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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1 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 Japan a) yabushita.hiroko@lab.ntt.co.jp (A) (B) クエリ画像 学習画像群 クエリ映像 学習画像 仮想視点画像群生成 3 次元推定 仮想視点群統合画像生成 マッチング マッチング 認識結果 認識結果 3 A B 3 A [3][4] 2 B [5][6] 3 2 DB c 202 Information Processing Society of Japan
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2 2 DB DB DB 3 P 2D P 3D 3 [7] DB [6] DB DB 2D [8][9][0] [][2] DB (B) 2 3 [3] c 202 Information Processing Society of Japan 2
3 r (x orig., y orig., z orig. ) ϑ, φ 3 (x, y, z) (x, y, z) = r (x orig., y orig., z orig. ),() x 2 orig. + y2 orig. + z2 orig. r x2 + y ϑ = 2 + z 2 ϑ x,y,z φ φ x,y ϑ x,y,z, φ x,y cos ϑ x,y,z = cos φ x,y = z x2 +y 2 +z 2 x, sin φ x 2 +y 2 x,y =. y x 2 +y 2 (2) (0 ϑ x,y,z π, 0 φ x,y 2π). (3) 3 (x, y, z) 2 (X, Y ) { X = ϑ Y = φ. (4) 2 (X, Y ) 3 3 (x orig., y orig., z orig. ) 2.3 DB DB DB D 2D 3 2 PnP Perspective-n point [4][5][6] RANSAC Random Sample Consensus 3 DB 2 (i) DB (ii) (iii) (ii) 6 (iv) (iii) 3 3 DB (v) (iii) (iv) n n > (a) DB (b) DB 2 c 202 Information Processing Society of Japan 3
4 DB (c) i S i DB 2 d i D i offset offset 2 Score(S i ) = (5) d i + offset D i + offset 2, (A) (B) (d) (b) (c) DB DB DB KIT ObjectModels Web Database[7] 3 HP DB 3 = 5 A B DB [6] 8, DB c 202 Information Processing Society of Japan 4
5 (a) (I) (b) (II) 6 a b 7 I II 50 I II DB 9 DB ( ) DB 3D DB 2D DB DB Web DB 3 3 c 202 Information Processing Society of Japan 5
![8 [6] DB 200, vol.4, pp.250-26, Nov.200. [0] J. Kim, and K. Grauman, Asymmetric Region-to-Image Matching for Comparing Images with Generic Object Categories, CVPR200, pp.2344-235. [] M. Cho, and K. M. Lee, Progressive Graph Matching: Making a Move of Graphs via Probabilistic Voting, Proc.](/docs-images/95/124946473/images/6-0.jpg "CVPR202, Jun.202. [2],,,, 3, FIT20, 3, H-03, pp., Sept.20. [3] J. Shimamura, M. Morimoto, and H. Koike. Robust vslam for dynamic scenes. MVA20, pp.344-347, Jun.20. [4], PnP, 90 90, 4-50, 990. [5] F.")
![[2],,,, Analysis- Synthesis 3 GPU, SSII200, IS4-7, Jun.200. [3] S. Savarese and L. Fei-Fei. View synthesis for recognizing unseen poses of object classes. ECCV2008, vol.5304/2008, pp.602-65, Oct.2008. [4] H.](/docs-images/95/124946473/images/6-2.jpg "Su, M. Sun, L. Fei-Fei, and S. Savarese. Learning a dense multi-view representation for detection, viewpoint classification and synthesis of object categories., ICCV2009, pp.23-220, Sept.2009. [5],,,, 3,, p.")
![56, 20 3. [6],,, 3,, vol., no. 379, PRMU20-53, pp. 73-78, 202. [7] O. Boiman, E. Shechtman, and M. Irani. In defence of nearest-neighbor based image classification. CVPR2008,](/docs-images/95/124946473/images/6-3.jpg "pp.-8, Jun.2008. [8],,,, Voting, 40(2), 34-323, 20 3. [9] M. Klinkigt, and K.")
6 8 [6] DB 200, vol.4, pp , Nov.200. [0] J. Kim, and K. Grauman, Asymmetric Region-to-Image Matching for Comparing Images with Generic Object Categories, CVPR200, pp [] M. Cho, and K. M. Lee, Progressive Graph Matching: Making a Move of Graphs via Probabilistic Voting, Proc. CVPR202, Jun.202. [2],,,, 3, FIT20, 3, H-03, pp., Sept.20. [3] J. Shimamura, M. Morimoto, and H. Koike. Robust vslam for dynamic scenes. MVA20, pp , Jun.20. [4], PnP, 90 90, 4-50, 990. [5] F. Moreno-Noguer, V. Lepetit, and P. Fua, EPnP: Accurate Non-Iterative O(n) Solution to the PnP Problem., International Journal of Computer Vision archive, vol.8(2), pp.55-66, Feb [6],,,, MIRU2005, [7] KIT ObjectModels Web Database, 9 DB [],, 2 3 -, (D-II), vol.j77-d-ii, no., pp , Nov.994. [2],,,, Analysis- Synthesis 3 GPU, SSII200, IS4-7, Jun.200. [3] S. Savarese and L. Fei-Fei. View synthesis for recognizing unseen poses of object classes. ECCV2008, vol.5304/2008, pp , Oct [4] H. Su, M. Sun, L. Fei-Fei, and S. Savarese. Learning a dense multi-view representation for detection, viewpoint classification and synthesis of object categories., ICCV2009, pp , Sept [5],,,, 3,, p.56, [6],,, 3,, vol., no. 379, PRMU20-53, pp , 202. [7] O. Boiman, E. Shechtman, and M. Irani. In defence of nearest-neighbor based image classification. CVPR2008, pp.-8, Jun [8],,,, Voting, 40(2), , [9] M. Klinkigt, and K. Kise, From Local Features to Global Shape Constraints: Heterogeneous Matching Scheme for Recognizing Objects under Serious Background Clutter, Asian Conference on Computer Vision c 202 Information Processing Society of Japan 6
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
3: 2: 2. 2 Semi-supervised learning Semi-supervised learning [5,6] Semi-supervised learning Self-training [13] [14] Self-training Self-training Semi-s
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THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. 599-8531 1-1 E-mail: tsukada@m.cs.osakafu-u.ac.jp, {masa,kise}@cs.osakafu-u.ac.jp Semi-supervised learning
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) omokawa@vision.is.tohoku.ac.jp b) okatani@vision.is.tohoku.ac.jp, [3],, (DNN), DNN, [3],
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.
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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
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(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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IPSJ SIG Technical Report 1,a) 1,b) 1,c) 1,d) 2,e) 2,f) 2,g) 1. [1] [2] 2 [3] Osaka Prefecture University 1 1, Gakuencho, Naka, Sakai,
![IPSJ SIG Technical Report 1,a) 1,b) 1,c) 1,d) 2,e) 2,f) 2,g) 1. [1] [2] 2 [3] Osaka Prefecture University 1 1, Gakuencho, Naka, Sakai,](/thumbs/94/119099134.jpg "IPSJ SIG Technical Report 1,a) 1,b) 1,c) 1,d) 2,e) 2,f) 2,g) 1. [1] [2] 2 [3] Osaka Prefecture University 1 1, Gakuencho, Naka, Sakai,")
1,a) 1,b) 1,c) 1,d) 2,e) 2,f) 2,g) 1. [1] [2] 2 [3] 1 599 8531 1 1 Osaka Prefecture University 1 1, Gakuencho, Naka, Sakai, Osaka 599 8531, Japan 2 565 0871 Osaka University 1 1, Yamadaoka, Suita, Osaka
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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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