8.dvi
|
|
|
- らむ たておか
- 5 years ago
- Views:
Transcription
1
2 i Simultanious Registration
3 ii
4 iii Cyrax Cyrax CCD Cyrax ( ) ( ) ( ) ( ) ( ) ( )
5 iv Cyrax
6 1 1
7 CAD
8 (alignment) (merging). 1.1 ( ) ( ) (x; y; z x; y; z )
9 : ( ) ( )
10 :
11
12
13 Cyrax Cyrax
14 9 2
15 [1] ( ) 2 Shum[2]
16 [3] Besl[4] ICP(Iterative Closest Point) [5-10] ICP ( ) 2 2 ICP 1 Zhang[5] ICP
17 k-d tree Chen[6] Dorai [11] Chen Fledmar[12] Bergervin[7] Chen 2 1 Masda[9] Least Median Squares 2 [10] [13-17]
18 13 3 Simultanious Registration Simultanious Registration
19 3 Simultanious Registration : Cyrax 6mm ( )( ) ( )( ) ( ) ( ) ( ) ( ) 100m(50cm - 50m ) 2mm(@50m) 2mm(@50m) 6mm (0-50m) Cyrax ( 3.2 ) Cyrax 3.. Cyrax ( ) ( ).. Cyrax
20 3 Simultanious Registration : Cyrax
21 3 Simultanious Registration : Cyrax.... CyraX CyraX CCD ( ).. ( ). depth. depth depth depth. 1
22 3 Simultanious Registration 17. CyraX ( ) n S 0...S n ~x x ~ 0 ( ) T ( ) i. T ( ) i ~x = ~ x 0 (3.1) ( ) ~ i S i. ~ i R () i t () i ~ i. ~x 2 S i T () i. T () i ~x = R () i ~x + t () i (3.2) T ( ) i T () i T () i = T () i T ( ) i (3.3)
23 3 Simultanious Registration 18. S i S j D(S i ;S j ) ". " 2 =min ~ =min =min =min X D(T () i S i ;T () j S j ) i6=j X D(T () i T ( ) i ~ i6=j X ~ i6=j;k X ~ i6=j;k S i ;T () j T ( ) j S j ) (R () i ~n ( ) ik ((R() j ~y ( ) + ~ ijk t () j ) 0 (R () i ~x ( ) ik + ~ t () i ))) 2 ka ijk ~ 0 sijk k 2 (3.4). A ijk = 0 :::0 {z } 6i21 C {z} ijk 621 s ijk = ~n ( ) ik 1 (~x( ) ik 0 ~y( ) ijk ) (3.5) 0 :::0 {z } 6(l0i01)21 {z } 6j :::0 C ijk = ~n ( ) ik 2 ~y( ) ijk 0~n ( ) ik 0C {z ijk } 0 :::0 {z } 6(l0j01)21 (3.6) 621! T (3.7) 5.
24 3 Simultanious Registration : CCD ( ) Cyrax ( )
25 20 4
26 Model Model Scene Scene Model 6 (x; y; z x; y; z ) 2..( 4.1 )
27 :
28 Model Scene " " 1. 3 ( )
29 4 24.( 4.1 ) 4.1: 4.1 n S 1 ;S 2 ;:::;S n 1 S 1 T w i S i i 0 1 S i01 T i;i01 S 0 1 ;S0 2 :::;S0 n S 1 S n T w ;T 2 ;:::;T n.( 4.2 ) 8 S 0 1 = T w S 1 >< >: S 0 2 = T 2 S 2 = T 2;1 T w S 1 S 0 3 = T 3 S 3 = T 3;2 T 2;1 T w S 3. S 0 n01 = T n01 S n01 = T n01;n02 T n02;n03 :::T 2;1 T w S n01 S 0 n = T n S n = T n;n01 T n01;n02 :::T 2;1 T w S n (4.1).
30 4 25 i S i i 0 1 S i01 1T i;i01 T i;i01 1 1T w T w i 1T i 1T i =1T i;i01 1T i01;i02 :::1T 2;1 1T w (4.2).. 4.2: S S 0 i ;S j ( S 0 i S j ) 3. 2 S 0 i ;S j
31 S j % 40% Model 3 M 1 ;M 2 ;M 3 3 Scene 3 S 1 ;S 2 ;S 3. 4S 1 S 2 S 3 4M 1 M 2 M 3 T Model. M 1 M 2 u 12 M 1 M 3 u 13 2 u 12 u 13 m. u 12 u 13 n 12,n 13
32 :. n 12 = u 12 k u 12 k n 13 = u 13 k u 13 k (4.3) (4.4) 2 u 12 u 13 2 m 1 ; m 2. 1 m 1 = n 12 m 2 m 2 = n 13 0 n 12 2 cos m k n 13 0 n 12 2 cos m k (4.5). 2 m 1 ; m 2 m 3 m 3 = m 1 2 m 2 (4.6) 3 m 1 =(m 1x ;m 1y ;m 1z ) T ; m 2 =(m 2x ;m 2y ;m 2z ) T ; m 3 = (m 3x ;m 3y ;m 3z ) T. Scene s 1 =(s 1x ;s 1y ;s 1z ) T ; s 2 =(s 2x ;s 2y ;s 2z ) T ; s 3 =(s 3x ;s 3y ;s 3z ) T.( 4.4 )
33 : s 1 ; s 2 ; s 3 m 1 ; m 2 ; m 3 R 0. (s 1 s 2 s 2 )=R 0 (m 1 m 2 m 2 ) (4.7) R 0 =(s 1 s 2 s 2 )(m 1 m 2 m 2 ) 01 (4.8) M = (m 1 m 2 m 2 ) M 01 = M T (4.9) R 0 =(s 1 s 2 s 2 )(m 1 m 2 m 2 ) T (4.10) T t R. Scene P S T Model P M P M = RP S + t (4.11)
34 4 29. R = R 0 (4.12) R Model Scene 1 t. P M = RP S + t (4.13) t = P M 0 RP S (4.14) 1 t 3. G M = M 1 + M 2 + M 3 3 G S = S 1 + S 2 + S 3 3 4M 1 M 2 M 3 4S 1 S 2 S 3 (4.15) (4.16) t = G M 0 RG S (4.17) t R t T
35 ( ) 30 ( ) Model Scene : (12 )
36 :
37 : 12.
38 Simultanious Registration
39 n S 0...S n (12 )
40 ( )
41 5 36 ~x ~ x 0 ( ) T ( ) i. T ( ) i ~x = ~ x 0 (5.1) T i R i ~ t i R ( ) i ~x + ~ t i ( ) = ~ x 0 (5.2). k-d tree 2 z ( ) ~ i S i. ~ i R () i t () ~ i. ~x 2 S i T () i. i 1 2
42 k-d tree Model Scene Model Model Scene ( 5.1) 2 ( ) 5.4 T () i ~x = R () i ~x + t () i (5.3)
43 : T ( ) i T () i T () i = T () i T ( ) i (5.4). S i S j D(S i ;S j ) ". " 2 =min ~ =min =min X D(T () i S i ;T () j S j ) i6=j X D(T () i T ( ) i ~ i6=j X ~ i6=j;k S i ;T () (R () i ~n ( ) ik ((R() j ~y ( ) j T ( ) j S j ) + ~ ijk t () j ) 0 (R () i ~x ( ) ik + ~ t () i ))) 2
44 : X =min ka ~ ijk 0 sijk k 2 (5.5) ~ i6=j;k. A ijk = 0 :::0 {z } 6i21 C {z} ijk 621 s ijk = ~n ( ) ik 1 (~x( ) ik 0 ~y( ) ijk ) (5.6) 0 :::0 {z } 6(l0i01)21 {z } 6j :::0 C ijk = ~n ( ) ik 2 ~y( ) ijk 0~n ( ) ik 0C {z ijk } 0 :::0 {z } 6(l0j01)21 (5.7) 621! T (5.8) (4).Newton-Taylor Levenberg-Marqurdt ~ (8)
45 5 40. ~ =( X i6=j;k A T ijk A ijk) 01 X i6=j;k A T ijk s ijk (5.9) 5.6 ". (9). R = 0 1 0c 3 c 2 c 3 1 0c 1 0c 2 c C A (5.10) " = R m n 1 ((R s y + t s ) 0 (R m x + t m ))) = R m n 1 ((R s y + R m y 0 R m y + t s ) 0 (R m x + t m ))) = R m n 1 ((R s 0 R m )y +(t s 0 t m ) 0 R m (x 0 y)) = R m n 1 ((R s 0 R m )y +(t s 0 t m )) 0 R m n 1 (R m n(x 0 y)) = R m n 1 ((R s 0 R m )y +(t s 0 t m )) 0 n 1 (x 0 y)) (5.11) 2 s. 1 1 = R m n 1 ((R s 0 R m )y +(t s 0 t m )) = n 1 ((R s 0 R m )y +(t s 0 t m )) = n 1 (( = 0 n x n y 0 1 C A 1 0 0c 3s c 2s c 3s 0 0c 1s 0 0c 2s c 1s C A 0 0 0c 3m c 2m c 3m 0 0c 1m 0c 2m c 1m 0 1 C (0c 3s y y + c 2s y z ) 0 (0c 3m y y + c 2m y z )+t sx 0 t mx (0c 1s y z + c 3s y x ) 0 (0c 1m y z + c 3m y x )+t sy 0 t my A )y +(t s + t m )) 1 C A n z (0c 2s y x + c 1s y y ) 0 (0c 2m y x + c 1m y y )+t sz 0 t mz
46 5 41 = = n x n y 0 n z 1 C A 1 n x n y n z C A 1 n y y z 0 n z y y n z y x 0 n x y z 0 n x y y 0 n y y x n x n y n z 1 0 C A 1 0c 3s y y + c 2s y z 0c 1s y z + c 3s y x 0c 2s y x + c 1s y y t sx t sy t sz 1 C A C A 1 t sx t sy t sz 1 C A + 0 c 1s c 2s 0 c 3s n x n y 1 0 C A C A 1 n 1 z 0 C A + n x n y n z =(n 2 y T 0n 0n 2 y T n ) 1 0 C A 1 0 n x n y n z t mx t my t mz 1 C A 1 1 C A 0 n y y z 0 n z y y n z y x 0 n x y z n x y y 0 n y y x t mx t my t mz c 1m c 1m c 1m t mx t my t mz c 1s c 1s c 1s t sx t sy t sz 1 C A 1 C A 0c 3m y y + c 2m y z 0c 1m y z + c 3m y x 0c 2m y x + c 1m y y 1 0 C A 1 c 1m c 2m c 3m 1 C A 1 C A (5.12) (Model
47 : ( ) Scene ) 5.8.
48 : ( )
49 : ( )
50 : yz
51 : zx
52 :
53 48 6
54 \ " cm
55 Newton-Taylor.Newton- Taylor Newton-Taylor 2...
56 51 [1],,,. 3., Vol.J75-D, No.4, pp , [2] H.Shum, K.Ikeuchi and R.Reddy. Principal Component Analysis with missing Data and Its Application to Polyhedral Object modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 9, pp , [3], P.Boulanger. ( 3D )., Vol.34,No.6,pp , [4] P.J.Besl, N.D.McKay. A Method of Registration of 3-D Shapes. IEEE Trans. Pattern Analysis and Machine Intelligence Vol. 14,No.2 pp , [5] Z.Zhang. Iterative Point Matching for Registration of Free-Form Curves and Surfaces. International Journal of Computer Vision, Vol. 13, No. 2, pp , [6] Yang Chen, Gerard Medioni. Object modelling by registaration of Maltiple range Images. Image and Vision Computing Vol. 10, No.3 pp , [7] R.Bergevin, M.Soucy, H.Gagnon and D.Laurendeau. Toward a General Multi-View Registration Technique. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.18, No. 5, pp , [8] G.Turk, M.Levoy. Zippered Polygon Meshes from Range Images. InACM SIGGRAPH Computer Graphics, pp , 1994.
57 52 [9] T.Masuda and N.Yokoya. A Robust Method for registration and Segmentation of Multiple Range Images. Computer Vision and Image Understanding, Vol. 61, No. 3, pp , [10],, pp , [11] C.Dorai, G.Wang, A.K.Jain and C.Mercer. Registration and Integration of Multiple Object Views for 3D Model Construction. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, pp , [12] J.Feldmar, N.Ayache and F.Berring. 3D-2D Projective Registration of Free-Form Curves and Surfaces. Computer Vision and Image Understanding, Vol. 65, No. 3, pp ,1997. [13] M.Rutishauser, M.Stricker and M.Trobina. Merging Range Images of Arbitrary. In Proceedings of IEEE Computer Vision and Pattern recognition, pp , [14] A.Hilton, A.J.Stoddart, J.Illingworth and T.Windeatt. Reliable Surface Reconstruction from Multiple Range Images. In Proceedings of IEEE Computer Vision and Pattern recognition, pp , [15] Y.Chen and G.Medioni. Description of Complex Objects from Multiple Range Images Using an inating Ballon Model. Computer Vision and Image Understanding, Vol. 61, No. 3, pp , [16] H.Hoppe, T.DeRose and T.Duchamp. Surface Reconstruction from Unorganized points. In ACM SIGGRAPH Computer Graphics, Vol. 26-2, pp , [17] P.Hebert, D.Laurendeau and D.Poussart. Surface Prole Reconstruction: Reliable Geometric Primitive Extraction. In Proceedings of IAPR International Conference on Pattern recognition, pp. A , 1994.
58 53 [18] Peter J. Neugebauter. Reconstruction of Real-World Objects Via Simultaneous Registration and Robust Combination of Multiple Range Images. International Journal of Shape Modeling, Vol. 3 No.1 & 2, pp , June [19] Mark D. Wheeler, Yoichi Sato, Katsushi Ikeuchi. Consensus Surfaces for Modeling 3D Objects from Multiple Range Images. Proceedingd of DARPA Image Understanding Workshop '97. [20] Berthold K.P.Horn. Closed-form Solution of Absolute Orientation Using Unit Quaternions. Optical Society of America, Vol. 4 No.4/April [21] O.D.Faugeras, M.Hebert. The Representation, Recognition, and Locating og 3-D Objects. The International Journal of Robotics Research, pp , May 1986 [22] R.Bergevin, D.Laurendeau and D.Poussart. Registrating Range Views of Multipart Objects. Computer Vision and Image Understanding 61 pp. 1-16, [23] K.Higuchi, M.Hebert and K.Ikeuchi Building 3-d models from Unregistrated Range Images. Graphical Models and Image Processing 57 pp , [24] B.Curless and M.Levoy. A Volumetric Method for Building Complex Models from Range Images. Proc. of SIGGRAPH '96 New Orleans, Louisiana pp , [25] G.Borgefors. Chamfering: A Fast Method for Obtaining Approximations of the Euclidean Distance in N Dimensions. 3rd Scand. Conf. on Image Analysis, Copenhagen, Denmark [26] W.E.Lorensen and H.E. Cline. Marching Cubes: A High Resolution 3d Surface Construction Algorithm. Proc. of SIGGRAPH '87 Anaheim, California pp , 1987.
59 54 1.,. 3 (Constructing a 3D Modeling Using a High Resolution Range Sensor) 4 pp , ,,, pp , 1999
60 . CAD
proc.dvi
The Great Buddha Project Λ1 Λ2 Λ2 Λ2 Λ3 Λ2 Λ1 Λ2 Λ3 ( (VR) VR (1) (2) (3) modeling-from-reality (MFR) The Great Buddha Project Digital archive of large-scale cultural heritage Ryo Kurazume Λ1 Ko Nishino
miru02_merging.dvi
1 PC 2 Geometric and Photometric Integration System for Large Objects Ryusuke Sagawa Ko Nishino Ryo Kurazume Katsushi Ikeuchi The University of Tokyo Kyushu University Some researchers have begun projects
2003 : ( ) :80226561 1 1 1.1............................ 1 1.2......................... 1 1.3........................ 1 1.4......................... 4 2 5 2.1......................... 5 2.2........................
proc.dvi
M. D. Wheler Cyra Technologies, Inc. 3 3 CAD albedo Mapping textures on 3D geometric model using reflectance image Ryo Kurazume M. D. Wheler Katsushi Ikeuchi The University oftokyo Cyra Technologies, Inc.
tenpyou.final.dvi
TVRSJ Vol.10 No.3, 2005 1 2 3 4 Digital Restoration of The Original Great Buddha and Main Hall of Todaiji Temple Takeshi Oishi 1, Tomohito Masuda 2, Ryo Kurazume 3 and Katsushi Ikeuchi 4 Abstract We digitally
1 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 +
![1 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 +](/thumbs/81/83701592.jpg "1 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
14 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.
(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
![(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](/thumbs/75/72066128.jpg "(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")
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
(MIRU2005) ( ) Radim Šára Globally Optimal Range Image Registration by G
(MIRU0) 0 ( ) Radim Šára E-mail: ikuko@cc.tuat.ac.jp, sara@cmp.felk.cvut.cz, sugimoto@nii.ac.jp Globally Optimal Range Image Registration by Graph Kernel Algorithm Ikuko Shimizu OKATANI, Radim ŠÁRA, and
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
![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](/thumbs/92/110416683.jpg "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")
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
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
A Survey of Shape Model Generation from Multiple Range Images (1) ( ) (2) (3) Takeshi Masuda(1), Ikuko Shimizu Okatani(2), Ryusuke Sagawa(3) 1: Nation
A Survey of Shape Model Generation from Multiple Range Images (1) ( ) (2) (3) Takeshi Masuda(1), Ikuko Shimizu Okatani(2), Ryusuke Sagawa(3) 1: National Institute of Advanced Industrial Science and Technology
Optical Flow t t + δt 1 Motion Field 3 3 1) 2) 3) Lucas-Kanade 4) 1 t (x, y) I(x, y, t)
http://wwwieice-hbkborg/ 2 2 4 2 -- 2 4 2010 9 3 3 4-1 Lucas-Kanade 4-2 Mean Shift 3 4-3 2 c 2013 1/(18) http://wwwieice-hbkborg/ 2 2 4 2 -- 2 -- 4 4--1 2010 9 4--1--1 Optical Flow t t + δt 1 Motion Field
proc.dvi
TVRSJ Vol.7 No.1, 2002 The Great Buddha Project Λ1 Λ2 Λ1 Λ1 Λ1 Λ3 The Great Buddha Project Digital archive of large-scale cultural heritage Katsushi Ikeuchi Λ1, Ryo Kurazume Λ2, Ko Nishino Λ1, Ryusuke
3 Abstract CAD 3-D ( ) 4 Spin Image Correspondence Grouping 46.1% 17.4% 97.6% ICP [0.6mm/point] 1 CAD [1][2]
![3 Abstract CAD 3-D ( ) 4 Spin Image Correspondence Grouping 46.1% 17.4% 97.6% ICP [0.6mm/point] 1 CAD [1][2]](/thumbs/94/118738978.jpg "3 Abstract CAD 3-D ( ) 4 Spin Image Correspondence Grouping 46.1% 17.4% 97.6% ICP [0.6mm/point] 1 CAD [1][2]")
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
Spin Image [3] 3D Shape Context [4] Spin Image 2 3D Shape Context Shape Index[5] Local Surface Patch[6] DAI [7], [8] [9], [10] Reference Frame SHO[11]
![Spin Image [3] 3D Shape Context [4] Spin Image 2 3D Shape Context Shape Index[5] Local Surface Patch[6] DAI [7], [8] [9], [10] Reference Frame SHO[11]](/thumbs/87/96951219.jpg "Spin Image [3] 3D Shape Context [4] Spin Image 2 3D Shape Context Shape Index[5] Local Surface Patch[6] DAI [7], [8] [9], [10] Reference Frame SHO[11]")
3-D 1,a) 1 1,b) 3 3 3 1% Spin Image 51.6% 93.8% 9 PCL Point Cloud Library Correspondence Grouping 13.5% 10 3 Extraction of 3-D Feature Point for Effect in Object Recognition based on Local Shape Distinctiveness
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
2003/3 Vol. J86 D II No.3 2.3. 4. 5. 6. 2. 1 1 Fig. 1 An exterior view of eye scanner. CCD [7] 640 480 1 CCD PC USB PC 2 334 PC USB RS-232C PC 3 2.1 2
![2003/3 Vol. J86 D II No.3 2.3. 4. 5. 6. 2. 1 1 Fig. 1 An exterior view of eye scanner. CCD [7] 640 480 1 CCD PC USB PC 2 334 PC USB RS-232C PC 3 2.1 2](/thumbs/42/22941906.jpg "2003/3 Vol. J86 D II No.3 2.3. 4. 5. 6. 2. 1 1 Fig. 1 An exterior view of eye scanner. CCD [7] 640 480 1 CCD PC USB PC 2 334 PC USB RS-232C PC 3 2.1 2")
Curved Document Imaging with Eye Scanner Toshiyuki AMANO, Tsutomu ABE, Osamu NISHIKAWA, Tetsuo IYODA, and Yukio SATO 1. Shape From Shading SFS [1] [2] 3 2 Department of Electrical and Computer Engineering,
(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, ryo-f@hiroshima-cu.ac.jp, fukuda@cv.ics.saitama-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
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 2 2 (1) (2) (3) (4) 4 4 AdaBoost 2. [11] Onishi&Yoda [8] Iwashita&Stoica [5] 4 [3] 3. 3 (1) (2) (3)](/thumbs/68/59204737.jpg "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)")
(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
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.
yoo_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 : : : : : : : : : : : : : : :
(a) (b) (c) Canny (d) 1 ( x α, y α ) 3 (x α, y α ) (a) A 2 + B 2 + C 2 + D 2 + E 2 + F 2 = 1 (3) u ξ α u (A, B, C, D, E, F ) (4) ξ α (x 2 α, 2x α y α,
[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
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
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
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
![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](/thumbs/91/106196100.jpg "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")
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
(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
Vol. 44 No. SIG 9(CVIM 7) ) 2) 1) 1 2) 3 7) 1) 2) 3 3) 4) 5) (a) (d) (g) (b) (e) (h) No Convergence? End (f) (c) Yes * ** * ** 1
Vol. 44 No. SIG 9(CVIM 7) July 2003, Robby T. Tan, 1 Estimating Illumination Position, Color and Surface Reflectance Properties from a Single Image Kenji Hara,, Robby T. Tan, Ko Nishino, Atsushi Nakazawa,
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
(a) (b) (c) Fig. 2 2 (a) ; (b) ; (c) (a)configuration of the proposed system; (b)processing flow of the system; (c)the system in use 1 GPGPU (
1 1 1 (a) (b) imperceptible A Realtime and Adaptive Technique for Projection onto Non-Flat Surfaces Using a Mobile Projector Camera System Eiji Seki, 1 Dao Vinh Ninh 1 and Masanori Sugimoto 1 In this paper,
[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
![[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) em53035@nda.ac.jp
IPSJ SIG Technical Report Vol.2010-MPS-77 No /3/5 VR SIFT Virtual View Generation in Hallway of Cybercity Buildings from Video Sequen
VR 1 1 1 1 1 SIFT Virtual View Generation in Hallway of Cybercity Buildings from Video Sequences Sachiyo Yoshida, 1 Masami Takata 1 and Joe Kaduki 1 Appearance of Three-dimensional (3D) building model
% 2 3 [1] Semantic Texton Forests STFs [1] ( ) STFs STFs ColorSelf-Simlarity CSS [2] ii
![% 2 3 [1] Semantic Texton Forests STFs [1] ( ) STFs STFs ColorSelf-Simlarity CSS [2] ii](/thumbs/91/106152959.jpg "% 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
第1章
( ) 2005 1 IC 1... 1 1.1... 1 1.1.1... 1 1.1.2... 1 1.2... 5 1.2.1... 5 1.2.2... 5 1.3... 7 2... 8 2.1... 8 2.1.1... 10 2.1.2... 11 2.1.3... 13 2.1.4... 13 2.2... 16 2.2.1... 19 2.2.2... 21 2.2.3... 23
Miyazaki-3DForum dvi
BRDF http://www.cvl.iis.u-tokyo.ac.jp/ Abstract In order to create a photorealistic VR model, we have to record the appearance of the object from dierent directions under dierent illuminations. In this
スライド 1
swk(at)ic.is.tohoku.ac.jp 2 Outline 3 ? 4 S/N CCD 5 Q Q V 6 CMOS 1 7 1 2 N 1 2 N 8 CCD: CMOS: 9 : / 10 A-D A D C A D C A D C A D C A D C A D C ADC 11 A-D ADC ADC ADC ADC ADC ADC ADC ADC ADC A-D 12 ADC
IPSJ SIG Technical Report Vol.2013-CVIM-188 No /9/2 1,a) D. Marr D. Marr 1. (feature-based) (area-based) (Dense Stereo Vision) van der Ma
,a) D. Marr D. Marr. (feature-based) (area-based) (Dense Stereo Vision) van der Mark [] (Intelligent Vehicle: IV) SAD(Sum of Absolute Difference) Intel x86 CPU SSE2(Streaming SIMD Extensions 2) CPU IV
22_05.dvi
Vol. 1 No. 2 41 49 (July 2008) 3 1 1 3 2 1 1 3 Person-independent Monocular Tracking of Face and Facial Actions Yusuke Sugano 1 and Yoichi Sato 1 This paper presents a monocular method of tracking faces
VRSJ-SIG-MR_okada_79dce8c8.pdf
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. 630-0192 8916-5 E-mail: {kaduya-o,takafumi-t,goshiro,uranishi,miyazaki,kato}@is.naist.jp,.,,.,,,.,,., CG.,,,
2). 3) 4) 1.2 NICTNICT DCRA Dihedral Corner Reflector micro-arraysdcra DCRA DCRA DCRA 3D DCRA PC USB PC PC ON / OFF Velleman K8055 K8055 K8055
1 1 1 2 DCRA 1. 1.1 1) 1 Tactile Interface with Air Jets for Floating Images Aya Higuchi, 1 Nomin, 1 Sandor Markon 1 and Satoshi Maekawa 2 The new optical device DCRA can display floating images in free
1234 Vol. 25 No. 8, pp , 2007 CPS SLAM Study on CPS SLAM 3D Laser Measurement System for Large Scale Architectures Ryo Kurazume,Yukihiro Toba
1234 Vol. 25 No. 8, pp.1234 1242, 2007 CPS SLAM Study on CPS SLAM 3D Laser Measurement System for Large Scale Architectures Ryo Kurazume,Yukihiro Tobata,KoujiMurakami and Tsutomu Hasegawa In order to construct
Computer Security Symposium October ,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) [1] 1 Meiji U
![Computer Security Symposium October ,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) [1] 1 Meiji U](/thumbs/101/150229680.jpg "Computer 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
28 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
[1] SBS [2] SBS Random Forests[3] Random Forests ii
![[1] SBS [2] SBS Random Forests[3] Random Forests ii](/thumbs/74/70333192.jpg "[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
光学
Range Image Sensors Using Active Stereo Methods Kazunori UMEDA and Kenji TERABAYASHI Active stereo methods, which include the traditional light-section method and the talked-about Kinect sensor, are typical
IPSJ SIG Technical Report iphone iphone,,., OpenGl ES 2.0 GLSL(OpenGL Shading Language), iphone GPGPU(General-Purpose Computing on Graphics Proc
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
左カメラ左光源 Z Y 書籍 右カメラ 右光源 X-Y Z X 1: 2 [9 12] [12] NURBS 1 NURBS X-Y [13,14] Hough [15] [13, 14] [15] Structure from motion NURB
![左カメラ左光源 Z Y 書籍 右カメラ 右光源 X-Y Z X 1: 2 [9 12] [12] NURBS 1 NURBS X-Y [13,14] Hough [15] [13, 14] [15] Structure from motion NURB](/thumbs/91/105231415.jpg "左カメラ左光源 Z Y 書籍 右カメラ 右光源 X-Y Z X 1: 2 [9 12] [12] NURBS 1 NURBS X-Y [13,14] Hough [15] [13, 14] [15] Structure from motion NURB")
Correction of Distorted Document Images Using a Stereo Vision System,, Yusuke SUZUKI Atsushi YAMASHITA and Toru KANEKO : {f0630042,tayamas,tmtkane}@ipc.shizuoka.ac.jp 1 [4,5] [1] 3 1 OCR 3 2 3 3 Shape
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
![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](/thumbs/91/107223763.jpg "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")
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,
2. Surface parameterization,,,,,, 2 φ,φ 2 M M 2, M,M 2, ) S 2 Surface parameterization 3 S [4], [5].. ) 0,, 3, [4] φ : R 3 R 2, φ = 0 F φ) = φ 2. 3) 3
![2. Surface parameterization,,,,,, 2 φ,φ 2 M M 2, M,M 2, ) S 2 Surface parameterization 3 S [4], [5].. ) 0,, 3, [4] φ : R 3 R 2, φ = 0 F φ) = φ 2. 3) 3](/thumbs/92/108467150.jpg "2. Surface parameterization,,,,,, 2 φ,φ 2 M M 2, M,M 2, ) S 2 Surface parameterization 3 S [4], [5].. ) 0,, 3, [4] φ : R 3 R 2, φ = 0 F φ) = φ 2. 3) 3")
2,a) surface parameterization, 2 Surface parameterization, ), 2, surface parameterization 2, 2, 2,,, ),,,,,, 2., []., 3 ),,, ), ), 2 ) 2 ) ) 4-- a) hatanaka.kohei@fujitsu.com ), ).,, M, M 2 M,M 2 {φ i
3 1 Table 1 1 Feature classification of frames included in a comic magazine Type A Type B Type C Others 81.5% 10.3% 5.0% 3.2% Fig. 1 A co
1 2 3 3 1 Hough 0.9 0.7 0.9 A Study on Frame Corner Detection of Comic Image Daisuke Ishii, 1 Kei Kawamura, 2 Keiichiro Hoashi, 3 Yasuhiro Takishima 3 and Hiroshi Watanabe 1 In this paper, we propose and
光学
Fundamentals of Projector-Camera Systems and Their Calibration Methods Takayuki OKATANI To make the images projected by projector s appear as desired, it is e ective and sometimes an only choice to capture
Vol1-CVIM-172 No.7 21/5/ Shan 1) 2 2)3) Yuan 4) Ancuti 5) Agrawal 6) 2.4 Ben-Ezra 7)8) Raskar 9) Image domain Blur image l PSF b / = F(
Vol1-CVIM-172 No.7 21/5/27 1 Proposal on Ringing Detector for Image Restoration Chika Inoshita, Yasuhiro Mukaigawa and Yasushi Yagi 1 A lot of methods have been proposed for restoring blurred images due
24 21 21115025 i 1 1 2 5 2.1.................................. 6 2.1.1........................... 6 2.1.2........................... 7 2.2...................................... 8 2.3............................
,,.,.,,.,.,.,.,,.,..,,,, 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
(3.6 ) (4.6 ) 2. [3], [6], [12] [7] [2], [5], [11] [14] [9] [8] [10] (1) Voodoo 3 : 3 Voodoo[1] 3 ( 3D ) (2) : Voodoo 3D (3) : 3D (Welc
![(3.6 ) (4.6 ) 2. [3], [6], [12] [7] [2], [5], [11] [14] [9] [8] [10] (1) Voodoo 3 : 3 Voodoo[1] 3 ( 3D ) (2) : Voodoo 3D (3) : 3D (Welc](/thumbs/65/54056306.jpg "(3.6 ) (4.6 ) 2. [3], [6], [12] [7] [2], [5], [11] [14] [9] [8] [10] (1) Voodoo 3 : 3 Voodoo[1] 3 ( 3D ) (2) : Voodoo 3D (3) : 3D (Welc")
1,a) 1,b) Obstacle Detection from Monocular On-Vehicle Camera in units of Delaunay Triangles Abstract: An algorithm to detect obstacles by using a monocular on-vehicle video camera is developed. Since
Fig. 2 Gaussian surfaces with different standard deviations
Free-Form Deformations Based on Gaussian Functions - Fundamental Theory for Interactive Modeling- Norimasa YOSHIDA, Ken'ya KANOU and Katsuhiro KITAJIMA Interactive and intuitive modeling is one of the
SSII原稿v5.doc
ステレオ計測と多項式曲面表現を利用した歪曲形状書籍画像の歪み補正 Restoration of Distorted Document Images by Using Stereo Measurement and Polynomial Surface Representation 田中友 鈴木優輔 山下淳 金子透 uu Tanaka, usuke Suzuki, Atushi amashita and
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
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
![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](/thumbs/82/86406952.jpg "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")
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
橡上野先生訂正2
(SIS) NII) 101-8430 tel 03-4212-2516 E-mail ueno@nii.ac.jp 1 NII 2 (symbiosis) 2 (parasitism) 2 Knowledge Creation The Symbiotic partnership of University, Government and Industry, Proc. Information Environment
& Vol.5 No (Oct. 2015) TV 1,2,a) , Augmented TV TV AR Augmented Reality 3DCG TV Estimation of TV Screen Position and Ro
TV 1,2,a) 1 2 2015 1 26, 2015 5 21 Augmented TV TV AR Augmented Reality 3DCG TV Estimation of TV Screen Position and Rotation Using Mobile Device Hiroyuki Kawakita 1,2,a) Toshio Nakagawa 1 Makoto Sato
untitled
1 (1) (2) (3) (4) (1) (2) (3) (1) (2) (3) (1) (2) (3) (4) (5) (1) (2) (3) (1) (2) 10 11 12 2 2520159 3 (1) (2) (3) (4) (5) (6) 103 59529 600 12 42 4 42 68 53 53 C 30 30 5 56 6 (3) (1) 7 () () (()) () ()
1 Table 1: Identification by color of voxel Voxel Mode of expression Nothing Other 1 Orange 2 Blue 3 Yellow 4 SSL Humanoid SSL-Vision 3 3 [, 21] 8 325
![1 Table 1: Identification by color of voxel Voxel Mode of expression Nothing Other 1 Orange 2 Blue 3 Yellow 4 SSL Humanoid SSL-Vision 3 3 [, 21] 8 325](/thumbs/91/105378470.jpg "1 Table 1: Identification by color of voxel Voxel Mode of expression Nothing Other 1 Orange 2 Blue 3 Yellow 4 SSL Humanoid SSL-Vision 3 3 [, 21] 8 325")
社団法人人工知能学会 Japanese Society for Artificial Intelligence 人工知能学会研究会資料 JSAI Technical Report SIG-Challenge-B3 (5/5) RoboCup SSL Humanoid A Proposal and its Application of Color Voxel Server for RoboCup SSL
DEIM Forum 2017 E Netflix (Video on Demand) IP 4K [1] Video on D
![DEIM Forum 2017 E Netflix (Video on Demand) IP 4K [1] Video on D](/thumbs/104/162687582.jpg "DEIM Forum 2017 E Netflix (Video on Demand) IP 4K [1] Video on D")
DEIM Forum 2017 E1-1 700-8530 3-1-1 E-mail: inoue-y@mis.cs.okayama-u.ac.jp, gotoh@cs.okayama-u.ac.jp 1. Netflix (Video on Demand) IP 4K [1] Video on Demand ( VoD) () 2. 2. 1 VoD VoD 2. 2 AbemaTV VoD VoD
1 7.35% 74.0% linefeed point c 200 Information Processing Society of Japan
1 2 3 Incremental Linefeed Insertion into Lecture Transcription for Automatic Captioning Masaki Murata, 1 Tomohiro Ohno 2 and Shigeki Matsubara 3 The development of a captioning system that supports the
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.2015-MUS-107 No /5/23 HARK-Binaural Raspberry Pi 2 1,a) ( ) HARK 2 HARK-Binaural A/D Raspberry Pi 2 1.
HARK-Binaural Raspberry Pi 2 1,a) 1 1 1 2 3 () HARK 2 HARK-Binaural A/D Raspberry Pi 2 1. [1,2] [2 5] () HARK (Honda Research Institute Japan audition for robots with Kyoto University) *1 GUI ( 1) Python
DEIM Forum 2012 E Web Extracting Modification of Objec
DEIM Forum 2012 E4-2 670 0092 1 1 12 E-mail: nd11g028@stshse.u-hyogo.ac.jp, {dkitayama,sumiya}@shse.u-hyogo.ac.jp Web Extracting Modification of Objects for Supporting Map Browsing Junki MATSUO, Daisuke
Microsoft Word - toyoshima-deim2011.doc
DEIM Forum 2011 E9-4 252-0882 5322 252-0882 5322 E-mail: t09651yt, sashiori, kiyoki @sfc.keio.ac.jp CBIR A Meaning Recognition System for Sign-Logo by Color-Shape-Based Similarity Computations for Images
IPSJ 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.
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
IPSJ SIG Technical Report Vol.2016-CG-165 No.16 Vol.2016-DCC-14 No.16 Vol.2016-CVIM-204 No /11/10 1 Marco Visentini Scarzanella (AR) (M
1 Marco Visentini Scarzanella 1 2 2 1 (AR) (MR) AR MR 1. (AR) (MR) AR MR AR MR Scarzanella [1], [2] 1 2 1 Department of Information and Biomedical Engineering, Kagoshima University, 1-21-4, Kohrimoto,
IPSJ SIG Technical Report Vol.2015-CVIM-196 No /3/6 1,a) 1,b) 1,c) U,,,, The Camera Position Alignment on a Gimbal Head for Fixed Viewpoint Swi
1,a) 1,b) 1,c) U,,,, The Camera Position Alignment on a Gimbal Head for Fixed Viewpoint Swiveling using a Misalignment Model Abstract: When the camera sets on a gimbal head as a fixed-view-point, it is
floating horizon algorithm 1 DEM [ 01] [Luebke01] LDI Layered Depth Image [Shade98] DEM Digital Elevation Model Height field 1 10 9
![floating horizon algorithm 1 DEM [ 01] [Luebke01] LDI Layered Depth Image [Shade98] DEM Digital Elevation Model Height field 1 10 9](/thumbs/41/22764431.jpg "floating horizon algorithm 1 DEM [ 01] [Luebke01] LDI Layered Depth Image [Shade98] DEM Digital Elevation Model Height field 1 10 9")
Introduction to Point-based Graphics Tadahiro FUJIMOTO Kouichi KONNO Norishige CHIBA Faculty of Engineering, Iwate University Point-based Graphics Eurographics 2002 2003 [ 01 02 02 03] LOD Level of Detail
Vol.9-CVIM-168 No.9 9/8/31 2) ROI(Region of Interest) ROI ROI 1) ROI 4) 5) ROI 3. (ROI ) ROI thin plate spline ROI 3.1 4) N {P j j = 1, 2,, N} P j x j
Vol.9-CVIM-168 No.9 9/8/31 1 1 FDG-PET/CT 1, X-CT 1) An Approach to Unification of Feature Extraction and Optimization for Registration of Organs in Medical Images Hidekata Hontani 1 and Wataru Watanabe
2006 3D M
2006 3D M0103190 2006 3D : M0103190 3DCG 3DCG 3DCG 3D 3 3D SKETCH 3D Teddy 1 1 2 3DModelingTool Dice 5 2.1................................... 5 2.2.............................. 6 2.2.1.............. 6
1 3DCG [2] 3DCG CG 3DCG [3] 3DCG 3 3 API 2 3DCG 3 (1) Saito [4] (a) 1920x1080 (b) 1280x720 (c) 640x360 (d) 320x G-Buffer Decaudin[5] G-Buffer D
![1 3DCG [2] 3DCG CG 3DCG [3] 3DCG 3 3 API 2 3DCG 3 (1) Saito [4] (a) 1920x1080 (b) 1280x720 (c) 640x360 (d) 320x G-Buffer Decaudin[5] G-Buffer D](/thumbs/91/105248386.jpg "1 3DCG [2] 3DCG CG 3DCG [3] 3DCG 3 3 API 2 3DCG 3 (1) Saito [4] (a) 1920x1080 (b) 1280x720 (c) 640x360 (d) 320x G-Buffer Decaudin[5] G-Buffer D")
3DCG 1) ( ) 2) 2) 1) 2) Real-Time Line Drawing Using Image Processing and Deforming Process Together in 3DCG Takeshi Okuya 1) Katsuaki Tanaka 2) Shigekazu Sakai 2) 1) Department of Intermedia Art and Science,
i CG ICP(Iterative Closest Point) ICP 2 2 ICP 2 2 2
24 2 2 i CG 3 3 1 1 1 + 1 ICP(Iterative Closest Point) ICP 2 2 ICP 2 2 2 ii 2 2 ICP 2 1 1 1 2 1 ICP 2 1.2 /frame 1.8 /frame 2 1 3 ICP ICP 2 Comparing performance among registration methods of multi-view
形状変形による古文書画像のシームレス合成
Use of Shape Deformation to Seamlessly Stitch Historical Document Images Wei Liu Wei Fan Li Chen Sun Jun あらまし 1 2 Abstract In China, efforts are being made to preserve historical documents in the form
IPSJ SIG Technical Report Vol.2012-ICS-167 No /3/ ,,., 3, 3., 3, 3. Automatic 3D Map Generation by Using a Small Unmanned Vehicle
1. 3 1 2 2 3,,., 3, 3., 3, 3. Automatic 3D Map Generation by Using a Small Unmanned Vehicle Hiroki Osaki, 1 Ken Watanabe 2 and Katashi Nagao 2 While 3D maps are useful to visualize complicated shapes of
2 (March 13, 2010) N Λ a = i,j=1 x i ( d (a) i,j x j ), Λ h = N i,j=1 x i ( d (h) i,j x j ) B a B h B a = N i,j=1 ν i d (a) i,j, B h = x j N i,j=1 ν i
1. A. M. Turing [18] 60 Turing A. Gierer H. Meinhardt [1] : (GM) ) a t = D a a xx µa + ρ (c a2 h + ρ 0 (0 < x < l, t > 0) h t = D h h xx νh + c ρ a 2 (0 < x < l, t > 0) a x = h x = 0 (x = 0, l) a = a(x,
,,, 2 ( ), $[2, 4]$, $[21, 25]$, $V$,, 31, 2, $V$, $V$ $V$, 2, (b) $-$,,, (1) : (2) : (3) : $r$ $R$ $r/r$, (4) : 3
![,,, 2 ( ), $[2, 4]$, $[21, 25]$, $V$,, 31, 2, $V$, $V$ $V$, 2, (b) $-$,,, (1) : (2) : (3) : $r$ $R$ $r/r$, (4) : 3](/thumbs/91/104771717.jpg ",,, 2 ( ), $[2, 4]$, $[21, 25]$, $V$,, 31, 2, $V$, $V$ $V$, 2, (b) $-$,,, (1) : (2) : (3) : $r$ $R$ $r/r$, (4) : 3")
1084 1999 124-134 124 3 1 (SUGIHARA Kokichi),,,,, 1, [5, 11, 12, 13], (2, 3 ), -,,,, 2 [5], 3,, 3, 2 2, -, 3,, 1,, 3 2,,, 3 $R$ ( ), $R$ $R$ $V$, $V$ $R$,,,, 3 2 125 1 3,,, 2 ( ), $[2, 4]$, $[21, 25]$,
A Navigation Algorithm for Avoidance of Moving and Stationary Obstacles for Mobile Robot Masaaki TOMITA*3 and Motoji YAMAMOTO Department of Production
A Navigation Algorithm for Avoidance of Moving and Stationary Obstacles for Mobile Robot Masaaki TOMITA*3 and Motoji YAMAMOTO Department of Production System Engineering, Kyushu Polytecnic College, 1665-1
IPSJ SIG Technical Report Vol.2014-MBL-70 No.49 Vol.2014-UBI-41 No /3/15 2,a) 2,b) 2,c) 2,d),e) WiFi WiFi WiFi 1. SNS GPS Twitter Facebook Twit
2,a) 2,b) 2,c) 2,d),e) WiFi WiFi WiFi 1. SNS GPS Twitter Facebook Twitter Ustream 1 Graduate School of Information Science and Technology, Osaka University, Japan 2 Cybermedia Center, Osaka University,
情報処理学会研究報告 IPSJ SIG Technical Report Vol.2014-GN-90 No.6 Vol.2014-CDS-9 No.6 Vol.2014-DCC-6 No /1/23 Bullet Time 1,a) 1 Bullet Time Bullet Time
Bullet Time 1,a) 1 Bullet Time Bullet Time Generation Technique and Eveluation on High-Resolution Bullet-Time Camera Work Ryuuki Sakamoto 1,a) Ding Chen 1 Abstract: The multi-camera environment have been
情報処理学会研究報告 IPSJ SIG Technical Report Vol.2011-CVIM-178 No /9/ Model-based Human Torso 3D Shape Estimation Shunta Saito, 1 Makiko
3 1 2 2 1 2 3 Model-based Human Torso 3D Shape Estimation Shunta Saito, 1 Makiko Kochi, 2 Masaaki Mochimaru 2 and Yoshimitsu Aoki 1 This paper attempts to estimate a 3D shape of human torso from 2 pictures
DEIM Forum 2019 A7-1 Flexible Distance-based Hashing mori
DEIM Forum 2019 A7-1 Flexible Distance-based Hashing 731 3194 E-mail: mc66023@e.hiroshima-cu.ac.jp,{wakaba,s naga,inagi,yoko}@hiroshima-cu.ac.jp, morikei18@gmail.com Flexible Distance-based Hashing(FDH)
IPSJ SIG Technical Report Vol.2014-DBS-159 No.6 Vol.2014-IFAT-115 No /8/1 1,a) 1 1 1,, 1. ([1]) ([2], [3]) A B 1 ([4]) 1 Graduate School of Info
![IPSJ SIG Technical Report Vol.2014-DBS-159 No.6 Vol.2014-IFAT-115 No /8/1 1,a) 1 1 1,, 1. ([1]) ([2], [3]) A B 1 ([4]) 1 Graduate School of Info](/thumbs/101/149057081.jpg "IPSJ SIG Technical Report Vol.2014-DBS-159 No.6 Vol.2014-IFAT-115 No /8/1 1,a) 1 1 1,, 1. ([1]) ([2], [3]) A B 1 ([4]) 1 Graduate School of Info")
1,a) 1 1 1,, 1. ([1]) ([2], [3]) A B 1 ([4]) 1 Graduate School of Information Science and Technology, Osaka University a) kawasumi.ryo@ist.osaka-u.ac.jp 1 1 Bucket R*-tree[5] [4] 2 3 4 5 6 2. 2.1 2.2 2.3
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
![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](/thumbs/72/67985311.jpg "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")
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
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],
![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],](/thumbs/92/110933465.jpg "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],")
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)
Convolutional Neural Network A Graduation Thesis of College of Engineering, Chubu University Investigation of feature extraction by Convolution
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
Lyra 2 2 2 X Y X Y ivis Designer Lyra ivisdesigner Lyra ivisdesigner 2 ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) (1) (2) (3) (4) (5) Iv Studio [8] 3 (5) (4) (1) (
![Lyra 2 2 2 X Y X Y ivis Designer Lyra ivisdesigner Lyra ivisdesigner 2 ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) (1) (2) (3) (4) (5) Iv Studio [8] 3 (5) (4) (1) (](/thumbs/42/23098909.jpg "Lyra 2 2 2 X Y X Y ivis Designer Lyra ivisdesigner Lyra ivisdesigner 2 ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) (1) (2) (3) (4) (5) Iv Studio [8] 3 (5) (4) (1) (")
1,a) 2,b) 2,c) 1. Web [1][2][3][4] [5] 1 2 a) ito@iplab.cs.tsukuba.ac.jp b) misue@cs.tsukuba.ac.jp c) jiro@cs.tsukuba.ac.jp [6] Lyra[5] ivisdesigner[6] [7] 2 Lyra ivisdesigner c 2012 Information Processing
応力とひずみ.ppt
in yukawa@numse.nagoya-u.ac.jp 2 3 4 5 x 2 6 Continuum) 7 8 9 F F 10 F L L F L 1 L F L F L F 11 F L F F L F L L L 1 L 2 12 F L F! A A! S! = F S 13 F L L F F n = F " cos# F t = F " sin# S $ = S cos# S S
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]., [
![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]., [](/thumbs/94/121682292.jpg "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]., [")
,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],
WISS 2018 [2 4] [5,6] Query-by-Dancing Query-by- Dancing Cao [1] OpenPose 2 Ghias [7] Query by humming Chen [8] Query by rhythm Jang [9] Query-by-tapp
![WISS 2018 [2 4] [5,6] Query-by-Dancing Query-by- Dancing Cao [1] OpenPose 2 Ghias [7] Query by humming Chen [8] Query by rhythm Jang [9] Query-by-tapp](/thumbs/91/107555259.jpg "WISS 2018 [2 4] [5,6] Query-by-Dancing Query-by- Dancing Cao [1] OpenPose 2 Ghias [7] Query by humming Chen [8] Query by rhythm Jang [9] Query-by-tapp")
Query-by-Dancing: WISS 2018. Query-by-Dancing Query-by-Dancing 1 OpenPose [1] Copyright is held by the author(s). DJ DJ DJ WISS 2018 [2 4] [5,6] Query-by-Dancing Query-by- Dancing Cao [1] OpenPose 2 Ghias
untitled
IT E- IT http://www.ipa.go.jp/security/ CERT/CC http://www.cert.org/stats/#alerts IPA IPA 2004 52,151 IT 2003 12 Yahoo 451 40 2002 4 18 IT 1/14 2.1 DoS(Denial of Access) IDS(Intrusion Detection System)
1(a) (b),(c) - [5], [6] Itti [12] [13] gaze eyeball head 2: [time] [7] Stahl [8], [9] Fang [1], [11] 3 -
![1(a) (b),(c) - [5], [6] Itti [12] [13] gaze eyeball head 2: [time] [7] Stahl [8], [9] Fang [1], [11] 3 -](/thumbs/89/100816452.jpg "1(a) (b),(c) - [5], [6] Itti [12] [13] gaze eyeball head 2: [time] [7] Stahl [8], [9] Fang [1], [11] 3 -")
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
九州大学学術情報リポジトリ Kyushu University Institutional Repository 多視点動画像処理による 3 次元モデル復元に基づく自由視点画像生成のオンライン化 : PC クラスタを用いた実現法 上田, 恵九州大学システム情報科学研究院知能システム学部門 有田, 大
九州大学学術情報リポジトリ Kyushu University Institutional Repository 多視点動画像処理による 3 次元モデル復元に基づく自由視点画像生成のオンライン化 : PC クラスタを用いた実現法 上田, 恵九州大学システム情報科学研究院知能システム学部門 有田, 大作九州大学システム情報科学研究院知能システム学部門 谷口, 倫一郎九州大学システム情報科学研究院知能システム学部門
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