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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1 (MIRU2012) {d kouno,shimada,endo}@pluto.ai.kyutech.ac.jp (1) (2) (3) (4) 4 AdaBoost 1. Kanade [6] CLAFIC [12] EigenFace [10] [7]
![2. [11]](/docs-images/68/59204737/images/2-4.jpg)
2 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)
3 720 図 4 人物領域抽出例 特徴量抽出 この節では人物識別に用いる特徴量について述べる 図 5 人物の立ち位置による の出力結果の違い 本手法では (1) 人物の身長 (2) 人物の面積 (3) 人物 の体型 (4) 深度ヒストグラムの 4 つを特徴量を扱って いる 注 2 本手法における身長特徴は からの距離であり 実際の 人物の身長ではないことに注意 注 3 本論文における人物の体型特徴は頭部や肩の領域の幅 図 6 正規化の例 y 人物の身長 人物の身長は人物間の区別において最も重要な特徴量 の一つになる 本手法では深度情報を利用することで人 物の身長に関する特徴量を求める カメラから最も近い 位置にある人物の頭部までの距離は人物ごとに異なる そこで それぞれの深度画像においてカメラから最も近 いピクセルまでの距離を抽出する このようにして人物 の頭部までの距離は得ることができるが 立ち位置によ る距離値の差という問題がある 図 5 はその問題の例を 表している. この図において 下の値はそれぞれ から得られた距離を示している この値から分かるよう に人物の立っている位置によって同一人物においても特 徴量に大きく差が出てしまう 注 2 この問題を解決するために人物の立ち位置により出力 値を正規化する 本手法では深度情報を利用することで 概ね正しく人物領域を推定することができる こうして 求められた人物領域の中心位置を基に正規化することで 同一人物内における誤差を減少させることができる 図 6 は正規化の例を表している. このようにして得られた 値を人物の身長特徴とする 人物の面積 人物の面積も人物識別において直感的で特有の特徴量 となる そこで本手法では人物領域における面積の値を 利用する この特徴量は 3. 1 節において抽出された人物 領域のピクセル数の総和を算出することにより求める 人物の体型 人物の身体の大きさ 注 3 は人物識別において最も効果 的な特徴量の一つになる 本手法では 体型特徴として x 図 7 人物の体型特徴の取得例 x 座標と y 座標における幅の大きさを使用している こ れらの値は人物領域画像の周辺分布から抽出する 人物 領域の x 座標と y 座標において幅が最大となる値を使用 する 図 7 は体型特徴の取得例を表している
4 AdaBoost [2] AdaBoost Adaboost 1 2 C4.5 [9] 4 C AdaBoost Weka leave-one-out ,,, 3. 2,, 4 Weka J48 5
5 1 [%] all , +. all [%] 1 (94.4%)., % A H A H E G % 5 6cm 2 3cm 52.5% 21.9% 40.6% [7] x y Gallagher&Chen [4] [13]
6 2 + A B C D E F G H A B C D E F G H (1) (2) (3) (4) 4 AdaBoost C4.5 (1) (4) % 6 [1] pp , [4] A. C. Gallagher and T. Chen. Using Context to Recognize People in Consumer Images, IPSJ Transactions on Computer Vision and Applications, Vol. 1, pp , [5] Y. Iwashita and A. Stoica. Gait Recognition using Shadow Analysis, Proc. of Symposium on Bioinspired, Learning, and Intelligent Systems for Security 2009, pp , [6] T. Kanade. Picture processing by computer complex and recognition of human face, Technical report, Kyoto University, Dept. of Information Science, [7] R. Nakatani, D. Kouno, K. Shimada and T. Endo. A Person Identification Method Using a Top-view Head Image from an Overhead Camera, Proc. of 2nd International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2011), SS4-1, [8] M. Onishi and I. Yoda. Visualization of Customer Flow in an Office Complex over a Long Period, Proceedings of International Conference on Pattern Recognition (ICPR), pp , [9] J. R. Quinlan. C4.5 Programs for Machine Learning, Morgan Kaufmann Publishers, [10] M. Turk and A. P. Pentland. Eigenfaces for recognition, Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp , [11],,, 129, No. 6, pp , [12] S. Watanabe and N. Pakvasa. Subspace method in pattern recognition, Proc. of 1st Int. J. Conf on Pattern Recognition, pp. 2-32, [13] Vol 23 No 2 pp [1] M. Farenzena, L. Bazzani, A. Perina, V. Murino and M. Cristani. Person re-identification by symmetrydriven accumulation of local features Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp , [2] Y. Freund and R. E. Schapier. Experiments with a new boosting algorithm, Proc of ICML pp , [3],,, 2008, Vol.J91-D, No. 5, 6
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1 2005 11 26 1 1 5 15 18 1 1. 2005 11 26 09 00 1245 12 45 18207 2. 1,000 2,000 3. 4,000 2,000 5,000 4. 10,000 2,000 30,000 5. 1 20 10 1300 17 00 1500 1700 6. 7. TEL 047-372-4111 821 FAX 047-373-9901 [email protected]
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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 [email protected]
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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
HASC2012corpus HASC Challenge 2010,2011 HASC2011corpus( 116, 4898), HASC2012corpus( 136, 7668) HASC2012corpus HASC2012corpus
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,
IPSJ SIG Technical Report Vol.2011-CE-110 No /7/9 Bebras 1, 6 1, 2 3 4, 6 5, 6 Bebras 2010 Bebras Reporting Trial of Bebras Contest for K12 stud
Bebras 1, 6 1, 2 3 4, 6 5, 6 Bebras 2010 Bebras Reporting Trial of Bebras Contest for K12 students in Japan Susumu Kanemune, 1, 6 Yukio Idosaka, 1, 2 Toshiyuki Kamada, 3 Seiichi Tani 4, 6 and Etsuro Moriya
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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,,,,
1 IDC Wo rldwide Business Analytics Technology and Services 2013-2017 Forecast 2 24 http://www.soumu.go.jp/johotsusintokei/whitepaper/ja/h24/pdf/n2010000.pdf 3 Manyika, J., Chui, M., Brown, B., Bughin,
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Vol. 49 No. 6 1932 1941 (June 2008) RFID 1 2 RFID RFID RFID 13.56 MHz RFID A Experimental Study for Measuring Human Activities in A Bathroom Using RFID Ryo Onishi 1 and Shigeyuki Hirai 2 A bathroom is
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
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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) [email protected]
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],
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知能制御システム学 画像追跡 (1) 特徴点の検出と追跡 東北大学大学院情報科学研究科鏡慎吾 swk(at)ic.is.tohoku.ac.jp 2008.07.07 今日の内容 前回までの基本的な画像処理の例を踏まえて, ビジュアルサーボシステムの構成要素となる画像追跡の代表的手法を概説する 画像上の ある点 の追跡 オプティカルフローの拘束式 追跡しやすい点 (Harris オペレータ ) Lucas-Kanade
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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 3 5 4 1 2 1,.,., Peg-Scope Viewer,,.,,,,. Utilization of Watching Logs for Support of Multi-View Video Contents Kosuke Niwa, 1 Shogo Tokai, 3 Tetsuya Kawamoto, 5 Toshiaki Fujii, 4 Marutani Takafumi,
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ol2013-nl-214 No6 1,a) 2,b) n-gram 1 M [1] (TG: Tree ubstitution Grammar) [2], [3] TG TG 1 2 a) ohno@ilabdoshishaacjp b) khatano@maildoshishaacjp [4], [5] [6] 2 Pitman-Yor 3 Pitman-Yor 1 21 Pitman-Yor
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.