2013/9 Vol. J96 D No. 9 [12] [14] [15] [17] [15] [16] [17] [18], [19] [20] [11], [14], [21] 2010 Visual Object Classes Chall
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- あかり かんざとばる
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1 a) b) c) Human Detection Based on Statistical Learning from Image Yuji YAMAUCHI a), Takayoshi YAMASHITA b), and Hironobu FUJIYOSHI c) 1. [1] 1969 Sakai [2] [3] [5] Chubu University, 1200 Matsumoto, Kasugai-shi, Japan OMRON Corporation, Nishikusatsu, Kusatsu-shi, Japan a) yuu@vision.cs.chubu.ac.jp b) takayosi@omm.ncl.omron.co.jp c) hf@cs.chubu.ac.jp 1990 [6] [9] Neural Network [7] SVM [10] Naive Bayes [8] AdaBoost [9], [11] 2001 Viola Jones [9], [11] D Vol. J96 D No. 9 pp c
2 2013/9 Vol. J96 D No. 9 [12] [14] [15] [17] [15] [16] [17] [18], [19] [20] [11], [14], [21] 2010 Visual Object Classes Challenge VOC [22], [23] 2018
3 Fig. 1 1 Process of training and detection. 2 [24], [25] Time of Flight TOF Microsoft Kinect [26], [27] (a) 2019
![2 [24], [25] Time of Flight TOF](/docs-images/46/20522959/images/page_3.jpg "Microsoft Kinect [26], [27] 2.")
4 2013/9 Vol. J96 D No. 9 Table 1 1 Factors to complicate the human detection, and its countermeasures. - HOG [14] CSS [28] HOF [29] - Joint Haar-like [30] CoHOG [31] Joint HOG [32] - Cluster Boosted Tree [33] - Deformable Parts Model [34] Hough Forest [35] - [36] - [37] [38] (b) 1(d) (e) 1(d) Mean Shift [39]
![- HOG [14] CSS [28] HOF [29] - Joint Haar-like [30] CoHOG [31] Joint HOG [32] -](/docs-images/46/20522959/images/page_4.jpg "Cluster Boosted Tree [33] - Deformable Parts Model [34] Hough Forest [35] - [36] -")
5 [9], [40] [14], [41], [42] Chen Edge of Orientation Histograms EOH [41] [43] EOH 2(a) Wu 2(b) Edgelet [42], [44] 2(c) 2 Local Binary Pattern LBP [45] 2 Fig. 2 Gradient-based features. 2021
![Orientation Histograms EOH [41] [43] EOH 2(a) Wu](/docs-images/46/20522959/images/page_5.jpg "2(b) Edgelet [42], [44] 2(c) 2 Local Binary Pattern")
6 2013/9 Vol. J96 D No. 9 [36], [46] [48] Dalal Histograms of Oriented Gradients HOG [14] HOG HOG 1987 [49] HOG [28], [29], [34], [36], [50] HOG Extended HOG EHOG [51] HOG Pyramid HOG P-HOG [52] Color-HOG C-HOG [53] Edge Similarity-based-HOG ES-HOG [54] Dollar [9] [55] LUV [56] [28] Walk Color Self-Similarity CSS 3(a) CSS 3 CSS [28] [58] Fig. 3 Visualized images of CSS features [28] and foreground probability maps [58]. CSS HOG CS-HOG [57] CSS [58] Yao [59] [58] 2022
![Extended HOG EHOG [51] HOG Pyramid HOG P-HOG [52] Color-HOG C-HOG [53] Edge Similarity-based-HOG ES-HOG [54] 3. 1.](/docs-images/46/20522959/images/page_6.jpg "2 Dollar [9] [55] LUV [56] [28] Walk Color Self-Similarity CSS 3(a) CSS 3 CSS [28] [58] Fig.")
7 3(b) [13], [29] Viola 2 Haar-like [13] Dalal 2 [29] HOF Histogram of Flow Dalal [60] [62] STpatch [63] [61] STpatch TOF Time of Flight 4 Relational Depth Similarity Feature RDSF [24] 4 RDSF Shotton 2 [64] Xia Kinect Chamfer Matching 3D [25] TOF Kinect [24] Fig. 4 Relational Depth Similarity Feature [24]. 2 Table 2 Comparison of property of local features. 2023
![4 TOF Time of Flight 4 Relational Depth Similarity Feature RDSF [24] 4 RDSF Shotton 2 [64] Xia](/docs-images/46/20522959/images/page_7.jpg "Kinect Chamfer Matching 3D [25] TOF Kinect 3. 1. 5 2 4 [24] Fig.")
![2013/9 Vol. J96 D No. 9 3. 2 Ω 3. 2. 1 Watanabe Co-occurrence Histograms of Oriented Gradients CoHOG [31], [65] CoHOG 5 2 CoHOG [66] Local Binary Pattern LBP [45] 5 CoHOG [65] Fig.](/docs-images/40/20522959/images/8-0.png "5 CoHOG features [65]. [67] Tuzel [68] 3. 2.")
8 2013/9 Vol. J96 D No Ω Watanabe Co-occurrence Histograms of Oriented Gradients CoHOG [31], [65] CoHOG 5 2 CoHOG [66] Local Binary Pattern LBP [45] 5 CoHOG [65] Fig. 5 CoHOG features [65]. [67] Tuzel [68] [30], [32], [69] [71] Joint Haar-like [30] Haar-like 2 2 Joint Haar-like AdaBoost Haar-like Joint Haar-like 2 [72] Sabzmeydani 4 AdaBoost Shapelet [69] Sabzmeydani 2 AdaBoost 1 AdaBoost
![1 Watanabe Co-occurrence Histograms of Oriented Gradients CoHOG [31], [65] CoHOG 5 2 CoHOG [66] Local](/docs-images/46/20522959/images/page_8.jpg "Binary Pattern LBP [45] 5 CoHOG [65] Fig. 5 CoHOG features [65]. [67] Tuzel [68] 3. 2.")
9 6 Shapelet [69] Fig. 6 Shapelet features [69]. Shapelet 2 AdaBoost Shapelet AdaBoost Shapelet Joint Haar-like Shapelet Joint HOG [32] Rowley [73] [74], [75] Rowley [51], [76], [77] Wu Cluster Boosted Tree CVT [33] 7 k-means [78] 2025
![Shapelet Joint HOG [32] 3. 2. 3 4.](/docs-images/46/20522959/images/page_9.jpg "3. 4. 1 Rowley [73] [74], [75] Rowley [51], [76], [77] Wu")
10 2013/9 Vol. J96 D No. 9 7 Cluster Boosted Tree [33] Fig. 7 Cluster Boosted Tree [33]. Joint Boosting Joint Boosting [33], [78] [79] [44], [80] 3 5 [35], [81] 2026
![7 Cluster Boosted Tree [33].](/docs-images/46/20522959/images/page_10.jpg "Joint Boosting Joint Boosting [33], [78] 4.")
11 8 Poselet [82] Poselet Poselet Fig. 8 Examples of Poselet [82]. left: Mean poselet image, right: Poselet represents a part of the human pose. [34], [82] Bourdev Poselet [82] 8 Poselet Poselet Latent SVM [34] Mohan 2 Adaptive Combination of Classifiers ACC [79] Mohan 1 2 Multi-Instance Learning MIL [83] [84] [86] MIL Xia Star Model [87] Xia Star Model Star Model 2027
![[34], [82] Bourdev Poselet [82] 8 Poselet Poselet Latent SVM [34] 4. 2.](/docs-images/46/20522959/images/page_11.jpg "2 Mohan 2 Adaptive Combination of Classifiers ACC [79] Mohan 1 2 Multi-Instance Learning MIL")
![2013/9 Vol. J96 D No. 9 10 ISM [97] Fig. 10 Flow of human detection by ISM [97]. 9 Deformable Parts Model [34] (a) (b) (c) (d) Fig.](/docs-images/40/20522959/images/12-0.png "9 Detection results using Deformable Parts Model and human model [34]. (a) Detection results, (b) Root filter. (c) Parts filter.")
12 2013/9 Vol. J96 D No ISM [97] Fig. 10 Flow of human detection by ISM [97]. 9 Deformable Parts Model [34] (a) (b) (c) (d) Fig. 9 Detection results using Deformable Parts Model and human model [34]. (a) Detection results, (b) Root filter. (c) Parts filter. (d) A spatial model for the location of each part relative to the root. Constellation Model [88] [89] Felzenszwalb Deformable Parts Model [34], [90] Deformable Parts Model 9 Star Model Latent SVM Deformable Parts Model Deformable Parts Model [91] [94] [95], [96] Leibe Implicit Shape Model ISM [81], [97], [98] Leibe 10 Leibe Space-Time patch [63] [60] Gall Hough Forests [35] Hough Forests Randam Forest [99] Hough Forests [100] [102] 2028
![4 Hoiem [37] 12 (a) 11 [36] (a) (b) Fig. 11 Estimated partial occlusion regions [36].](/docs-images/40/20522959/images/13-0.png "(a) Original images. (b) Corresponding segmented occlusion likelihood images.")
13 Deformable Parts Model Web 4. 3 Wang [36] Wang Mean Shift [39] 11 Wang HOG LBP TOF [24] Enzweiler [103] Hoiem [37] 12 (a) 11 [36] (a) (b) Fig. 11 Estimated partial occlusion regions [36]. (a) Original images. (b) Corresponding segmented occlusion likelihood images. For each segmented region, the negative sore. 12 [37] Fig. 12 Human detection using geometry information [37]. 2029
![4 Hoiem [37] 12 (a) 11 [36] (a) (b) Fig. 11 Estimated partial occlusion regions [36].](/docs-images/46/20522959/images/page_13.jpg "(a) Original images. (b) Corresponding segmented occlusion likelihood images.")
14 2013/9 Vol. J96 D No (c) Hoiem 12 (b) Hoiem Pang [38] Boosting h m 13 h m h m α m Covariate Boost Pang [104] 13 [38] Fig. 13 Optimizing classifier by Transfer learning [38] [9] 2030
![m 13 h m h m α m Covariate Boost Pang [104] 13](/docs-images/46/20522959/images/page_14.jpg "[38] Fig.")
15 [55] [105] Zhu HOG HOG [105] Integral Channel Features [56] [9] Zhu HOG SVM [105] [43], [87], [91] Zhang [106] Zhang 8 Lampert [107] Lampert TOF [24] Benenson [108] Graphics Processing Unit GPU GPU [109] [111] GPU GPU HOG Dalal HOG SVM [14] FPS Dollar Integral Channel Features Soft cascade Boosting [56] 1.18 FPS [112] 6.4 FPS GPU [111] 135 FPS (a) (b) [114] Fig. 14 (a) Real images. (b) Virtual images [114]. 2031
![GPU HOG Dalal HOG SVM [14] 640 480 0.24 FPS Dollar Integral Channel Features Soft cascade Boosting [56] 1.](/docs-images/46/20522959/images/page_15.jpg "18 FPS [112] 6.4 FPS GPU [111] 135 FPS 5. 2 14 (a) (b) [114] Fig. 14 (a) Real images.")
16 2013/9 Vol. J96 D No Fig. 15 History of object detection. [113] [114] [116] Mar [114] 14 CG CG Yamauchi [115]
![[113] [114] [116] Mar [114] 14](/docs-images/46/20522959/images/page_16.jpg "CG CG Yamauchi [115] 5. 3 2032")
17 Li y [117] Li Smart Window Transform [118] [119] 2008 [120] 2010 [121] 2010 LSI FPGA [122] 2011 LSI [123] Web MIT CBCL Pedestrian Data [124] MIT CBCL Pedestrian Data Dalal HOG SVM INRIA Person Dataset [14] HOG SVM MIT CBCL Pedestrian Data INRIA Person Dataset INRIA Person Dataset INRIA Person Dataset INRIA Person Dataset 2033
![1. 1 3 MIT CBCL Pedestrian Data [124] MIT CBCL Pedestrian Data Dalal HOG SVM INRIA Person](/docs-images/46/20522959/images/page_17.jpg "Dataset [14] HOG SVM MIT CBCL Pedestrian Data INRIA Person Dataset INRIA Person Dataset")
18 2013/9 Vol. J96 D No. 9 3 Table 3 Comparing human image databases. MIT [124] INRIA [14] 2,416 1, , USC-A [44] USC-B [44] USC-C [33] ETH [125] 1,578-1,803 9,380 - Daimler2006 [126] 14, ,000-1, ,000 Daimler2009 [15] 15,660 6,744 21,800 56,492 - NICTA [127] 18,700 5,200-6,900 50,000 TUD [128] Caltech [16] 192,000 61,000 56, ,000 5,600 [15], [16], [126] Caltech Pedestrian Detection Benchmark [16] Miss rate VS. False Positive Per Window (FPPW) [14] 2 Miss rate VS. False Positive Per Image (FPPI) [16] 1 FPPW 10, FPPW FPPW 2 FPPI FPPI FPPI 16 DET Fig. 16 Example of comparison of DET curves. 2 FPPI 1 2 Miss rate FPPW FPPI Detection Error Tradeoff DET 2034
![150,000-1,600 100,000 Daimler2009 [15] 15,660 6,744 21,800 56,492 - NICTA [127] 18,700 5,200-6,900 50,000 TUD [128] 400-250 311 - Caltech [16] 192,000 61,000 56,000 155,000 5,600 [15], [16], [126]](/docs-images/46/20522959/images/page_18.jpg "Caltech Pedestrian Detection Benchmark [16] 6. 2 1 Miss rate VS. False Positive Per Window (FPPW) [14] 2 Miss rate VS. False Positive Per Image (FPPI) [16] 1 FPPW 10,000 10 0.")
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23 Workshop, [101] Joint Hough Forests: MIRU2011, [102] Hough Forests [103] M. Enzweiler, A. Eigenstetter, B. Schiele, and D. Gavrila, Multi-cue pedestrian classification with partial occlusion handling, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp , [104] PRMU , [105] Q. Zhu, S. Avidan, M.C. Yeh, and K.T. Cheng, Fast human detection using a cascade of histograms of oriented gradients, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp , [106] W. Zhang, G. Zelinsky, and D. Samaras, Real-time accurate object detection using multiple resolutions, IEEE International Conference on Computer Vision, [107] C.H. Lampert, M.B. Blaschko, and T. Hofmann, Beyond sliding windows: Object localization by efficient subwindow search, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June [108] R. Benenson, M. Mathias, R. Timofte, and L.V. Gool, Fast stixel computation for fast pedestrian detection, IEEE Workshop on Computer Vision in Vehicle Technology: From Earth to Mars (in Conjunction with ECCV2012), [109] B. Bilgic, B.K.P. Horn, and I. Masaki, Fast human detection with cascaded ensembles on the GPU, IEEE Intelligent Vehicles Symposium, pp , [110] V.A. Prisacariu and I. Reid, fasthog A realtime GPU implementation of HOG, Technical Report, Oxford University, [111] R. Benenson, M. Mathias, R. Timofte, and L.V. Gool, Pedestrian detection at 100 frames per second, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp , [112] P. Dollár, S. Belongie, and P. Perona, The fastest pedestrian detector in the west, British Machine Vision Conference, [113] vol.46, no.15, pp.35 42, [114] J. Marín, V. David, D. Gerónimo, and Antonio M-López, Learning appearance in virtual scenarios for pedestrian detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp , [115] Y. Yamauchi and H. Fujiyoshi, Automatic generation of training samples and a learning method based on advanced MILboost for human detection, Asian Conference on Pattern Recognition, pp , [116] PRMU , [117] Y. Li, B. Wu, and R. Nevatia, Human detection by searching in 3D space using camera and scene knowledge, International Conference on Pattern Recognition, [118] Smart Window Transform [119] HONDA [120] TOYOTA jpn/tech/safety/technology/technology file/active/ night view.html. [121] Mobileye C2-270, products/mobileye-c2-series/mobileye-c2-270/. [122] ODEN [123] LSI t06.htm [124] M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, and T. Poggio, Pedestrian detection using wavelet templates, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp , [125] A. Ess, B. Leibe, and L. Van Gool, Depth and appearance for mobile scene analysis, IEEE International Conference on Computer Vision, [126] S. Munder and D.M. Gavrila, An experimental study on pedestrian classification, IEEE Trans. Pattern Anal. Mach. Intell., vol.28, no.11, pp , [127] G. Overett, L. Petersson, N. Brewer, L. Andersson, and N. Pettersso, A new pedestrian dataset for supervised learning, The Intelligent Vehicles Symposium, [128] M. Andriluka, S. Roth, and B. Schiele, Peopletracking-by-detection and people-detection-bytracking, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, [129] M. Wang and X. Wang, Automatic adaptation of a generic pedestrian detector to a specific traffic scene, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp , [130] P. Sharma and C. Huang, Unsupervised incremental learning for improved object detection in a video, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp ,
24 2013/9 Vol. J96 D No. 9 [131] M. Wang and X. Wang, Transferring a generic pedestrian detector towards specific scenes, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp , [132] X. Wang and T.X. Han, Detection by detections: Non-parametric detector adaptation for a video, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp , DC IEEE-CS Postdoctoral Fellow IEEE 2040
1 AdaBoost [8], [10] 2001 Viola Jones [8], [10] [11], [12] 2 3 4 5 6 7 2. 2. 1 2 2. 1. 1 1(a) 2. 1. 2 1(b) 2
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THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. [], 487 8501 1200 525 0025 2 2-1 E-mail: yuu@vision.cs.chubu.ac.jp, takayosi@omm.ncl.omron.co.jp, hf@cs.chubu.ac.jp
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 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
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
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
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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
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
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
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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
[1] SBS [2] SBS Random Forests[3] Random Forests ii
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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
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
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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
図 1 提案手法による生成型学習の流れ Fig. 1 Generative learning procedure in the proposed method. 図 2 3 次元人体モデル Fig. 2 3D human model. 図 3 パラメータに対応した人体モデル Fig. 3 Adapt
(MIRU2012) 2012 8 Negative-Bag MILBoost Graduate School of Engineering, Chubu University, 1200 Matsumoto, Kasugai, Aichi, 487-8501 Japan. E-mail: {tsuchiya,yuu}@vision.cs.chubu.ac.jp, hf@cs.chubu.ac.jp
(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
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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
(b) BoF codeword codeword BoF (c) BoF Fergus Weber [11] Weber [12] Weber Fergus BoF (b) Fergus [13] Fergus 2. Fergus 2. 1 Fergus [3]
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* A Multimodal Constellation Model for Generic Object Recognition Yasunori KAMIYA, Tomokazu TAKAHASHI,IchiroIDE, and Hiroshi MURASE Bag of Features (BoF) BoF EM 1. [1] Part-based Graduate School of Information
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1 1 Multi-Person Tracking for a Mobile Robot using Overlapping Silhouette Templates Junji Satake 1 and Jun Miura 1 This paper describes a stereo-based person tracking method for a person following robot.
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IS2-26 第 19 回 画 像 センシングシンポジウム, 横 浜,2013 年 6 月 SVM E-mail: yuhi@vision.cs.chubu.ac.jp Abstract SVM SVM SVM SVM HOG B-HOG HOG SVM 6.1% 17 1 Intelligent Transport System(ITS: ) 2005 Dalal HOG SVM[1] [2] HOG
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
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
(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
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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.
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
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a) Change Detection Using Joint Intensity Histogram Yasuyo KITA a) 2 (0 255) (I 1 (x),i 2 (x)) I 2 = CI 1 (C>0) (I 1,I 2 ) (I 1,I 2 ) 2 1. [1] 2 [2] [3] [5] [6] [8] Intelligent Systems Research Institute,
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)
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(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
Silhouette on Image Object Silhouette on Images Object 1 Fig. 1 Visual cone Fig. 2 2 Volume intersection method Fig. 3 3 Background subtraction Fig. 4
Image-based Modeling 1 1 Object Extraction Method for Image-based Modeling using Projection Transformation of Multi-viewpoint Images Masanori Ibaraki 1 and Yuji Sakamoto 1 The volume intersection method
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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
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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,,,,
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[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
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. TRECVID2012 Instance Search {sak
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. TRECVID2012 Instance Search 599 8531 1 1 E-mail: {sakata,matozaki}@m.cs.osakafu-u.ac.jp, {kise,masa}@cs.osakafu-u.ac.jp
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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
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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
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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
Microsoft PowerPoint - SSII_harada pptx
The state of the world The gathered data The processed data w d r I( W; D) I( W; R) The data processing theorem states that data processing can only destroy information. David J.C. MacKay. Information
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DEIM Forum 213 D3-4 食事認識を用いたモバイル食事管理システム 河野 憲之 柳井 啓司 電気通信大学 電気通信学部 情報工学科 182-8585 東京都調布市調布ヶ丘 1-5-1 電気通信大学 大学院情報理工学研究科 総合情報学専攻 182-8585 東京都調布市調布ヶ丘 1-5-1 E-mail: kawano-y@mm.inf.uec.ac.jp, yanai@cs.uec.ac.jp
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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
IS3-18 第21回画像センシングシンポジウム 横浜 2015年6月 ２つの人物検出の組み合わせと複数特徴量の利用による人物追跡 川下 雄大 増山 岳人 梅田 和昇 中央大学大学院 中央大学 Abstract 本
２つの人物検出の組み合わせと複数特徴量の利用による人物追跡 川下 雄大 増山 岳人 梅田 和昇 中央大学大学院 中央大学 E-mail: kawashita@sensor.mech.chuo-u.ac.jp Abstract 本稿では ステレオカメラを用いた人物検出 追跡 手法を提案する 人物検出では つの人物検出手法よ り得た結果を組み合せ さらに前景領域と視差前景領 域を用いて検出結果の妥当性を検証することで
図 2: 高周波成分を用いた超解像 解像度度画像とそれらを低解像度化して得られる 低解像度画像との差により低解像度の高周波成分 を得る 高解像度と低解像度の高周波成分から位 置関係を保ったままパッチ領域をそれぞれ切り出 し 高解像度パッチ画像と低解像度パッチ画像の ペアとしてデータベースに登録する
Exemplar-Based Super-Resolution of Human Body Image in Surveillance Video 1 1,2 1 1 1 Kento Nishibori 1, Tomokazu TAKAHASHI 1,2, Daisuke DEGUCHI 1, Ichiro IDE 1 and Hiroshi MURASE 1 1 2 nishiborik@murase.m.is.nagoya-u.ac.jp
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1 2 ( ) Quantitative Characteristics of Software Process (Is There any Myth, Mystery or Anomaly? No Silver Bullet?) Zenya Koono and Hui Chen A process creates a product. This paper reviews various samples
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2.R R R R Pan-Tompkins(PT) [8] R 2 SQRS[9] PT Q R WQRS[10] Quad Level Vector(QLV)[11] QRS R Continuous Wavelet Transform(CWT)[12] Mexican hat 4
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G-002 R Database and R-Wave Detecting System for Utilizing ECG Data Takeshi Nagatomo Ikuko Shimizu Takeshi Ikeda Akio Sashima Koichi Kurumatani R R MIT-BIH R 90% 1. R R [1] 2 24 16 Tokyo University of
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1, 1,2, 1 Hammering Test with Image and Sound Signal Processing Atsushi YAMASHITA 1, Takahiro HARA 1,2 and Toru KANEKO 1 1 Department of Mechanical Engineering, Shizuoka University. 2 Mitsubishi Electric.
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STHOG 1 1 1 STHOG STHOG Pedestrian Matching across Cameras using STHOG Features Ryo Kawai, 1 Yasushi Makihara 1 and Yasushi Yagi 1 In this paper, we propose a method of pedestrian matching across CCTV
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1 1 1 1 2 Geographical Feature Extraction for Retrieval of Modified Maps Junki Matsuo, 1 Daisuke Kitayama, 1 Ryong Lee 1 and Kazutoshi Sumiya 1 Digital maps available on the Web are widely used for obtaining
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HASC2012corpus 1 1 1 1 1 1 2 2 3 4 5 6 7 HASC Challenge 2010,2011 HASC2011corpus( 116, 4898), HASC2012corpus( 136, 7668) HASC2012corpus HASC2012corpus: Human Activity Corpus and Its Application Nobuo KAWAGUCHI,
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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
Computer Security Symposium October ,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) [1] 1 Meiji U
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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
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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
す 局所領域 ωk において 線形変換に用いる係数 (ak 画素の係数 (ak bk ) を算出し 入力画像の信号成分を bk ) は次式のコスト関数 E を最小化するように最適化 有さない画素に対して 式 (2) より画素値を算出する される これにより 低解像度な画像から補間によるアップサ E(
IR E-mail: hf@cs.chubu.ac.jp Abstract IR RGB ( ) IR IR IR RGB RGB PSNR 1 Time-Of- Flight(TOF)[1] Kinect [2] TOF LED TOF [3] [6] [4][5] 2 [6] RGB ( ) Infrared(IR) IR 2 2.1 1 す 局所領域 ωk において 線形変換に用いる係数 (ak
(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
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1 1 1, Extraction of Transmitted Light using Parallel High-frequency Illumination Kenichiro Tanaka 1 Yasuhiro Mukaigawa 1 Yasushi Yagi 1 Abstract: We propose a new sharpening method of transmitted scene
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE
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(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
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3 1,a) 1,b) 3D 3 3 Difference of Normals (DoN)[1] DoN, 1. 2010 Kinect[2] 3D 3 [3] 3 [4] 3 [5] 3 [6] [7] [1] [8] [9] [10] Difference of Normals (DoN) 48 8 [1] [6] DoN DoN 1 National Defense Academy a) em53035@nda.ac.jp
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