204/3 Vol. J97 A o. 3 HDR HDR wavelet-shrinkage wavelet wavelet wavelet-shrinkage wavelet RGB wavelet wavelet wavelet HDR wavelet 2.
|
|
|
- あきお とこたに
- 5 years ago
- Views:
Transcription
1 HDR Multiple Exposure Image Fusion with oise Reduction for HDR Acquisition Ryo MATSUOKA, Takao JIO, and Masahiro OKUDA HDR (High Dynamic Range) HDR LDR (Low Dynamic Range) HDR HDR HDR. (Human Visual System (HVS)) CCD CMOS High Dynamic Range Image (HDR ) HDR CG The University of Kitakyushu, Kitakyushu-shi, Japan Toyohashi University of Technology, Toyohashi-shi, Japan [] HDR [2] [8] HDR ISO HDR A Vol. J97 A o. 3 pp c
2 204/3 Vol. J97 A o. 3 HDR HDR wavelet-shrinkage wavelet wavelet wavelet-shrinkage wavelet RGB wavelet wavelet wavelet HDR wavelet 2. 2 R Y () [4] Y = R t, () t Y i i RGB 8bit [0, ] i = g (Y ) (2) () (2) R R = f (i) /t (3) f (x) f (x) =g (x) Mitsunaga [4] f f Fig. Our method flow. 20
3 HDR 2 Fig. 2 Inverse camera response curve. CAO EOS 20D f 2 f RGB f G n= I m = ωm(n)rm(n) n= ωm(n), (4) R m(n) n m ω m(n) wavelet waveletshrinkage ( ) ( ) shrinkage (3) (5) (6) L H M(n)M(n +)(Rm(n)+Rm(n +)) L m(n) =, 2 (5) M(n)M(n +)(Rm(n) Rm(n +)) H m(n) =, 2 (6) n =,, n n = R m (n) n m = {R, G, B} M(n) n R, G, B 0( ) ( ) 0 (6) H m (n) { Hm(n) 0, if H m=g (n) < threshold =, H m(n), otherwise (7) (7) Hm (n) 0 ( ) G 0 R, B 0 RGB G RGB R m (n) =L m (n)+h m (n). (8) HDR 2
4 204/3 Vol. J97 A o. 3 3 : ( ) ( ) Fig. 3 By multiple exposure image noise removal: (left) low exposure image, (right) denoising result. ωm(n) M(n)M(n +)(ωm(n)+ωm(n + )), 2 = if H m=g(n) < threshold M(n)ω m(n). otherwise (9) (4) wavelet (3) R m(n) R m (n) =ω m (n) R m (n). (0) n =,..., (8) (9) n = (3) (0) Haar wavelet wavelet-shrinkage wavelet 3. 3 wavelet-shrinkage HDR HDR wavelet-shrinkage (0) Haar wavelet wavelet wavelet (L) (H) (LL, HL, LH, HH) LL wavelet-shrinkage wavelet wavelet [9] [2] wavelet-shrinkage Hard Shrinkage [] min h 0 + λ(h l) 2, λ > 0, () h l wavelet h wavelet λ () wavelet h 0, if l < /λ =, (2) l, otherwise (2) wavelet wavelet 0 waveletshrinkage 22
5 HDR wavelet-shrinkage min E (h) = h 0 + λ h (h l(n)) 2, (3) n= l(n) (0) n wavelet (HL, LH, HH) wavelet h HDR wavelet λ shrinkage m wavelet ( ) 2 0, if λ h n= = l(n) >0. n= l(n), otherwise (4) (2) waveletshrinkage [9] [2] wavelet 0 = (4) (2) (4) wavelet-shrinkage (4) (4) [2] [6] [2], [3] CCD CMOS 4 : ( ) Hat ( ) Fig. 4 Conventional two weight functions: (left) Hat-type, (right) Gauss-type. ( [3]) Y = x + a σ + a 2σ 2 x, (5) x Y σ σ 2 a a y y = g(y )+σ q σ q y f(y) =f (g (Y )+σ q), f(y) x f(g(y )+σ q) x f(g(y )) + f (g(y ))σ q x = Y + f (g(y ))σ q x = a σ + a 2σ 2 x + f (g(y ))σ q (6) g f f(g(y )) = Y t n n = ( a σ + a 2σ 2 x + f (g (Y )) σ q ) /t. (7) 23
6 204/3 Vol. J97 A o. 3 ω 0(i) =m(i) ((a σ +a 2σ 2 x+f (g(y ))σ q)/t) m(i) ((a σ +a 2σ 2 Y +f (g(y ))σ q)/t) = m(i) ((a σ +a 2σ 2 f(i)+f (i)σ q)/t) (8) m (i) i 0 0 x x Y RAW (8) (8) ω (i) =m (i) (b + b 2f (i)+b 3f (i)) /t (9) 2 3 b, b 2, b 3 b =0.00, b 2 =0.99, b 3 =0.0 b 3 b 2 5. Bilateral Filter [4] BM3D [5] 5. CAO EOS 20D 3 ISO ISO ISO600 Ground truth (HDR ) 5 ( 3 5 = 45 ) 5 (scene, scene2) scene /8sec, /2sec, 2sec, scene2 /50sec, /3sec, /3sec HDR HDR LDR 5 HDR Ground truth HDR (oisy image) 5 HDR (scene, scene2) Fig. 5 Tonemapped HDR sample images (scene, scene2). 24
![HDR Bilateral Filter [4] BM3D [5] oisy image HDR Bilateral](/docs-images/91/104924882/images/7-0.jpg "Filter [4] HDR LDR PSR SSIM [7] LDR (7) 0.0008 0.")
![0020 0 33% (7) Hard Shrinkage [] 5.](/docs-images/91/104924882/images/7-1.jpg "2 6 HDR MATLAB tonemap LDR Ward [6] 6 Bilateral Filter [4] (a)")
7 HDR Bilateral Filter [4] BM3D [5] oisy image HDR Bilateral Filter [4] HDR LDR PSR SSIM [7] LDR (7) % (7) Hard Shrinkage [] HDR MATLAB tonemap LDR Ward [6] 6 Bilateral Filter [4] (a) (b) (c) 6 HDR : ( ) Ground truth Bilateral Filter BM3D Fig. 6 Tonemapped HDR images: (from left to right) Ground truth, oisy image, our method, Bilateral Filter, BM3D. 25
8 204/3 Vol. J97 A o. 3 BM3D [5] 6(a) BM3D [5] BM3D [5] BM3D [5] 6(b) BM3D [5] 6(c) shrinkage BM3D [5] 5. 3 PSR SSIM [7] PSR ( ) MAX 2 PSR =0 log 0 (20) (Ildr I ldr )2 I ldr Ground truth I ldr HDR Bilateral Filter [4] BM3D [5] oisy image Ground truth oisy image 4.2. HDR oisy image Ground truth PSR Table Value of PSR, (R.L.T.: Reinhard Local Tonemap, R.G.T.: Reinhard Global Tonemap). Tone-mapping R.L.T. R.G.T. CLAHE MATLAB oisy image (scene) (scene2) our method (scene) (scene2) Bilateral Filter (scene) (scene2) BM3D (scene) (scene2) SSIM Table 2 Value of SSIM, (R.L.T.: Reinhard Local Tonemap, R.G.T.: Reinhard Global Tonemap). Tone-mapping R.L.T. R.G.T. CLAHE MATLAB oisy image (scene) (scene2) our method (scene) (scene2) Bilateral Filter (scene) (scene2) BM3D (scene) (scene2) Reinhard [8], CLAHE [9], MATLAB tonemap 2 HDR LDR HDR SR SR HDR HDR HDR-VDP-2 [20] HDR-VDP-2 [20] 2 Reinhard [8] 7 Reinhard [8] 26
![HDR 7 Reinhard [8] HDR : ( ) Ground truth Bilateral Filter BM3D Fig.](/docs-images/91/104924882/images/9-0.jpg "7 Tonemapped HDR images by Reinhard Local Tonemap: (from left to right) Ground truth, oisy image, our method, Bilateral Filter, BM3D.")
![7 BM3D [5] Bilateral Filter [4] CLAHE [9] MATLAB BM3D [5] Bilateral Filter [4] Reinhard [8] MATLAB Bilateral Filter [4] BM3D [5] CLAHE [9] 8 Hat Fig.](/docs-images/91/104924882/images/9-1.jpg "8 (upper) conventional method (Fig.")
9 HDR 7 Reinhard [8] HDR : ( ) Ground truth Bilateral Filter BM3D Fig. 7 Tonemapped HDR images by Reinhard Local Tonemap: (from left to right) Ground truth, oisy image, our method, Bilateral Filter, BM3D. 7 BM3D [5] Bilateral Filter [4] CLAHE [9] MATLAB BM3D [5] Bilateral Filter [4] Reinhard [8] MATLAB Bilateral Filter [4] BM3D [5] CLAHE [9] 8 Hat Fig. 8 (upper) conventional method (Fig. 2 left: Hat function), (lower left) our method quantization noise suppression, (lower right) our method sensor noise suppression. SSIM [7] PSR 5. 4 CLAHE [9] 8 Hat b =0.00, b 2 =0.0, b 3 =0.95 b =0.00, b 2 =0.99, b 3 =0.0 27
10 204/3 Vol. J97 A o. 3 9 : ( ) ( ) Fig. 9 Proposed two weight functions: (left) quantization noise suppression, (right) sensor noise suppression Ground truth HDR Ground truth onlinearsr (SR) HDR-VDP-2 [20] HDR SR HDR HDR SR s(x) =L 2 x max (2) x +2.6x Table 3 Quantitative evaluation using multiple exposure image with additional Gaussian noises (BRF: Bilateral Filter). oisy our BRF BM3D SR (scene) (scene2) HDR-VDP-2 (scene) (scene2) s x L max (2) Daly [2] HDR-VDP-2 [20] Rafal HDR 3 HDR- VDP-2 [20] 3 Bilateral Filter [4] BM3D 0 HDR Reinhard [8] LDR 0 Bilateral Filter [4] BM3D [5] BM3D [5] MATLAB ( ) sec BM3D [5] 36sec BM3D [5] 28
![HDR 0 Reinhard [8] HDR : ( ) Ground truth Bilateral Filter BM3D Fig.](/docs-images/91/104924882/images/11-0.jpg "0 Tonemapped HDR images by Reinhard Global Tonemap: (from left to right) Ground truth, oisy image, our method, Bilateral Filter, BM3D. Bilateral Filter [4] 6.")
11 HDR 0 Reinhard [8] HDR : ( ) Ground truth Bilateral Filter BM3D Fig. 0 Tonemapped HDR images by Reinhard Global Tonemap: (from left to right) Ground truth, oisy image, our method, Bilateral Filter, BM3D. Bilateral Filter [4] 6. HDR wavelet-shrinkage HDR wavelet-shrinkage BM3D [5] (BM3D [5]) (B)( ), JST A-step KDDI [] E. Reinhard, S. Pattanaik, G. Ward, and P. Debevec, High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (Morgan Kaufmann Series in Computer Graphics and Geometric Modeling), Morgan Kaufmann Publisher, [2] P.E. Debevec and J. Malik, Recovering high dynamic range radiance maps from photographs, Proc. SIGGRAPH 97, Computer Graphics Proceedings, pp , 997. [3] S. Mann and R. Picard, On being undigital with digital cameras: Extending dynamic range by combining differently exposed pictures, Proc. IS&T 46th Annual Conference (May 995), pp [4] T. Mitsunaga and S.K. ayer, Radiometric self calibration, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol., pp , June 999. [5] S.W. Hasinoff, F. Durand, and W.T. Freeman, oise-optimal capture for high dynamic range photography, 200 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp , June 200. [6] A. Akyuz and E. Reinhard, oise reduction in high dynamic range imaging, J. Visual Communication and Image Representation, vol.8, pp , [7] M. Granados, B. Ajdin, M. Wand, C. Theobalt, H.-P. Seidel, and H. Lensch, Optimal HDR reconstruction with linear digital cameras, 200 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp , June 200. [8] T. Jinno and M. Okuda, Motion blur free HDR image acquisition using multiple exposures, IEEE International Conference on Image Processing, pp , Oct [9] R.H. Chan, R.F. Chan, L. Shen, and Z. Shen, Wavelet algorithms for high-resolution image reconstruction, SIAM Journal on Scientific Computing, vol.24, pp , [0] T. Saito,. Fujii, and T. Komatsu, Iterative soft color-shrinkage for color-image denoising, IEEE International Conference on Image Processing, pp , ov [] M. Elad, Why simple shrinkage is still relevant for redundant representations?, IEEE Trans. Inf. Theory, vol.52, no.2, pp , Dec [2] D.L. Donoho, Denoising by soft thresholding, 29
![204/3 Vol. J97 A o. 3 IEEE Trans. Inf. Theory, vol.4, no.3, pp.63 627, May 995. [3] T. Buades, Y. Lou, J.M. Morel, and Z.](/docs-images/91/104924882/images/12-0.jpg "Tang, A note on multi-image denoising, International Workshop on Local and on-local Approximation in Image Processing, pp. 5, Aug. 2009. [4] C. Tomasi and R.")
![Manduchi, Bilateral filtering for gray and color images, Proc. IEEE 6th Inter. Conf. Computer Vision, pp.839 846, 988. [5] K. Dabov, A. Foi, V. Katkovnik, and K.](/docs-images/91/104924882/images/12-1.jpg "Egiazarian, Image denoising by 3D transform-domain collaborative filtering, IEEE Trans. Image Process., vol.6, no.8, pp.2080 2095, Aug. 2007. [6] G.J. Ward, H. Rushmeier, and C.")
![Piatko, A visibility matching tone reproduction operator for high dynamic range scenes, IEEE Trans. Vis. Comput. Graphics, vol.3, no.4, pp.29 306, Dec. 997. [7] Z. Wang, A.C. Bovik, H.R.](/docs-images/91/104924882/images/12-2.jpg "Sheikh, and E.P. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process., vol.3, no.4, pp.600 62, 2004. [8] E. Reinhard, M. Stark, P.")
12 204/3 Vol. J97 A o. 3 IEEE Trans. Inf. Theory, vol.4, no.3, pp , May 995. [3] T. Buades, Y. Lou, J.M. Morel, and Z. Tang, A note on multi-image denoising, International Workshop on Local and on-local Approximation in Image Processing, pp. 5, Aug [4] C. Tomasi and R. Manduchi, Bilateral filtering for gray and color images, Proc. IEEE 6th Inter. Conf. Computer Vision, pp , 988. [5] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, Image denoising by 3D transform-domain collaborative filtering, IEEE Trans. Image Process., vol.6, no.8, pp , Aug [6] G.J. Ward, H. Rushmeier, and C. Piatko, A visibility matching tone reproduction operator for high dynamic range scenes, IEEE Trans. Vis. Comput. Graphics, vol.3, no.4, pp , Dec [7] Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process., vol.3, no.4, pp , [8] E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, Photographic tone reproduction for digital images, ACM Trans. Graphics, vol.2, no.3, pp , July 2002 (Proc. SIGGRAPH 2002). [9] K. Zuiderveld, Contrast limited adaptive histogram equalization, Graphic Gems IV, pp , Academic Press Professional, San Diego, 994. [20] R. Mantiuk K.J. Kim, A.G. Rempel, and W. Heidrich, HDR-VDP-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions, ACM Trans. Graphics, vol.30, pp. 4, July 20. [2] S. Daly, The visible differences predictor: An algorithm for the assessment of image fidelity, in Digital Image and Human Vision, ed. A.B. Watson, pp , MIT Press, Cambridge, MA, 993. (4) (3) E(h) h =0 h 0 E(h) =+ λ (h l(n)) 2 (A ) n= h = n= l(n) (i) h =0 (ii) h = n= l(n) ( E(h) =+ λ l(k) n= k= ) 2 ( ) ) 2 l(k) l(n)+l(n) 2 k= ( ) 2 = λ l(n) + λ l(n) 2 n= n= (A 3) h ( ) 2 0, if λ h n= = l(n) >0. n= l(n), otherwise (A 4) E(0) = λ l(n) 2 n= (A 2) 220
4 4 2 RAW 4 4 4 (PCA) 4 4 4 4 RAW RAW [5] 4 RAW 4 Park [12] Park 2 RAW RAW 2 RAW y = Mx + n. (1) y RAW x RGB M CFA n.. R G B σr 2, σ2 G, σ2 B D n ( )
![4 4 2 RAW 4 4 4 (PCA) 4 4 4 4 RAW RAW [5] 4 RAW 4 Park [12] Park 2 RAW RAW 2 RAW y = Mx + n. (1) y RAW x RGB M CFA n.. R G B σr 2, σ2 G, σ2 B D n ( )](/thumbs/40/20574692.jpg "4 4 2 RAW 4 4 4 (PCA) 4 4 4 4 RAW RAW [5] 4 RAW 4 Park [12] Park 2 RAW RAW 2 RAW y = Mx + n. (1) y RAW x RGB M CFA n.. R G B σr 2, σ2 G, σ2 B D n ( )")
RAW 4 E-mail: hakiyama@ok.ctrl.titech.ac.jp Abstract RAW RAW RAW RAW RAW 4 RAW RAW RAW 1 (CFA) CFA Bayer CFA [1] RAW CFA 1 2 [2, 3, 4, 5]. RAW RAW RAW RAW 3 [2, 3, 4, 5] (AWGN) [13, 14] RAW 2 RAW RAW RAW
(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
(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
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,
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
H(ω) = ( G H (ω)g(ω) ) 1 G H (ω) (6) 2 H 11 (ω) H 1N (ω) H(ω)= (2) H M1 (ω) H MN (ω) [ X(ω)= X 1 (ω) X 2 (ω) X N (ω) ] T (3)
![H(ω) = ( G H (ω)g(ω) ) 1 G H (ω) (6) 2 H 11 (ω) H 1N (ω) H(ω)= (2) H M1 (ω) H MN (ω) [ X(ω)= X 1 (ω) X 2 (ω) X N (ω) ] T (3)](/thumbs/92/108069141.jpg "H(ω) = ( G H (ω)g(ω) ) 1 G H (ω) (6) 2 H 11 (ω) H 1N (ω) H(ω)= (2) H M1 (ω) H MN (ω) [ X(ω)= X 1 (ω) X 2 (ω) X N (ω) ] T (3)")
72 12 2016 pp. 777 782 777 * 43.60.Pt; 43.38.Md; 43.60.Sx 1. 1 2 [1 8] Flexible acoustic interface based on 3D sound reproduction. Yosuke Tatekura (Shizuoka University, Hamamatsu, 432 8561) 2. 2.1 3 M
h(n) x(n) s(n) S (ω) = H(ω)X(ω) (5 1) H(ω) H(ω) = F[h(n)] (5 2) F X(ω) x(n) X(ω) = F[x(n)] (5 3) S (ω) s(n) S (ω) = F[s(n)] (5
![h(n) x(n) s(n) S (ω) = H(ω)X(ω) (5 1) H(ω) H(ω) = F[h(n)] (5 2) F X(ω) x(n) X(ω) = F[x(n)] (5 3) S (ω) s(n) S (ω) = F[s(n)] (5](/thumbs/91/106545489.jpg "h(n) x(n) s(n) S (ω) = H(ω)X(ω) (5 1) H(ω) H(ω) = F[h(n)] (5 2) F X(ω) x(n) X(ω) = F[x(n)] (5 3) S (ω) s(n) S (ω) = F[s(n)] (5")
1 -- 5 5 2011 2 1940 N. Wiener FFT 5-1 5-2 Norbert Wiener 1894 1912 MIT c 2011 1/(12) 1 -- 5 -- 5 5--1 2008 3 h(n) x(n) s(n) S (ω) = H(ω)X(ω) (5 1) H(ω) H(ω) = F[h(n)] (5 2) F X(ω) x(n) X(ω) = F[x(n)]
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
3 3 3 Knecht (2-3fps) AR [3] 2. 2 Debevec High Dynamic Range( HDR) [4] HDR Derek [5] 2. 3 [6] 3. [6] x E(x) E(x) = 2π π 2 V (x, θ i, ϕ i )L(θ
![3 3 3 Knecht (2-3fps) AR [3] 2. 2 Debevec High Dynamic Range( HDR) [4] HDR Derek [5] 2. 3 [6] 3. [6] x E(x) E(x) = 2π π 2 V (x, θ i, ϕ i )L(θ](/thumbs/91/105641694.jpg "3 3 3 Knecht (2-3fps) AR [3] 2. 2 Debevec High Dynamic Range( HDR) [4] HDR Derek [5] 2. 3 [6] 3. [6] x E(x) E(x) = 2π π 2 V (x, θ i, ϕ i )L(θ")
(MIRU212) 212 8 RGB-D 223 8522 3 14 1 E-mail: {ikeda,charmie,saito}@hvrl.ics.keio.ac.jp, sugimoto@ics.keio.ac.jp RGB-D Lambert RGB-D 1. Augmented Reality AR [1] AR AR 2 [2], [3] [4], [5] [6] RGB-D RGB-D
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
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
Abstract This paper concerns with a method of dynamic image cognition. Our image cognition method has two distinguished features. One is that the imag
2004 RGB A STUDY OF RGB COLOR INFORMATION AND ITS APPLICATION 03R3237 Abstract This paper concerns with a method of dynamic image cognition. Our image cognition method has two distinguished features. One
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
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,
1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2
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
2 2. Fig.2. Schematic flow of proposed method. 2 DIC Technical Review No.2 / 26
Realtime Color Appearance Processing for Images with High Dynamic Range ISHIHARA Daisuke, ISHII Toru, TSUMURA Norimichi, NAKAGUCHI Toshiya, SHISHIKURA Masami and MIYAKE Yoichi With the recently progress
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
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
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,
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
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
PSF SN 2 DFD PSF SN PSF PSF PSF 2 2 PSF 2 PSF PSF 2 3 PSF 4 DFD PSF PSF 3) DFD Levin 4) PSF DFD KL KL PSF DFD 2 Zhou 5) 2 DFD DFD DFD DFD Zhou 2
DFD that uses focus changes during an image integration time for engineering the PSF. We can capture higher SNR input images, since we can control the PSF with wide aperture setting unlike coded aperture.
(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,
2_05.dvi
Vol. 52 No. 2 901 909 (Feb. 2011) Gradient-Domain Image Editing is a useful technique to do various-type image editing, for example, Poisson Image Editing which can do seamless image composition. This
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
Summary 3D cinemas are becoming real thanks to digital image processing technology. The most feasible and stable technology based on the binocular dis
Summary 3D cinemas are becoming real thanks to digital image processing technology. The most feasible and stable technology based on the binocular disparity requires stereoglasses. However, there is still
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.
Input image Initialize variables Loop for period of oscillation Update height map Make shade image Change property of image Output image Change time L
1,a) 1,b) 1/f β Generation Method of Animation from Pictures with Natural Flicker Abstract: Some methods to create animation automatically from one picture have been proposed. There is a method that gives
DPA,, ShareLog 3) 4) 2.2 Strino Strino STRain-based user Interface with tacticle of elastic Natural ObjectsStrino 1 Strino ) PC Log-Log (2007 6)
1 2 1 3 Experimental Evaluation of Convenient Strain Measurement Using a Magnet for Digital Public Art Junghyun Kim, 1 Makoto Iida, 2 Takeshi Naemura 1 and Hiroyuki Ota 3 We present a basic technology
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.
SICE東北支部研究集会資料（2017年）
307 (2017.2.27) 307-8 Deep Convolutional Neural Network X Detecting Masses in Mammograms Based on Transfer Learning of A Deep Convolutional Neural Network Shintaro Suzuki, Xiaoyong Zhang, Noriyasu Homma,
LED a) A New LED Array Acquisition Method Focusing on Time-Gradient and Space- Gradient Values for Road to Vehicle Visible Light Communication Syunsuk
VOL. J97-B NO. 7 JULY 2014 本 PDF の扱いは 電子情報通信学会著作権規定に従うこと なお 本 PDF は研究教育目的 ( 非営利 ) に限り 著者が第三者に直接配布することができる 著者以外からの配布は禁じられている LED a) A New LED Array Acquisition Method Focusing on Time-Gradient and Space-
(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
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
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
Fig Measurement data combination. 2 Fig. 2. Ray vector. Fig (12) 1 2 R 1 r t 1 3 p 1,i i 2 3 Fig.2 R 2 t 2 p 2,i [u, v] T (1)(2) r R 1 R 2
![Fig Measurement data combination. 2 Fig. 2. Ray vector. Fig (12) 1 2 R 1 r t 1 3 p 1,i i 2 3 Fig.2 R 2 t 2 p 2,i [u, v] T (1)(2) r R 1 R 2](/thumbs/93/113464882.jpg "Fig Measurement data combination. 2 Fig. 2. Ray vector. Fig (12) 1 2 R 1 r t 1 3 p 1,i i 2 3 Fig.2 R 2 t 2 p 2,i [u, v] T (1)(2) r R 1 R 2")
IP 06 16 / IIS 06 32 3 3-D Environment Modeling from Images Acquired with an Omni-Directional Camera Mounted on a Mobile Robot Atsushi Yamashita, Tomoaki Harada, Ryosuke Kawanishi, Toru Kaneko (Shizuoka
[2] OCR [3], [4] [5] [6] [4], [7] [8], [9] 1 [10] Fig. 1 Current arrangement and size of ruby. 2 Fig. 2 Typography combined with printing
![[2] OCR [3], [4] [5] [6] [4], [7] [8], [9] 1 [10] Fig. 1 Current arrangement and size of ruby. 2 Fig. 2 Typography combined with printing](/thumbs/93/112070099.jpg "[2] OCR [3], [4] [5] [6] [4], [7] [8], [9] 1 [10] Fig. 1 Current arrangement and size of ruby. 2 Fig. 2 Typography combined with printing")
1,a) 1,b) 1,c) 2012 11 8 2012 12 18, 2013 1 27 WEB Ruby Removal Filters Using Genetic Programming for Early-modern Japanese Printed Books Taeka Awazu 1,a) Masami Takata 1,b) Kazuki Joe 1,c) Received: November
2.2 (a) = 1, M = 9, p i 1 = p i = p i+1 = 0 (b) = 1, M = 9, p i 1 = 0, p i = 1, p i+1 = 1 1: M 2 M 2 w i [j] w i [j] = 1 j= w i w i = (w i [ ],, w i [
![2.2 (a) = 1, M = 9, p i 1 = p i = p i+1 = 0 (b) = 1, M = 9, p i 1 = 0, p i = 1, p i+1 = 1 1: M 2 M 2 w i [j] w i [j] = 1 j= w i w i = (w i [ ],, w i [](/thumbs/93/111684107.jpg "2.2 (a) = 1, M = 9, p i 1 = p i = p i+1 = 0 (b) = 1, M = 9, p i 1 = 0, p i = 1, p i+1 = 1 1: M 2 M 2 w i [j] w i [j] = 1 j= w i w i = (w i [ ],, w i [")
RI-002 Encoding-oriented video generation algorithm based on control with high temporal resolution Yukihiro BANDOH, Seishi TAKAMURA, Atsushi SHIMIZU 1 1T / CMOS [1] 4K (4096 2160 /) 900 Hz 50Hz,60Hz 240Hz
& 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
IPSJ SIG Technical Report 1, Instrument Separation in Reverberant Environments Using Crystal Microphone Arrays Nobutaka ITO, 1, 2 Yu KITANO, 1
1, 2 1 1 1 Instrument Separation in Reverberant Environments Using Crystal Microphone Arrays Nobutaka ITO, 1, 2 Yu KITANO, 1 Nobutaka ONO 1 and Shigeki SAGAYAMA 1 This paper deals with instrument separation
IPSJ SIG Technical Report Vol.2010-AVM-68 No /3/ High-Efficient 2-pass Video Coding Algorithm based on Macroblock Rate-Distortion Kazu
. High-Efficient -pass Video Coding Algorithm based on Macroblock Rate-Distortion Kazuya Yokohari, Atsushi Sagata and Kazuto Kamikura In order to achieve high-efficient video coding, -pass video coding
it-ken_open.key
深層学習技術の進展 ImageNet Classification 画像認識 音声認識 自然言語処理 機械翻訳 深層学習技術は これらの分野において 特に圧倒的な強みを見せている Figure (Left) Eight ILSVRC-2010 test Deep images and the cited4: from: ``ImageNet Classification with Networks et
(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,
鉄鋼協会プレゼン
NN :~:, 8 Nov., Adaptive H Control for Linear Slider with Friction Compensation positioning mechanism moving table stand manipulator Point to Point Control [G] Continuous Path Control ground Fig. Positoining
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
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
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
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
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
IPSJ SIG Technical Report Vol.2011-MUS-91 No /7/ , 3 1 Design and Implementation on a System for Learning Songs by Presenting Musical St
1 2 1, 3 1 Design and Implementation on a System for Learning Songs by Presenting Musical Structures based on Phrase Similarity Yuma Ito, 1 Yoshinari Takegawa, 2 Tsutomu Terada 1, 3 and Masahiko Tsukamoto
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.
sumi.indd
S/N S/N CCDCMOS CCD CMOS & E-mail hirofumi.sumi@jp.sony.com & E-mail Tadakuni.Narabu@jp.sony.com & E-mail Shinichiro.Saito@jp.sony.com Hirofumi SUMI, Non - Member and Tadakuni NARABU, Member and Shinichiro
撮 影
DC cathode ray tube, 2.2 log log log + log log / / / A method determining tone conversion characteristics of digital still camera from two pictorial images Tone conversion characteristic Luminance
IPSJ SIG Technical Report Vol.2010-CVIM-171 No /3/18 Scene Point M Aperture 1 u v m m b Depth from defocus (PSF) Coded Imaging Hajime Nagahara
Scene Point M Aperture 1 u v m m b Depth from defocus (PSF) Coded Imaging Hajime Nagahara 1 Recently, a coded imaging is getting popular as one of computational photography research area. The coded imaging
a) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a
a) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a), Tetsuo SAWARAGI, and Yukio HORIGUCHI 1. Johansson
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
08_研究速報_3校_1002.indd
196 MEDICAL IMAGING TECHNOLOGY Vol. 36 No. 4 September 2018 MRI 1 2 2 1 3 4 1 MRI MRI ScSR MRI T1 T2 FLAIR TOF ScSR PSNR SSIM T1 T1 T2 FLAIR ScSR PSNR SSIM Bilinear Bicubic Lanczos p
Fig. 2 Signal plane divided into cell of DWT Fig. 1 Schematic diagram for the monitoring system
Study of Health Monitoring of Vehicle Structure by Using Feature Extraction based on Discrete Wavelet Transform Akihisa TABATA *4, Yoshio AOKI, Kazutaka ANDO and Masataka KATO Department of Precision Machinery
日本感性工学会論文誌
pp.343-351 2013 Changes in Three Attributes of Color by Reproduction of Memorized Colors Hiroaki MIYAKE, Takeshi KINOSHITA and Atsushi OSA Graduate School of Science and Engineering, Yamaguchi University,
IPSJ SIG Technical Report Vol.2014-HCI-158 No /5/22 1,a) 2 2 3,b) Development of visualization technique expressing rainfall changing conditions
1,a) 2 2 3,b) Development of visualization technique expressing rainfall changing conditions with a still picture Yuuki Hyougo 1,a) Hiroko Suzuki 2 Tadanobu Furukawa 2 Kazuo Misue 3,b) Abstract: In order
スライド 1
CMOS : swk(at)ic.is.tohoku.ac.jp [ 2003] [Wong1999] 2 : CCD CMOS 3 : CCD Q Q V 4 : CMOS V C 5 6 CMOS light input photon shot noise α quantum efficiency dark current dark current shot noise dt time integration
,4) 1 P% P%P=2.5 5%!%! (1) = (2) l l Figure 1 A compilation flow of the proposing sampling based architecture simulation
1 1 1 1 SPEC CPU 2000 EQUAKE 1.6 50 500 A Parallelizing Compiler Cooperative Multicore Architecture Simulator with Changeover Mechanism of Simulation Modes GAKUHO TAGUCHI 1 YOUICHI ABE 1 KEIJI KIMURA 1
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
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
Wavelet HSI / [1] JPEG2000 9/7Wavelet [2][6] 2:1 9/7Wavelet Wavelet 80 Wavelet i
![Wavelet HSI / [1] JPEG2000 9/7Wavelet [2][6] 2:1 9/7Wavelet Wavelet 80 Wavelet i](/thumbs/91/105321936.jpg "Wavelet HSI / [1] JPEG2000 9/7Wavelet [2][6] 2:1 9/7Wavelet Wavelet 80 Wavelet i")
17 Wavelet Image Enhancement by Wavelet Transform 1060326 2006 3 10 Wavelet HSI / [1] JPEG2000 9/7Wavelet [2][6] 2:1 9/7Wavelet Wavelet 80 Wavelet i Abstract Image Enhancement by Wavelet Transform Yuichi
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
,.,. NP,., ,.,,.,.,,, (PCA)...,,. Tipping and Bishop (1999) PCA. (PPCA)., (Ilin and Raiko, 2010). PPCA EM., , tatsukaw
,.,. NP,.,. 1 1.1.,.,,.,.,,,. 2. 1.1.1 (PCA)...,,. Tipping and Bishop (1999) PCA. (PPCA)., (Ilin and Raiko, 2010). PPCA EM., 152-8552 2-12-1, tatsukawa.m.aa@m.titech.ac.jp, 190-8562 10-3, mirai@ism.ac.jp
とができ 現在までにいくつかの手法が提案されている 去性能を向上させる 以下の最適化問題を解き 多重露 提案手法では 多項式により逆カメラレスポンスカーブ 光画像統合における重み wk を求める を近似する Mitsunaga 等の手法 9] を用いる 逆カメラ min kp w hk + α kd
![とができ 現在までにいくつかの手法が提案されている 去性能を向上させる 以下の最適化問題を解き 多重露 提案手法では 多項式により逆カメラレスポンスカーブ 光画像統合における重み wk を求める を近似する Mitsunaga 等の手法 9] を用いる 逆カメラ min kp w hk + α kd](/thumbs/85/93027378.jpg "とができ 現在までにいくつかの手法が提案されている 去性能を向上させる 以下の最適化問題を解き 多重露 提案手法では 多項式により逆カメラレスポンスカーブ 光画像統合における重み wk を求める を近似する Mitsunaga 等の手法 9] を用いる 逆カメラ min kp w hk + α kd")
第8回信号処理シンポジウム 3年月9日 日 下関 多重露光画像を用いたノイズ除去のための重み最適化 Weight Optimization for Multi-exposure Noise Reduction 山内智弘 松岡諒 馬場達也 北九州市立大学大学院国際環境工学研究科 奥田正浩 omohiro Yamauchi Ryo Matsuoka atsuya Baba Masahiro Okuda
IPSJ SIG Technical Report Vol.2012-CG-148 No /8/29 3DCG 1,a) On rigid body animation taking into account the 3D computer graphics came
3DCG 1,a) 2 2 2 2 3 On rigid body animation taking into account the 3D computer graphics camera viewpoint Abstract: In using computer graphics for making games or motion pictures, physics simulation is
GPGPU
GPGPU 2013 1008 2015 1 23 Abstract In recent years, with the advance of microscope technology, the alive cells have been able to observe. On the other hand, from the standpoint of image processing, the
[2] 2. [3 5] 3D [6 8] Morishima [9] N n 24 24FPS k k = 1, 2,..., N i i = 1, 2,..., n Algorithm 1 N io user-specified number of inbetween omis
![[2] 2. [3 5] 3D [6 8] Morishima [9] N n 24 24FPS k k = 1, 2,..., N i i = 1, 2,..., n Algorithm 1 N io user-specified number of inbetween omis](/thumbs/76/74386791.jpg "[2] 2. [3 5] 3D [6 8] Morishima [9] N n 24 24FPS k k = 1, 2,..., N i i = 1, 2,..., n Algorithm 1 N io user-specified number of inbetween omis")
1,a) 2 2 2 1 2 3 24 Motion Frame Omission for Cartoon-like Effects Abstract: Limited animation is a hand-drawn animation style that holds each drawing for two or three successive frames to make up 24 frames
IPSJ SIG Technical Report Vol.2014-CG-155 No /6/28 1,a) 1,2,3 1 3,4 CG An Interpolation Method of Different Flow Fields using Polar Inter
,a),2,3 3,4 CG 2 2 2 An Interpolation Method of Different Flow Fields using Polar Interpolation Syuhei Sato,a) Yoshinori Dobashi,2,3 Tsuyoshi Yamamoto Tomoyuki Nishita 3,4 Abstract: Recently, realistic
IPSJ SIG Technical Report Vol.2014-DPS-158 No.27 Vol.2014-CSEC-64 No /3/6 1,a) 2,b) 3,c) 1,d) 3 Cappelli Bazen Cappelli Bazen Cappelli 1.,,.,.,
1,a),b) 3,c) 1,d) 3 Cappelli Bazen Cappelli Bazen Cappelli 1.,,,,,.,,,,.,,.,,,,.,, 1 Department of Electrical Electronic and Communication Engineering Faculty of Science and Engineering Chuo University
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,
2 ( ) i
25 Study on Rating System in Multi-player Games with Imperfect Information 1165069 2014 2 28 2 ( ) i ii Abstract Study on Rating System in Multi-player Games with Imperfect Information Shigehiko MORITA
Fig. 1. Example of characters superimposed on delivery slip.
Extraction of Handwritten Character String Superimposed on Delivery Slip Data Ken-ichi MATSUO, Non-member, Katsuhiko UEDA, Non-member (Nara National College of Technology), Michio UMEDA, Member (Osaka
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.2012-CVIM-182 No /5/23 1,a) Study on Automatic Parameter Control for Local Adaptive Tone Mapping Naoto Kato
1,a) 1 1 2 Study on Automatic Parameter Control for Local Adaptive Tone Mapping Naoto Kato 1,a) Masayuki Ikebe 1 Junichi Motohisa 1 Sousuke Shimoyama 2 Abstract: In this paper, we propose an automatic
2007/8 Vol. J90 D No RGB Grossberg [5] Grossberg [5] Fujii [4] Majumder [8] Wang [12] Majumder Wang [12] Perceptually-based Threshold Map
![2007/8 Vol. J90 D No RGB Grossberg [5] Grossberg [5] Fujii [4] Majumder [8] Wang [12] Majumder Wang [12] Perceptually-based Threshold Map](/thumbs/93/112136905.jpg "2007/8 Vol. J90 D No RGB Grossberg [5] Grossberg [5] Fujii [4] Majumder [8] Wang [12] Majumder Wang [12] Perceptually-based Threshold Map")
Perceptual Photometric Compensation for Projected Images Mark ASHDOWN,ImariSATO, Takahiro OKABE, and Yoichi SATO 1. [1], [9] Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA
IPSJ SIG Technical Report Vol.2017-MUS-116 No /8/24 MachineDancing: 1,a) 1,b) 3 MachineDancing MachineDancing MachineDancing 1 MachineDan
MachineDancing: 1,a) 1,b) 3 MachineDancing 2 1. 3 MachineDancing MachineDancing 1 MachineDancing MachineDancing [1] 1 305 0058 1-1-1 a) s.fukayama@aist.go.jp b) m.goto@aist.go.jp 1 MachineDancing 3 CG
A Feasibility Study of Direct-Mapping-Type Parallel Processing Method to Solve Linear Equations in Load Flow Calculations Hiroaki Inayoshi, Non-member
A Feasibility Study of Direct-Mapping-Type Parallel Processing Method to Solve Linear Equations in Load Flow Calculations Hiroaki Inayoshi, Non-member (University of Tsukuba), Yasuharu Ohsawa, Member (Kobe
318 T. SICE Vol.52 No.6 June 2016 (a) (b) (c) (a) (c) ) 11) (1) (2) 1 5) 6) 7), 8) 5) 20 11) (1
Vol.52, No.6, 317/329 2016 Mathematical Model and Analysis on Human s Handwork and Their Application to Brush Motion and Junkatsu of Japanese Calligraphy Tadamichi Mawatari and Kensuke Tsuchiya There is
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
3. ( 1 ) Linear Congruential Generator:LCG 6) (Mersenne Twister:MT ), L 1 ( 2 ) 4 4 G (i,j) < G > < G 2 > < G > 2 g (ij) i= L j= N
RMT 1 1 1 N L Q=L/N (RMT), RMT,,,., Box-Muller, 3.,. Testing Randomness by Means of RMT Formula Xin Yang, 1 Ryota Itoi 1 and Mieko Tanaka-Yamawaki 1 Random matrix theory derives, at the limit of both dimension
Fig. 3 Flow diagram of image processing. Black rectangle in the photo indicates the processing area (128 x 32 pixels).
Fig. 1 The scheme of glottal area as a function of time Fig. 3 Flow diagram of image processing. Black rectangle in the photo indicates the processing area (128 x 32 pixels). Fig, 4 Parametric representation
1611 原著 論文受付 2009 年 6 月 2 日 論文受理 2009 年 9 月 18 日 Code No. 733 ピクセル開口率の向上による医用画像表示用カラー液晶モニタの物理特性の変化 澤田道人 石川晃則 1) 松永沙代子 1) 1) 石川陽子 有限会社ムツダ商会 1) 安城更生病院放射
1611 原著 論文受付 2009 年 6 月 2 日 論文受理 2009 年 9 月 18 日 Code No. 733 ピクセル開口率の向上による医用画像表示用カラー液晶モニタの物理特性の変化 澤田道人 石川晃則 1) 松永沙代子 1) 1) 石川陽子 有限会社ムツダ商会 1) 安城更生病院放射線技術科 緒言 3D PET/CT Fusion 1 liquid crystal display:
IEEE HDD RAID MPI MPU/CPU GPGPU GPU cm I m cm /g I I n/ cm 2 s X n/ cm s cm g/cm
Neutron Visual Sensing Techniques Making Good Use of Computer Science J-PARC CT CT-PET TB IEEE HDD RAID MPI MPU/CPU GPGPU GPU cm I m cm /g I I n/ cm 2 s X n/ cm s cm g/cm cm cm barn cm thn/ cm s n/ cm
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. TV A310
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. TV 367 0035 1011 A310 E-mail kawamura@suou.waseda.jp Total Variation Total Variation Total Variation Abstract
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
E-1E-9 1 5) 15) ) 1 E-1 E-5 E-1E-5 11) 14 (E-2)(E-3) E-1E-5 Exif E-6E-9 E-6 2.1, 2.2 E E-1 E-5 E-6E-9 1-1) 1 Exif 11) (E-1) (E-2E-5) E-7
77 674647-6562012 J. Struct. Constr. Eng., AIJ, Vol. 77 No. 674, 647-656, Apr., 2012 Satoshi FUJIMOTO Construction photo-editing has prevailed among the Japanese construction industries as digitalization
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
3D VR CAD 3D CAD CAD [1] CAD 3DCG [2] [3] CAD 3D NC CG [4] Ccurve XY C curve α C curve [5], [6], [7], [8], [9] 2 [10] 1 [11], [12] 2.2 [13] Tcu
![3D VR CAD 3D CAD CAD [1] CAD 3DCG [2] [3] CAD 3D NC CG [4] Ccurve XY C curve α C curve [5], [6], [7], [8], [9] 2 [10] 1 [11], [12] 2.2 [13] Tcu](/thumbs/92/109954609.jpg "3D VR CAD 3D CAD CAD [1] CAD 3DCG [2] [3] CAD 3D NC CG [4] Ccurve XY C curve α C curve [5], [6], [7], [8], [9] 2 [10] 1 [11], [12] 2.2 [13] Tcu")
1,a) 2 2 2011 10 21, 2012 5 12 A Study of Simulating Log-aesthetic Curved Surfaces under Various Light Source Environments with Augmented Reality Ryo Hirano 1,a) Toshinobu Harada 2 Kohe Tokoi 2 Received:
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
IPSJ-CVIM
1 1 2 1 Estimation of Shielding Object Distribution in Scattering Media by Analyzing Light Transport Shosei Moriguchi, 1 Yasuhiro Mukaigawa, 1 Yasuyuki Matsushita 2 and Yasushi Yagi 1 In this paper, we
,,.,.,,.,.,.,.,,.,..,,,, 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
main.dvi
A 1/4 1 1/ 1/1 1 9 6 (Vergence) (Convergence) (Divergence) ( ) ( ) 97 1) S. Fukushima, M. Takahashi, and H. Yoshikawa: A STUDY ON VR-BASED MUTUAL ADAPTIVE CAI SYSTEM FOR NUCLEAR POWER PLANT, Proc. of FIFTH
,,, 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 ( ), $[2, 4]$, $[21, 25]$,
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)
パナソニック技報
Panasonic Technical Journal Vol. 63 No. 1 May 2017 Development of Simultaneous-Capture Wide-dynamic-range Technology and Global Shutter Technology for Organic Photoconductive Film Image Sensor Masashi