4 4 2 RAW (PCA) 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 ( )
|
|
|
- あゆみ こいたばし
- 9 years ago
- Views:
Transcription
1 RAW 4 [email protected] 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 RAW [6, 7, 12] 3 [8, 9, 10, 11] RAW RAW RAW 4 G 2 4
![CFA [1] RAW CFA 1 2 [2, 3, 4, 5].](/docs-images/45/20574692/images/page_1.jpg "RAW RAW RAW RAW 3 [2, 3, 4, 5] (AWGN) [13, 14] RAW 2 RAW RAW")
2 4 4 2 RAW (PCA) 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 ( ) D m ( ) ˆx = D n (D m (Mx + n)). (2). η = D m (Mx + n) x. (3) ˆx = D m (D n (Mx + n)). (4) RAW RAW RAW RAW RAW 1 4 RAW 4 G R B GRBG RGGB BGGR GBRG 1 RAW
3 RAW RAW 4 4 RAW G 1, R, B, G 2 4 RGB Park RGB RGB 4 4 [12] RAW [7] 4 [ ] X = x 1 + n 1 x 2 + n 2 x m + n m, (5) Σ = 1 m 1 ( X µ 1 T m)( X µ 1 T m) T, (6) µ = 1 m m x i. (7) i=1 x i i 4 n i i m 4 1 m 1 m T Σ = Σ diag ( [ σ 2 Y 1 σ 2 Y 2 σ 2 Y 3 σ 2 Y 4 ] T ). (8) diag(z i ) z i σ 2 k 4 k GRBG
![3 Y 1 G 1 Y 2 Y 3 = P R B, (9) Y 4 G 2 P 11 P 12 P 13 P 14 P = P 21 P 22 P 23 P 24 P 31 P 32 P 33 P 34. (10) P 41 P 42 P 43 P 44 P Σ v k. [ ] T P = v 1 v 2 v 3 v 4. (11) P 4.](/docs-images/40/20574692/images/4-0.png "k σ 2 Y k = P 2 1kσ 2 G + P 2 2kσ 2 R + P 2 3kσ 2 B + P 2 4kσ 2 G. (12) 4. 3.3 4 4 RAW 4 RAW 4 4 1 (a) (b) (34.49dB). (35.28dB). 4 (σ = 20).")
4 3 Y 1 G 1 Y 2 Y 3 = P R B, (9) Y 4 G 2 P 11 P 12 P 13 P 14 P = P 21 P 22 P 23 P 24 P 31 P 32 P 33 P 34. (10) P 41 P 42 P 43 P 44 P Σ v k. [ ] T P = v 1 v 2 v 3 v 4. (11) P 4. k σ 2 Y k = P 2 1kσ 2 G + P 2 2kσ 2 R + P 2 3kσ 2 B + P 2 4kσ 2 G. (12) RAW 4 RAW (a) (b) (34.49dB). (35.28dB). 4 (σ = 20). RAW GRBG RGGB BGGR GBRG RAW 4 4 RAW RAW RAW RAW (PSNR)
5 1 24 RAW PSNR[dB] ( ) σ [14] to CFA [7] [12] proposed CPSNR [db] Proposed [12] [10] [11] [14]toCFA [9] [7] Noise level PSNR [db] Proposed [12] [14]toCFA [7] Noise sigma 5 PSNR. RAW RAW RAW CPSNR Kodak high resolution image dataset RAW RAW 0 RAW [14] [5] 4.1 RAW RAW BM3D [14] RAW PCASAD [7] Park [12] BM3D [14] RAW 6 CPSNR. RAW BM3D Park BM3D 1 5 Kodak dataset 24 PSNR PSNR 4.2 RAW CFA [5] LPAICI [9], JDDTV [10], LSLCD [11] RAW CPSNR CPSNR BM3D [14] RAW CPSNR Park [12] 0.5dB 7 Park BM3D
![97 CPSNR [db] 40 38 36 34 32 30 Proposed [12] [10] [11] [14]toCFA [9] [7] 28 5 10 15 20 25 30 35 40 Noise level PSNR [db] 50 45 40 35 30 Proposed [12] [14]toCFA [7] 25 0 10 20 30 40](/docs-images/45/20574692/images/page_5.jpg "Noise sigma 5 PSNR. RAW RAW RAW CPSNR Kodak high resolution image dataset 24 2048 3072 RAW RAW 0 RAW [14] [5] 4.1 RAW RAW BM3D [14] RAW PCASAD [7] Park [12] BM3D [14] RAW 6 CPSNR.")
![2 24 CPSNR[dB] ( ) Type Dm Dm Dn Joint Dn and Dm Dn Dm [5] σ [5] [5] + [14] [9] [10] [11] [14]toCFA [7] [12] proposed 5 34.84 38.76 38.48 38.99 39.01 39.48 39.13 39.07 39.52 10 29.35 35.38 35.81 36.](/docs-images/40/20574692/images/6-0.png "80 36.67 36.97 36.38 36.78 37.32 15 26.01 32.92 34.16 35.40 35.26 35.17 34.49 35.41 35.99 20 23.63 30.99 32.93 34.35 34.12 33.68 33.01 34.46 34.98 30 20.32 28.10 31.01 32.62 N/A 31.33 30.74 32.81 33.")
6 2 24 CPSNR[dB] ( ) Type Dm Dm Dn Joint Dn and Dm Dn Dm [5] σ [5] [5] + [14] [9] [10] [11] [14]toCFA [7] [12] proposed N/A N/A [11] σ = 20. (a) ground truth (b) [5] (c) [5]+[14] (d) [9] (e) [10] (f) [11] (g) [14]toCFA (h) [7] (i) [12] (j) proposed 7 Kodak high resolution image dataset (σ = 20). Park [12] 5 RAW RAW 4 RAW [1] B. Bayer, Color imaging array, U.S. Patent , [2] L. Zhang and X. Wu, Color demosaicking via directional linear minimum mean square-error estimation, IEEE Trans. on Image Processing, vol. 14, no. 12, pp , [3] X. Li, B. Gunturk, and L. Zhang, Image Demosaicing: A Systematic Survey, Proc. SPIE Electronic Imaging, vol. 6822, pp , [4] L. Zhang, X. Wu, A. Buades, and X. Li, Color demosaicking by local directional interpolation and nonlocal adaptive thresholding, Journal of Electronic Imaging, vol. 20, no. 2, pp , [5] D. Kiku, Y. Monno, M. Tanaka, and M. Okutomi, Residual Interpolation for Color Image Demosaicking, Proc. of IEEE Int. Conf. on Image Processing (ICIP), pp , [6] A. Danielyan, M. Vehvilainen, A. Foi, V. Katkovnik, and K. Egiazarian Cross-color BM3D filtering of noisy raw data, in Proc. International Workshop on Local and Non-Local Approximation in Image Processing LNLA, pp.
![35 40 18.04 26.00 29.47 31.12 N/A 29.46 29.00 31.41 31.89 [11] σ = 20.](/docs-images/45/20574692/images/page_6.jpg "(a) ground truth (b) [5] (c) [5]+[14] (d) [9] (e) [10] (f) [11] (g) [14]toCFA (h) [7] (i) [12] (j) proposed 7 Kodak high resolution image dataset (σ = 20). Park [12] 5 RAW RAW 4 RAW [1] B.")
7 , [7] L. Zhang, R. Lukac, X. Wu, and D. Zhang, PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras, IEEE Trans. on Image Processing, vol. 18, no. 4, pp , [8] K. Hirakawa, and T.W. Parks, Joint demosaicing and denoising Image Processing, IEEE Trans. on Image Processing, vol. 15, no. 8, pp , [9] D. Paliy, V. Katkovnik, R. Bilcu, S. Alenius, and K. Egiazarian, Spatially Adaptive Color Filter Array Interpolation for Noiseless and Noisy Data, Int. J. Imaging Sys. Tech., Sp. Iss. Appl. Color Image Process., vol. 17, no. 3, pp , [10] L. Condat, and S. Mosaddegh, Joint demosaicking and denoising by total variation minimization, Proc. of IEEE Int. Conf. on Image Processing (ICIP), pp , [11] E. Dubois, and G. Jeon, Demosaicking of Noisy Bayer-Sampled Color Images With Least-Squares Luma-Chroma Demultiplexing and Noise Level Estimation, IEEE Trans. on Image Processing, vol. 22, no. 1, pp , [12] S. H. Park, H. S. Kim, S. Lansel, M. Parmar, and B. A. Wandell, A case for denoising before demosaicking color filter array data Asilomar Conf. on Signals, Systems, and Computers, pp , [13] J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, Non-local sparse models for image restoration. IEEE Int. Conf. on Computer Vision (ICCV), pp , [14] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, Image denoising with block-matching and 3D filtering, Proc. SPIE Electronic Imaging, no. 6064A 30, 2006.
IPSJ SIG Technical Report Vol.2012-CVIM-182 No /5/ RGB [1], [2], [3], [4], [5] [6], [7], [8], [9] 1 (MSFA: Multi-Spectrum Filt
![IPSJ SIG Technical Report Vol.2012-CVIM-182 No /5/ RGB [1], [2], [3], [4], [5] [6], [7], [8], [9] 1 (MSFA: Multi-Spectrum Filt](/thumbs/104/162109798.jpg "IPSJ SIG Technical Report Vol.2012-CVIM-182 No /5/ RGB [1], [2], [3], [4], [5] [6], [7], [8], [9] 1 (MSFA: Multi-Spectrum Filt")
1 1 1 1 1. 4 3 RGB [1], [2], [3], [4], [5] [6], [7], [8], [9] 1 (MSFA: Multi-Spectrum Filter Array) 1 [8], [9] RGB 1 Tokyo Institute of Technology 1 [10], [11], [12], [13], [14] [15] Parmar Wiener RGB
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)]
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
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,
(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
技術の系統化調査報告「プロセス制御システムの技術系統化調査」
A Technical Survey of Process Control Systems 3 Yutaka Wakasa Abstract Yutaka Wakasa 95 1 2 96 Vol.11 2008.March 2.1 2.2 97 98 Vol.11 2008.March 99 3 3.1 3.2 3.3 100 Vol.11 2008.March 101 3.4 102 Vol.11
25 11M15133 0.40 0.44 n O(n 2 ) O(n) 0.33 0.52 O(n) 0.36 0.52 O(n) 2 0.48 0.52
26 1 11M15133 25 11M15133 0.40 0.44 n O(n 2 ) O(n) 0.33 0.52 O(n) 0.36 0.52 O(n) 2 0.48 0.52 1 2 2 4 2.1.............................. 4 2.2.................................. 5 2.2.1...........................
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
す 局所領域 ωk において 線形変換に用いる係数 (ak 画素の係数 (ak bk ) を算出し 入力画像の信号成分を bk ) は次式のコスト関数 E を最小化するように最適化 有さない画素に対して 式 (2) より画素値を算出する される これにより 低解像度な画像から補間によるアップサ E(
IR E-mail: [email protected] Abstract IR RGB ( ) IR IR IR RGB RGB PSNR 1 Time-Of- Flight(TOF)[1] Kinect [2] TOF LED TOF [3] [6] [4][5] 2 [6] RGB ( ) Infrared(IR) IR 2 2.1 1 す 局所領域 ωk において 線形変換に用いる係数 (ak
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
untitled
Application of image correlation technique to determination of in-plane deformation distribution of paper Toshiharu Enomae Graduate School of Agricultural and Life Sciences The University of Tokyo 1 Peters
(4) ω t(x) = 1 ω min Ω ( (I C (y))) min 0 < ω < C A C = 1 (5) ω (5) t transmission map tmap 1 4(a) 2. 3 2. 2 t 4(a) t tmap RGB 2 (a) RGB (A), (B), (C)
(MIRU2011) 2011 7 890 0065 1 21 40 105-6691 1 1 1 731 3194 3 4 1 338 8570 255 346 8524 1836 1 E-mail: {fukumoto,kawasaki}@ibe.kagoshima-u.ac.jp, [email protected], [email protected],
pp d 2 * Hz Hz 3 10 db Wind-induced noise, Noise reduction, Microphone array, Beamforming 1
72 12 2016 pp. 739 748 739 43.60.+d 2 * 1 2 2 3 2 125 Hz 0.3 0.8 2 125 Hz 3 10 db Wind-induced noise, Noise reduction, Microphone array, Beamforming 1. 1.1 PSS [1] [2 4] 2 Wind-induced noise reduction
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.
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
2. CABAC CABAC CABAC 1 1 CABAC Figure 1 Overview of CABAC 2 DCT 2 0/ /1 CABAC [3] 3. 2 値化部 コンテキスト計算部 2 値算術符号化部 CABAC CABAC
![2. CABAC CABAC CABAC 1 1 CABAC Figure 1 Overview of CABAC 2 DCT 2 0/ /1 CABAC [3] 3. 2 値化部 コンテキスト計算部 2 値算術符号化部 CABAC CABAC](/thumbs/92/108208872.jpg "2. CABAC CABAC CABAC 1 1 CABAC Figure 1 Overview of CABAC 2 DCT 2 0/ /1 CABAC [3] 3. 2 値化部 コンテキスト計算部 2 値算術符号化部 CABAC CABAC")
H.264 CABAC 1 1 1 1 1 2, CABAC(Context-based Adaptive Binary Arithmetic Coding) H.264, CABAC, A Parallelization Technology of H.264 CABAC For Real Time Encoder of Moving Picture YUSUKE YATABE 1 HIRONORI
(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
(MIRU2008) HOG Histograms of Oriented Gradients (HOG)
(MIRU2008) 2008 7 HOG - - E-mail: [email protected], {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 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) [email protected]
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:
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
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
2 DS SS (SS+DS) Fig. 2 Separation algorithm for motorcycle sound by combining DS and SS (SS+DS). 3. [3] DS SS 2 SS+DS 1 1 B SS SS 4. NMF 4. 1 (NMF) Y
![2 DS SS (SS+DS) Fig. 2 Separation algorithm for motorcycle sound by combining DS and SS (SS+DS). 3. [3] DS SS 2 SS+DS 1 1 B SS SS 4. NMF 4. 1 (NMF) Y](/thumbs/49/25425602.jpg "2 DS SS (SS+DS) Fig. 2 Separation algorithm for motorcycle sound by combining DS and SS (SS+DS). 3. [3] DS SS 2 SS+DS 1 1 B SS SS 4. NMF 4. 1 (NMF) Y")
a) Separation of Motorcycle Sound by Near Field Microphone Array and Nonnegative Matrix Factorization Chisaki YOSHINAGA, Nonmember, Yosuke TATEKURA a), Member, Kazuaki HAMADA, and Tetsuya KIMURA, Nonmembers
2004
2008 3 20 400 1 1,222 7 1 2 3 55.8 54.8 3 35.8 6 64.0 50.5 93.5 1 1,222 1 1,428 1 1,077 6 64.0 52.5 80.5 56.6 81.5 30.2 1 2 3 7 70.5 1 65.6 2 61.3 3 51.1 1 54.0 2 49.8 3 32.0 68.8 37.0 34.3 2008 3 2 93.5
1 Web [2] Web [3] [4] [5], [6] [7] [8] S.W. [9] 3. MeetingShelf Web MeetingShelf MeetingShelf (1) (2) (3) (4) (5) Web MeetingShelf
![1 Web [2] Web [3] [4] [5], [6] [7] [8] S.W. [9] 3. MeetingShelf Web MeetingShelf MeetingShelf (1) (2) (3) (4) (5) Web MeetingShelf](/thumbs/79/80104027.jpg "1 Web [2] Web [3] [4] [5], [6] [7] [8] S.W. [9] 3. MeetingShelf Web MeetingShelf MeetingShelf (1) (2) (3) (4) (5) Web MeetingShelf")
1,a) 2,b) 4,c) 3,d) 4,e) Web A Review Supporting System for Whiteboard Logging Movies Based on Notes Timeline Taniguchi Yoshihide 1,a) Horiguchi Satoshi 2,b) Inoue Akifumi 4,c) Igaki Hiroshi 3,d) Hoshi
2005 1
2005 1 1 1 2 2 2.1....................................... 2 2.2................................... 5 2.3 VSWR................................. 6 2.4 VSWR 2............................ 7 2.5.......................................
Micro-D 小型高密度角型コネクタ
Micro- 1 2 0.64 1.27 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 1.09 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 3 4 J J
A Study of Adaptive Array Implimentation for mobile comunication in cellular system GD133
A Study of Adaptive Array Implimentation for mobile comunication in cellular system 15 1 31 01GD133 LSI DSP CMA 10km/s i 1 1 2 LS-CMA 5 2.1 CMA... 5 2.1.1... 5 2.1.2... 7 2.1.3... 10 2.2 LS-CMA... 13 2.2.1...
Microsoft PowerPoint - 201409_秀英体の取組み素材（予稿集）.ppt
1 2 3 4 5 6 7 8 9 10 11 No Image No Image 12 13 14 15 16 17 18 19 20 21 22 23 No Image No Image No Image No Image 24 No Image No Image No Image No Image 25 No Image No Image No Image No Image 26 27 28
10_08.dvi
476 67 10 2011 pp. 476 481 * 43.72.+q 1. MOS Mean Opinion Score ITU-T P.835 [1] [2] [3] Subjective and objective quality evaluation of noisereduced speech. Takeshi Yamada, Shoji Makino and Nobuhiko Kitawaki
パナソニック技報
Liquid Crystal Display Technology for Realizing Contrast Ratio of 1 million to 1 Katsuhiro Kikuchi LCDLiquid Crystal Display IPSIn-Plane Switching-LCD 2100:1 IPS-LCDIPS-LCD50 20:1 Realization of Liquid
sumi.indd
S/N S/N CCDCMOS CCD CMOS & E-mail [email protected] & E-mail [email protected] & E-mail [email protected] Hirofumi SUMI, Non - Member and Tadakuni NARABU, Member and Shinichiro
００４１３９ 医用画像‐２７‐３／★追悼文‐２７‐３‐０ 松本様
12 13 1 vii 2 x 3 xii 4 xiv 5 xvii 6 xx 7 xxii 8 xxvii 9 xxix 10 xxxi 11 xxxii vi X 1950 69 X 1964 RII RII 2 [1, 2] [3] [4] X 1953 P.Elias OTF [5] OTF X 1962 ICO OTF RII X I-m M-n m n X X RII 1 1964 3
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
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
,,.,.,,.,.,.,.,,.,..,,,, 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
BDH Cao BDH BDH Cao Cao Cao BDH ()*$ +,-+.)*$!%&'$!"#$ 2. 1 Weng [4] Metric Learning Weng DB DB Yang [5] John [6] Sparse Coding sparse coding DB [7] K
![BDH Cao BDH BDH Cao Cao Cao BDH ()*$ +,-+.)*$!%&'$!#$ 2. 1 Weng [4] Metric Learning Weng DB DB Yang [5] John [6] Sparse Coding sparse coding DB [7] K](/thumbs/93/114210200.jpg "BDH Cao BDH BDH Cao Cao Cao BDH ()*$ +,-+.)*$!%&'$!#$ 2. 1 Weng [4] Metric Learning Weng DB DB Yang [5] John [6] Sparse Coding sparse coding DB [7] K")
Bucket Distance Hashing Metric Learning 1,a) 1,b) 1,c) 1,d) (DB) [1] DB Cao [2] Cao Metric Learning Cao Cao Cao Cao Cao 100 DB 10% 1. m DB DB DB 1 599 8531 1 1 Graduate School of Engineering, Osaka Prefecture
平成 28 年 6 月 3 日 報道機関各位 東京工業大学広報センター長 岡田 清 カラー画像と近赤外線画像を同時に撮影可能なイメージングシステムを開発 - 次世代画像センシングに向けオリンパスと共同開発 - 要点 可視光と近赤外光を同時に撮像可能な撮像素子の開発 撮像データをリアルタイムで処理する
平成 28 年 6 月 3 日 報道機関各位 東京工業大学広報センター長 岡田 清 カラー画像と近赤外線画像を同時に撮影可能なイメージングシステムを開発 - 次世代画像センシングに向けオリンパスと共同開発 - 要点 可視光と近赤外光を同時に撮像可能な撮像素子の開発 撮像データをリアルタイムで処理する画像処理システムの開発 カラー画像と近赤外線画像を同時に撮影可能なプロトタイプシステムの開発 概要 国立大学法人東京工業大学工学院システム制御系の奥富正敏教授らと