
More informationuntitled
i ii iii iv v 43 43 vi 43 vii T+1 T+2 1 viii 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 a) ( ) b) ( ) 51
More information2
1 2 3 4 5 6 7 8 9 10 I II III 11 IV 12 V 13 VI VII 14 VIII. 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 _ 33 _ 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 VII 51 52 53 54 55 56 57 58 59
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 informationyoo_graduation_thesis.dvi
200 3 A Graduation Thesis of College of Engineering, Chubu University Keypoint Matching of Range Data from Features of Shape and Appearance Yohsuke Murai 1 1 2 2.5D 3 2.1 : : : : : : : : : : : : : : :
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 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 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 informationfiš„v2.dvi
(2001) 49 1 9 21 * 2000 12 27 2001 3 19 (PCA) (MDS) MDS Young Yamane AIT MDS MDS Makioka 2 MDS MDS PCA, MDS. 1. 140 Yes * 351 0198 2 1 Figures 1 and 3: Reprinted with permission from Young, P. M. and Yamane,
More information% 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
More information<4D6963726F736F667420506F776572506F696E74202D208376838C835B83938365815B835683878393312E707074205B8CDD8AB78382815B83685D>
i i vi ii iii iv v vi vii viii ix 2 3 4 5 6 7 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
More informationSC-85X2取説
I II III IV V VI .................. VII VIII IX X 1-1 1-2 1-3 1-4 ( ) 1-5 1-6 2-1 2-2 3-1 3-2 3-3 8 3-4 3-5 3-6 3-7 ) ) - - 3-8 3-9 4-1 4-2 4-3 4-4 4-5 4-6 5-1 5-2 5-3 5-4 5-5 5-6 5-7 5-8 5-9 5-10 5-11
More informationi ii iii iv v vi vii ( ー ー ) ( ) ( ) ( ) ( ) ー ( ) ( ) ー ー ( ) ( ) ( ) ( ) ( ) 13 202 24122783 3622316 (1) (2) (3) (4) 2483 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) 11 11 2483 13
More informationSICE東北支部研究集会資料(2017年)
307 (2017.2.27) 307-8 Deep Convolutional Neural Network X Detecting Masses in Mammograms Based on Transfer Learning of A Deep Convolutional Neural Network Shintaro Suzuki, Xiaoyong Zhang, Noriyasu Homma,
More information2017 (413812)
2017 (413812) Deep Learning ( NN) 2012 Google ASIC(Application Specific Integrated Circuit: IC) 10 ASIC Deep Learning TPU(Tensor Processing Unit) NN 12 20 30 Abstract Multi-layered neural network(nn) has
More informationDeep Learning Deep Learning GPU GPU FPGA %
2016 (412825) Deep Learning Deep Learning GPU GPU FPGA 16 1 16 69% Abstract Recognition by DeepLearning attracts attention, because of its high recognition accuracy. Lots of learning is necessary for Deep
More informationこれわかWord2010_第1部_100710.indd
i 1 1 2 3 6 6 7 8 10 10 11 12 12 12 13 2 15 15 16 17 17 18 19 20 20 21 ii CONTENTS 25 26 26 28 28 29 30 30 31 32 35 35 35 36 37 40 42 44 44 45 46 49 50 50 51 iii 52 52 52 53 55 56 56 57 58 58 60 60 iv
More informationパワポカバー入稿用.indd
i 1 1 2 2 3 3 4 4 4 5 7 8 8 9 9 10 11 13 14 15 16 17 19 ii CONTENTS 2 21 21 22 25 26 32 37 38 39 39 41 41 43 43 43 44 45 46 47 47 49 52 54 56 56 iii 57 59 62 64 64 66 67 68 71 72 72 73 74 74 77 79 81 84
More informationこれでわかるAccess2010
i 1 1 1 2 2 2 3 4 4 5 6 7 7 9 10 11 12 13 14 15 17 ii CONTENTS 2 19 19 20 23 24 25 25 26 29 29 31 31 33 35 36 36 39 39 41 44 45 46 48 iii 50 50 52 54 55 57 57 59 61 63 64 66 66 67 70 70 73 74 74 77 77
More information35_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
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 information平成18年版 男女共同参画白書
i ii iii iv v vi vii viii ix 3 4 5 6 7 8 9 Column 10 11 12 13 14 15 Column 16 17 18 19 20 21 22 23 24 25 26 Column 27 28 29 30 Column 31 32 33 34 35 36 Column 37 Column 38 39 40 Column 41 42 43 44 45
More informationIII
III 1 1 2 1 2 3 1 3 4 1 3 1 4 1 3 2 4 1 3 3 6 1 4 6 1 4 1 6 1 4 2 8 1 4 3 9 1 5 10 1 5 1 10 1 5 2 12 1 5 3 12 1 5 4 13 1 6 15 2 1 18 2 1 1 18 2 1 2 19 2 2 20 2 3 22 2 3 1 22 2 3 2 24 2 4 25 2 4 1 25 2
More informationiii iv v vi vii viii ix 1 1-1 1-2 1-3 2 2-1 3 3-1 3-2 3-3 3-4 4 4-1 4-2 5 5-1 5-2 5-3 5-4 5-5 5-6 5-7 6 6-1 6-2 6-3 6-4 6-5 6 6-1 6-2 6-3 6-4 6-5 7 7-1 7-2 7-3 7-4 7-5 7-6 7-7 7-8 7-9 7-10 7-11 8 8-1
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 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知的学習認識システム特論9.key
shouno@uec.ac.jp 1 http://www.slideshare.net/pfi/deep-learning-22350063 1960 1970 1980 1990 2000 2010 Perceptron (Rosenblatt 57) Linear Separable (Minski & Papert 68) SVM (Vapnik 95) Neocognitron
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 informationエクセルカバー入稿用.indd
i 1 1 2 3 5 5 6 7 7 8 9 9 10 11 11 11 12 2 13 13 14 15 15 16 17 17 ii CONTENTS 18 18 21 22 22 24 25 26 27 27 28 29 30 31 32 36 37 40 40 42 43 44 44 46 47 48 iii 48 50 51 52 54 55 59 61 62 64 65 66 67 68
More informationi
14 i ii iii iv v vi 14 13 86 13 12 28 14 16 14 15 31 (1) 13 12 28 20 (2) (3) 2 (4) (5) 14 14 50 48 3 11 11 22 14 15 10 14 20 21 20 (1) 14 (2) 14 4 (3) (4) (5) 12 12 (6) 14 15 5 6 7 8 9 10 7
More information01_.g.r..
I II III IV V VI VII VIII IX X XI I II III IV V I I I II II II I I YS-1 I YS-2 I YS-3 I YS-4 I YS-5 I YS-6 I YS-7 II II YS-1 II YS-2 II YS-3 II YS-4 II YS-5 II YS-6 II YS-7 III III YS-1 III YS-2
More informationuntitled
I...1 II...2...2 III...3...3...7 IV...15...15...20 V...23...23...24...25 VI...31...31...32...33...40...47 VII...62...62...67 VIII...70 1 2 3 4 m 3 m 3 m 3 m 3 m 3 m 3 5 6 () 17 18 7 () 17 () 17 8 9 ()
More informationii iii iv CON T E N T S iii iv v Chapter1 Chapter2 Chapter 1 002 1.1 004 1.2 004 1.2.1 007 1.2.2 009 1.3 009 1.3.1 010 1.3.2 012 1.4 012 1.4.1 014 1.4.2 015 1.5 Chapter3 Chapter4 Chapter5 Chapter6 Chapter7
More information14 2 5
14 2 5 i ii Surface Reconstruction from Point Cloud of Human Body in Arbitrary Postures Isao MORO Abstract We propose a method for surface reconstruction from point cloud of human body in arbitrary postures.
More information1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 () - 1 - - 2 - - 3 - - 4 - - 5 - 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
More information色の類似性に基づいた形状特徴量CS-HOGの提案
IS3-04 第 18 回 画 像 センシングシンポジウム, 横 浜, 2012 年 6 月 CS-HOG CS-HOG : Color Similarity-based HOG feature Yuhi Goto, Yuji Yamauchi, Hironobu Fujiyoshi Chubu University E-mail: yuhi@vision.cs.chubu.ac.jp Abstract
More informationIPSJ 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
More information2008 : 80725872 1 2 2 3 2.1.......................................... 3 2.2....................................... 3 2.3......................................... 4 2.4 ()..................................
More information3 5 18 3 5000 1 2 7 8 120 1 9 1954 29 18 12 30 700 4km 1.5 100 50 6 13 5 99 93 34 17 2 2002 04 14 16 6000 12 57 60 1986 55 3 3 3 500 350 4 5 250 18 19 1590 1591 250 100 500 20 800 20 55 3 3 3 18 19 1590
More informationIPSJ SIG Technical Report Vol.2015-MPS-103 No.29 Vol.2015-BIO-42 No /6/24 Deep Convolutional Neural Network 1,a) 1,b),c) X CT (Computer Aided D
Deep Convolutional Neural Network 1,a) 1,b),c) X CT (Computer Aided Diagnosis : CAD) CAD Deep Convolutional Neural Network (DCNN) DCNN CT DCNN DCNN Support Vector Machine DCNN, Anaysis for Deep Convolutional
More informationDeep Learningとは
企画セッション 2 ディープラーニング 趣旨 : 応用 3 分野における Deep Learning( 深層学習 ) の研究の現状 画像 : 岡谷貴之 ( 東北大学 ) 画像認識分野でのディープラーニングの研究動向 音声 : 久保陽太郎 (NTT コミュニケーション科学基礎研究所 ) 音声認識分野における深層学習技術の研究動向 自然言語処理 : 渡邉陽太郎 ( 東北大学 ) 自然言語処理におけるディープラーニングの現状
More information活用ガイド (ソフトウェア編)
(Windows 95 ) ii iii iv NEC Corporation 1999 v P A R T 1 vi P A R T 2 vii P A R T 3 P A R T 4 viii P A R T 5 ix x P A R T 1 2 3 1 1 2 4 1 2 3 4 5 1 1 2 3 4 6 5 6 7 7 1 1 2 8 1 9 1 1 2 3 4 5 6 1 2 3 4
More information困ったときのQ&A
ii iii iv NEC Corporation 1997 v P A R T 1 vi vii P A R T 2 viii P A R T 3 ix x xi 1P A R T 2 1 3 4 1 5 6 1 7 8 1 9 1 2 3 4 10 1 11 12 1 13 14 1 1 2 15 16 1 2 1 1 2 3 4 5 17 18 1 2 3 1 19 20 1 21 22 1
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 informationHaiku 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
More information入門ガイド
ii iii iv NEC Corporation 1998 v P A R 1 P A R 2 P A R 3 T T T vi P A R T 4 P A R T 5 P A R T 6 P A R T 7 vii 1P A R T 1 2 2 1 3 1 4 1 1 5 2 3 6 4 1 7 1 2 3 8 1 1 2 3 9 1 2 10 1 1 2 11 3 12 1 2 1 3 4 13
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 information03_特集2_3校_0929.indd
MEDICAL IMAGING TECHNOLOGY Vol. 35 No. 4 September 2017 187 CT 1 1 convolutional neural network; ConvNet CT CT ConvNet 2D ConvNet CT ConvNet CT CT Med Imag Tech 35 4 : 187 193, 2017 1. CT MR 1 501-1194
More informationComputer 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 informationIPSJ SIG Technical Report Vol.2017-CVIM-207 No /5/10 GAN 1,a) 2,b) Generative Adversarial Networks GAN GAN CIFAR-10 10% GAN GAN Stacked GAN Sta
1,a) 2,b) Generative Adversarial Networks CIFAR-10 10% Stacked Stacked 8.9% CNN 1. ILSVRC 1000 50000 5000 Convolutional Neural Network(CNN) [3] Stacked [4] 1 2 a) y.kono@chiba-u.jp b) kawa@faculty.chiba-u.jp
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 informationHOG HOG LBP LBP 4) LBP LBP Wang LBP HOG LBP 5) LBP LBP 1 r n 1 n, 1
1 1 1 Shwartz Histgrams of Oriented Gradients HOG PLS PLS KPLS INRIA PLS KPLS KPLS PLS Pedestrian Detection Using Kernel Partial Least Squares Analysis Takashi Abe, 1 Takayuki Okatani 1 and Kouichiro Deguchi
More informationComputational Semantics 1 category specificity Warrington (1975); Warrington & Shallice (1979, 1984) 2 basic level superiority 3 super-ordinate catego
Computational Semantics 1 category specificity Warrington (1975); Warrington & Shallice (1979, 1984) 2 basic level superiority 3 super-ordinate category preservation 1 / 13 analogy by vector space Figure
More information活用ガイド (ソフトウェア編)
(Windows 98 ) ii iii iv v NEC Corporation 1999 vi P A R T 1 P A R T 2 vii P A R T 3 viii P A R T 4 ix P A R T 5 x P A R T 1 2 3 1 1 2 4 1 2 3 4 5 1 1 2 3 4 5 6 6 7 7 1 1 2 8 1 9 1 1 2 3 4 5 6 1 2 3 10
More informationIPSJ SIG Technical Report Vol.2016-GI-35 No /3/9 StarCraft AI Deep Q-Network StarCraft: BroodWar Blizzard Entertainment AI Competition AI Convo
StarCraft AI Deep Q-Network StarCraft: BroodWar Blizzard Entertainment AI Competition AI Convolutional Neural Network(CNN) Q Deep Q-Network(DQN) CNN DQN,,, 1. StarCraft: Brood War *1 Blizzard Entertainment
More informationi
i ii iii iv v vi vii viii ix x xi ( ) 854.3 700.9 10 200 3,126.9 162.3 100.6 18.3 26.5 5.6/s ( ) ( ) 1949 8 12 () () ア イ ウ ) ) () () () () BC () () (
More informationpaper.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
More information活用ガイド (ソフトウェア編)
ii iii iv NEC Corporation 1998 v vi PA RT 1 vii PA RT 2 viii PA RT 3 PA RT 4 ix P A R T 1 2 3 1 4 5 1 1 2 1 2 3 4 6 1 2 3 4 5 7 1 6 7 8 1 9 1 10 1 2 3 4 5 6 7 8 9 10 11 11 1 12 12 1 13 1 1 14 2 3 4 5 1
More information2007/8 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,
More informationパソコン機能ガイド
PART12 ii iii iv v 1 2 3 4 5 vi vii viii ix P A R T 1 x P A R T 2 xi P A R T 3 xii xiii P A R T 1 2 3 1 4 5 1 6 1 1 2 7 1 2 8 1 9 10 1 11 12 1 13 1 2 3 4 14 1 15 1 2 3 16 4 1 1 2 3 17 18 1 19 20 1 1
More informationパソコン機能ガイド
PART2 iii ii iv v 1 2 3 4 5 vi vii viii ix P A R T 1 x P A R T 2 xi P A R T 3 xii xiii P A R T 1 2 1 3 4 1 5 6 1 2 1 1 2 7 8 9 1 10 1 11 12 1 13 1 2 3 14 4 1 1 2 3 15 16 1 17 1 18 1 1 2 19 20 1 21 1 22
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 informationo 2o 3o 3 1. I o 3. 1o 2o 31. I 3o PDF Adobe Reader 4o 2 1o I 2o 3o 4o 5o 6o 7o 2197/ o 1o 1 1o
78 2 78... 2 22201011... 4... 9... 7... 29 1 1214 2 7 1 8 2 2 3 1 2 1o 2o 3o 3 1. I 1124 4o 3. 1o 2o 31. I 3o PDF Adobe Reader 4o 2 1o 72 1. I 2o 3o 4o 5o 6o 7o 2197/6 9. 9 8o 1o 1 1o 2o / 3o 4o 5o 6o
More information1... 1 2... 1 1... 1 2... 2 3... 2 4... 4 5... 4 6... 4 7... 22 8... 22 3... 22 1... 22 2... 23 3... 23 4... 24 5... 24 6... 25 7... 31 8... 32 9... 3
3 2620149 3 6 3 2 198812 21/ 198812 21 1 3 4 5 JISJIS X 0208 : 1997 JIS 4 JIS X 0213:2004 http://www.pref.hiroshima.lg.jp/site/monjokan/ 1... 1 2... 1 1... 1 2... 2 3... 2 4... 4 5... 4 6... 4 7... 22
More information「産業上利用することができる発明」の審査の運用指針(案)
1 1.... 2 1.1... 2 2.... 4 2.1... 4 3.... 6 4.... 6 1 1 29 1 29 1 1 1. 2 1 1.1 (1) (2) (3) 1 (4) 2 4 1 2 2 3 4 31 12 5 7 2.2 (5) ( a ) ( b ) 1 3 2 ( c ) (6) 2. 2.1 2.1 (1) 4 ( i ) ( ii ) ( iii ) ( iv)
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 information262014 3 1 1 6 3 2 198810 2/ 198810 2 1 3 4 http://www.pref.hiroshima.lg.jp/site/monjokan/ 1... 1... 1... 2... 2... 4... 5... 9... 9... 10... 10... 10... 10... 13 2... 13 3... 15... 15... 15... 16 4...
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 information長崎県地域防災計画
i ii iii iv v vi vii viii ix - 1 - - 2 - - 3 - - 4 - - 5 - - 6 - - 7 - - 8 - - 9 - 玢 - 10 - - 11 - - 12 - - 13 - - 14 - - 15 - - 16 - - 17 - - 18 - - 19 - - 20 - - 21 - - 22 - - 23 - - 24 - - 25 - -
More information3.1 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.
More informationONLINE_MANUAL
JPN ii iii iv v 6 vi vii viii 1 CHAPTER 1-1 1 2 1-2 1 2 3 4 5 1-3 6 7 1-4 2 CHAPTER 2-1 2-2 2-3 1 2 3 4 5 2-4 6 7 8 2-5 9 10 2-6 11 2-7 1 2 2-8 3 (A) 4 5 6 2-9 1 2-10 2 3 2-11 4 5 2-12 1 2 2-13 3 4 5
More informationONLINE_MANUAL
JPN ii iii iv v vi 6 vii viii 1 CHAPTER 1-1 1 2 1-2 1 2 3 1-3 4 5 6 7 1-4 2 CHAPTER 2-1 2-2 2-3 1 2 3 4 5 2-4 6 7 8 2-5 9 10 2-6 11 2-7 1 2 2-8 3 (A) 4 5 6 2-9 1 2-10 2 3 2-11 4 5 2-12 1 2 2-13 3 4 5
More information[1] [2] [3] [11], [12] [4], [5] [6] [9] [10] (1) W D C i N i i 0.62 [t/m 2 ] [1] W D = C i N i (1) i c 2016 Information Proce
1,a) 1,b) 1,c) 2,d) 3,e) 2015 8 31, 2016 3 4 10 86% Detecting Collapsed Buildings using Convolutional Neural Network for Estimating the Disaster Debris Amount Rin Tonegawa 1,a) Hiroyuki Iizuka 1,b) Masahito
More informationletter by letter reading read R, E, A, D 1
3 2009 10 14 1 1.1 1 1.2 1 letter by letter reading read R, E, A, D 1 1.3 1.4 Exner s writing center hypergraphia, micrographia hypergraphia micrographia 2 3 phonological dyslexia surface dyslexia deep
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 information1... 1... 1... 3 2... 4... 4... 4... 4... 4... 6... 10... 11... 15... 30
1 2420128 1 6 3 2 199103 189/1 1991031891 3 4 5 JISJIS X 0208, 1997 1 http://www.pref.hiroshima.lg.jp/site/monjokan/ 1... 1... 1... 3 2... 4... 4... 4... 4... 4... 6... 10... 11... 15... 30 1 3 5 7 6 7
More information1... 1 2... 3 3... 5 1... 5 2... 6 4... 7 1... 7 2... 9 3... 9 6... 9 7... 11 8... 11 5... 7
3 2620149 1 3 6 3 2 198829 198829 19/2 19 2 3 4 5 JISJIS X 0208 : 1997 1 http://www.pref.hiroshima.lg.jp/site/monjokan/ 1... 1 2... 3 3... 5 1... 5 2... 6 4... 7 1... 7 2... 9 3... 9 6... 9 7... 11 8...
More information