CVIM2010Nov.pdf
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16 Output Input 16
17 <. =. >. Y. Z. [. U. X5G \. HX]
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24 I = A( I + N s + N DC + N ) R I( ( N S I A( N S ( N DC ( N R ( I = f A( I + N s + N DC + N ) R ( ) + N Q Glenn E. Healey and Raghava Kondepudy, Radiometric CCD Camera Calibration and Noise Estimation, PAMI, Vol. 16, No. 3, pp ,
25 I = A( I + N s + N DC + N ) R I = AtP + AtE( N ) DC " 2 = A 2 tp + A 2 2 t" DC + A 2 " R 2 " 2 = s I + t 76 25
26 ^7&/*A6"fG6%g7/hfH#I3%//"' 26
27 HAW HAW 27
28 HAW " 2 = s I + t I R I B " p I % R $ ' = p ( I # & )p " K % ( $ # I ' di, K = I R & I B f(x) = 1/x I B 28
29 _IIGd _IJKLd IIG IJKL 29
30 <. =. >. Y. Z. [. U. X5G \.
31 " 2 I ( i) = $ 2 w j " O ( ) j#n i ( j) # # # # " " " # # " # # " # # "##$%&'( " # "##$%&'* Jun Takamatsu, Yasuyuki Matsushita, Tsukasa Ogasawara and Katsushi Ikeuchi, Estimating demosaicing algorithms using image noise variance, CVPR,
32 O = f ( I) ( ) = p( I) f '( I) p O " 2 O # f '( I) ( ) 2 " I 2 $ p( I I )di " 2 O = ( I # µ ) 2 O Jun Takamatsu, Yasuyuki Matsushita and Katsushi Ikeuchi, Estimating radiometric response functions from image noise variance, ECCV,
33 X5G [a i,b i ) "i # Z " O 2 = " I 2 + q2 12 q 1 " 2 Q = * x 2 q dx = 1 2 q ) q 2 # % $ x 3 3 & ( ' q 2 ) q 2 = q
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38 ^7&/*A6"fG6%g7/hfH#I3%//"' <RR 38
39 ( ) = A( K p ( ) + N ( s p) + N ( DC p) + N ) R + N Q I p ( ) I p " N 2 p ( ( )) +" 2 C ( ) # A 2 I + E N ( DC p) " N 2 p ( ( ( ))) µ = A I + E N DC p ( ) " C 2 kx " C 2 = A 2 " R 2 + q2 12 Glenn E. Healey and Raghava Kondepudy, Radiometric CCD Camera Calibration and Noise Estimation, PAMI, Vol. 16, No. 3, pp ,
40 ( ) = A N ( DC p) + N R I p ( ) + N Q ( ) = AE( N ( DC p) ) E I( p) E I ( i p) ( ) = A K p ( ( ) I ( p ) + E( N ( i DC p) )) ( ) E N ( DC p) 40
41 P&+7/G>LlLK\RR <5ZR C96%1b3#Y 97/m"6*#C"0*3\ \2&* " # X R.RUe R.R<Y R.RRee n 9= R.=> R.R>< R.<\ # # # " " # # # " " # # # # "## " "## 41
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45 T7*%'N%6&%87/ ]&"/"6?&'*"6 ]%m"'"*t36"-37'c&/g P7/#'79%'_Po#$"%/-VOE%$0'" VWJ>Gd A. Buades, B. Coll, and J. M. Morel, A review of image denoising algorithms, with a new one, Multiscale Modeling and Simulation, Vol. 4, No. 2, pp ,
46 46
47 P7/o79%' Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian, Image Denoising by Sparse 3D Transform-domain Collaborative Filtering, IEEE Trans on Image Processing, Vol. 16, No. pp ,
48 G6%g7/hfH#I3%//"'VWJ>G 6 x
49 Ce Liu, William T. Freeman, Richard Szeliski and Sing Bing Kang, Noise Estimation from a Single Image, CVPR,
50 50
51 I = At( P + E( N )) DC k ( ) = At 1 P + E( N ) DC g O 1 g O 2 ( ) ( ) ( ) = At 2 P + E( N ) DC ( ) ( ) = t 1 g O 1 g O 2 t 2 51
52 f ( I) = I " f ( I) = "I # + $ f ( I) = I P I, { " i } f ( I) = w i I i " i=1 ^6&76M^IX ( ),P I, " i ( { }) = " i I i # i=0 52
53 N&g/"p/g K089%'C"/-&*f " 1 % Optical density = log 10 $ ' # Light Transmission& 1 Light Transmission = pow(10, Optical density) 53
54 FGH =R<R << =e STIe(YR HDR " 2 = A 2 tp + A 2 2 t" DC + A 2 " R 2 +" Q 2 I = At( P + E( N DC )) Samuel W. Hasinoff, Fredo Durand and William T. Freeman, Noise-Optimal Capture for High Dynamic Range Photography, CVPR, t 2 P 2 SNR 2 = 2 tp + t" DC +" 2 R + 1 A " 2 2 Q 54
55 Li Zhang, Alok Deshpande and Xin Chen, Denoising vs. Deblurring: HDR Imaging Techniques Using Moving Camera, CVPR,
56 $ %$ $% 56
57 V n = 4kTR"f k( "f( af@c T( R( 57
58 HX] " 2 = A 2 tp + A 2 2 t" DC + A 2 " R 2 X5G i 58
59 Yasuyuki Matsushita and Stephen Lin, Radiometric Calibration from Noise Distributions, CVPR,
60 Noise variance# Imaging process# Output# Response f Noise variance# Observation# Input# Radiometric Calibration# Inverse response g Noise variance# Input Input# Output# Output# Input# Jun Takamatsu, Yasuyuki Matsushita and Katsushi Ikeuchi, Estimating radiometric response functions from image noise variance, ECCV, 2008
61 I?X I?X Jun Takamatsu, Yasuyuki Matsushita, Tsukasa Ogasawara and Katsushi Ikeuchi, Estimating demosaicing algorithms using image noise variance, CVPR,
62 $ %$&'()'*$+'(,-'. %$ 62
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64 IH? ( ) R xx R x 2 = R xy R x R y ( ) = R yy R = f '' f "1 R 2 y f '( f "1 ( R) ) 2 Yu-Feng Hsu and Shih-Fu Chang, Image Splicing Detection using Camera Response Function Consistency and Automatic Segmentation, Int. Conf. on Multimedia Expo., pp , 2007 Zhouchen Lin, Rongrong Wang, Xiaoou Tang and Heung-Yeung Shum, Detecting Doctored Images Using Camera Response Normality and Consistency, CVPR,
65 koj Alin C. Popescu and Hany Farid, Exposing Digital Forgeries in Color Filter Array Interpolated Images, IEEE Transactions on Signal Processing, Vol. 52, No. 10, pp ,
66 G%B2"93&"-1%m"'"* Jan Lukas, Jessica Fridrich and Miroslav Goljan, Digital Camera Identification from Sensor Pattern Noise, IEEE Trans. on Information Forensics and Security, Vol. 1, No. 2, pp , Mo Chen, Jessica Fridrich, Miroslav Goljan and Jan Lukas, Determining Image Origin and Integrity Using Sensor Noise, IEEE Trans. on Information Forensics and Security, Vol. 3, No. 1, pp ,
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69 ˆ " = argmin " $ i ( y i # f ( x i ;")) 2 k 69
70 y = f (x;") + e p( y i x i,") = ( ) 1 & 2#$ exp y % f x i i 2 ( ' 2$ 2 ) + * ( { }) = p { y i} x i p " { x i }, y i & ( { },") = 1, 2#$ exp ( % y % f x i i 2 ( 2$ 2 i ' ( ) ( ) 2 ) + + * "log p ({ y i }{ x i },#) = $ y i " f x i i ( ) ( ) 2 + const 70
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74 o0# # x p = % $ n " i=1 x i p & ( ' x " = max x 1,, x n ( ) x 2 = x x n 2 x 1 = x 0 = x 1 ++ x n R $ 1"# ( x i ) i L1-Lasso, Fixed point algorithm Marching pursuit 74
75 G( minrank( A) + " E 0 s.t. D = A + E o<#6"'%e%87/ min A * + " E 1 s.t. D = A + E John Wright, Arvind Ganesh, Shankar Rao, Yigang Peng, and Yi Ma, Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization, NIPS,
76 o= o0 p( x) = % " % % 2#$ ' & 1 exp + x + µ ( ' * ( * ' & # ) ") & p ( * ) or I7$06"--"C-"/-&/gi k 76
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78 ( Christopher M. Harris and Daniel M. Wolpert, Signal-dependent noise determines motor planning, Nature, Vol. 394, pp ,
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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 ( )
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RAW 4 E-mail: [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
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(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],
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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
(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
(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
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
3 2 2 (1) (2) (3) (4) 4 4 AdaBoost 2. [11] Onishi&Yoda [8] Iwashita&Stoica [5] 4 [3] 3. 3 (1) (2) (3)
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(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
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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)]
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.
(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 [email protected]
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
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T-1 http://adapt.cs.tsukuba.ac.jp/moodle263/course/view.php?id=7 ([email protected]) 29 10 11 1 ( ) (a) (b) (c) (d) SVD Tikhonov 3 (e) 1: ( ) 1 Objective Output s Known system p(s) b =
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
SICE東北支部研究集会資料(2013年)
280 (2013.5.29) 280-4 SURF A Study of SURF Algorithm using Edge Image and Color Information Yoshihiro Sasaki, Syunichi Konno, Yoshitaka Tsunekawa * *Iwate University : SURF (Speeded Up Robust Features)
(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
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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]
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
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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
2 1,384,000 2,000,000 1,296,211 1,793,925 38,000 54,500 27,804 43,187 41,000 60,000 31,776 49,017 8,781 18,663 25,000 35,300 3 4 5 6 1,296,211 1,793,925 27,804 43,187 1,275,648 1,753,306 29,387 43,025
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
デジタルカメラ用ISP：Milbeaut
ISP Milbeaut Image Signal Processor: Milbeaut あらまし MilbeautISP Image Signal Processor 20 Mpixel Milbeaut6 MB91696AM MB91696AM Abstract Milbeaut is an image signal processor (ISP) that realizes a digital
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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
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