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) [email protected] b) [email protected] c) [email protected] 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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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: [email protected], [email protected], [email protected]
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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: [email protected] Abstract
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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
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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
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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: [email protected] 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
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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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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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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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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
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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
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2, a) Scene Character Extraction by an Optimal Two-Dimensional Segmentation Hiroaki TAKEBE, a) and Seiichi UCHIDA / 2 2 2 2 2 2 1. FUJITSU LABORATORIES LTD., 4 1 1 Kamikodanaka, Nakahara-ku, Kawasaki-shi,
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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
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- - 1,a) 1,b) 1,c) 2,d) Joint MILBoost 1. [1], [2] [3] *1 Histograms of Oriented Gradients(HOG) [4] Support Vector Machine(SVM) AdaBoost 1 Chubu University, 1200 Matsumoto-cho, Kasugai, Aichi 487-8501,
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]., [
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,a),b),,,,,,,, (DNN),,,, (CNN),,.,,,,,,,,,,,,,,,,,, [], [6], [7], [], [3]., [8], [0], [7],,,, Tohoku University a) [email protected] b) [email protected], [3],, (DNN), DNN, [3],
1(a) (b),(c) - [5], [6] Itti [12] [13] gaze eyeball head 2: [time] [7] Stahl [8], [9] Fang [1], [11] 3 -
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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
Gaze Head Eye (a) deg (b) 45 deg (c) 9 deg 1: - 1(b) - [5], [6] [7] Stahl [8], [9] Fang [1], [11] Itti [12] Itti [13] [7] Fang [1],
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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)
xx/xx Vol. Jxx A No. xx 1 Fig. 1 PAL(Panoramic Annular Lens) PAL(Panoramic Annular Lens) PAL (2) PAL PAL 2 PAL 3 2 PAL 1 PAL 3 PAL PAL 2. 1 PAL
PAL On the Precision of 3D Measurement by Stereo PAL Images Hiroyuki HASE,HirofumiKAWAI,FrankEKPAR, Masaaki YONEDA,andJien KATO PAL 3 PAL Panoramic Annular Lens 1985 Greguss PAL 1 PAL PAL 2 3 2 PAL DP
(4) ω t(x) = 1 ω min Ω ( (I C (y))) min 0 < ω < C A C = 1 (5) ω (5) t transmission map tmap 1 4(a) 2. 3 2. 2 t 4(a) t tmap RGB 2 (a) RGB (A), (B), (C)
(MIRU2011) 2011 7 890 0065 1 21 40 105-6691 1 1 1 731 3194 3 4 1 338 8570 255 346 8524 1836 1 E-mail: {fukumoto,kawasaki}@ibe.kagoshima-u.ac.jp, [email protected], [email protected],
3 Abstract CAD 3-D ( ) 4 Spin Image Correspondence Grouping 46.1% 17.4% 97.6% ICP [0.6mm/point] 1 CAD [1][2]
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3 E-mail: {akizuki}@isl.sist.chukyo-u.ac.jp Abstract CAD 3-D ( ) 4 Spin Image Correspondence Grouping 46.1% 17.4% 97.6% ICP [0.6mm/point] 1 CAD [1][2] Shape Index [3] [4][5] 3 SHOT [6] [7] Point Pair Feature
2 ECCV2008,2010,2012 ECCV % % % ECCV % % % ECCV % % % Ligh
ECCV2012 1 2 1 3 2012 10 8 11 ECCV2012 1. ECCV2012 European Conference on Computer Vision (ECCV) 12 2012 10 8 11 4 General Chairs Roberto Cipolla (University of Cambridge, UK), Carlo Colombo (University
Haiku Generation Based on Motif Images Using Deep Learning Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura Scho
Haiku Generation Based on Motif Images Using Deep Learning 1 2 2 2 Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura 2 1 1 School of Engineering Hokkaido University 2 2 Graduate
DEIM Forum 2012 E Web Extracting Modification of Objec
DEIM Forum 2012 E4-2 670 0092 1 1 12 E-mail: [email protected], {dkitayama,sumiya}@shse.u-hyogo.ac.jp Web Extracting Modification of Objects for Supporting Map Browsing Junki MATSUO, Daisuke
Fig. 1 Hammer Two video cameras Object Overview of hammering test (14) (8) T s T s 2
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.
IPSJ-CVIM
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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HASC2012corpus 1 1 1 1 1 1 2 2 3 4 5 6 7 HASC Challenge 2010,2011 HASC2011corpus( 116, 4898), HASC2012corpus( 136, 7668) HASC2012corpus HASC2012corpus: Human Activity Corpus and Its Application Nobuo KAWAGUCHI,
2. 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
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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: [email protected] 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
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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![[6] DoN DoN DDoN(Donuts DoN) DoN 4(2) DoN DDoN 3.2 RDoN(Ring DoN) 4(1) DoN 4(3) DoN RDoN 2 DoN 2.2 DoN PCA DoN DoN 2 DoN PCA 0 DoN 3. DoN](/thumbs/89/99418350.jpg "[6] DoN DoN DDoN(Donuts DoN) DoN 4(2) DoN DDoN 3.2 RDoN(Ring DoN) 4(1) DoN 4(3) DoN RDoN 2 DoN 2.2 DoN PCA DoN DoN 2 DoN PCA 0 DoN 3. DoN")
3 1,a) 1,b) 3D 3 3 Difference of Normals (DoN)[1] DoN, 1. 2010 Kinect[2] 3D 3 [3] 3 [4] 3 [5] 3 [6] [7] [1] [8] [9] [10] Difference of Normals (DoN) 48 8 [1] [6] DoN DoN 1 National Defense Academy a) [email protected]