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
|
|
|
- たつぞう わくや
- 4 years ago
- Views:
Transcription
1 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 [16] Wiener state-of-the-art c 2012 Information Processing Society of Japan 1
![GF [9] GF Park [4] 3.](/docs-images/104/162109798/images/2-1.jpg "srgb srgb 4 420-720 [nm] 10 [nm] 31 N L N r N i ( ) E ref (θ) = r j i f L k, r j i,")
2 3 5 MSFA 2 srgb srgb srgb srgb 2. srgb 2 MSFA 1 srgb srgb srgb MSFA 5 4 MSFA 3 5 MSFA [9] MSFA B Cy G Or R Guided Filter GF [9] GF Park [4] 3. srgb srgb [nm] 10 [nm] 31 N L N r N i ( ) E ref (θ) = r j i f L k, r j i, C(θ) 2, (1) k j i C θ r j i j 31 i L k k f N j j 31 N r 31 N L srgb 2 c 2012 Information Processing Society of Japan 2
![[17] を装着したモノクロカメラ 関数を表す により 420-720 [nm] で 10 [nm] 刻みで撮影された 全 32 では 非線形なマルチバンドデモザイキング処 シーンのうち 16](/docs-images/104/162109798/images/3-4.jpg "シーンをセンサ感度最適化に用いた う 理を含むため センサ感度の大域的な最適化を行うことは ち 5 シーンを図 6 に示す 最適化の際考慮する照明光は 困難である そこで 5 バンドのセンサ感度をガウス分布")
3 情報処理学会研究報告 Butterfly 図 6 Flower Stripes SGchart ChinaCloth センサ感度最適化に用いた画像の例 31 バンド画像から srgb 画像に変換したもの 図 4 提案する単板式マルチバンドカメラのセンサ感度最適化の流れ 図 5 図 7 分光最適センサ感度 図 8 srgb 最適センサ感度 ガウス分布を用いたセンサ感度のパラメータ化 ピーク波長間隔を drb と それぞれおく EsRGB (θ) = NL Nr Ni k j ( ( )) 2 ( ) g rji g f Lk, rji, C(θ), (2) i 2 4. 実験結果 実験では 32 シーンに対する 31 バンド画像を真の分光 反射率として用いた これらの 31 バンド画像は Varipec ここで g は分光反射率を srgb 画像の画素値に変換する 液晶チューナブルフィルタ [17] を装着したモノクロカメラ 関数を表す により [nm] で 10 [nm] 刻みで撮影された 全 32 では 非線形なマルチバンドデモザイキング処 シーンのうち 16 シーンをセンサ感度最適化に用いた う 理を含むため センサ感度の大域的な最適化を行うことは ち 5 シーンを図 6 に示す 最適化の際考慮する照明光は 困難である そこで 5 バンドのセンサ感度をガウス分布 理想的な白色光のみとした パラメータの最適化は 5 パ を用いてパラメータ化し 全探索により最適パラメータを ラメータをそれぞれ 5-85 の間で 5 刻みで変化させ 全探 求めることにより センサ感度の最適化を行う 図 5 にセ 索することにより行った ンサ感度のパラメータ化の方法を示す まず G バンドの 図 7 図 8 にそれぞれ およびによる ピーク波長を 550 [nm] に固定し 標準偏差を σg とおく 分光最適センサ感度と srgb 最適センサ感度を示す ここ ついで Cy バンドと Or バンドの標準偏差を σoc と Cy で MSFA を仮定しマルチバンドデモザイキング手法を考 バンドと G バンドおよび Or バンドと G バンドのピーク 慮したセンサ感度最適化手法をと呼び 各画素に 波長間隔を doc と B バンドと R バンドの標準偏差を σrb おいて全てのバンドの画素値情報を得られることを仮定し と B バンドと Cy バンドおよび R バンドと Or バンドの たセンサ感度最適化手法をと呼ぶことにする c 2012 Information Processing Society of Japan 3
4 1 PSNR Butterfly ChinaCloth Color Doll Flower Hand Stripes Origami Pink SGchart Wool srgb PSNR Butterfly ChinaCloth Color Doll Flower Hand Stripes Origami Pink SGchart Wool srgb PSNR srgb sr sg sb Wool 9 PSNR PSNR PSNR PSNR 9 PSNR PSNR PSNR Color 490 [nm] Wool 620 [nm] Color 4.2 srgb srgb 2 srgb3 PSNR 3 srgb PSNR srgb PSNR PSNR PSNR 11 Butterfly SGchart srgb Butterfly SGchart 5. srgb c 2012 Information Processing Society of Japan 4
5 情報処理学会研究報告 Color Wool 図 バンド画像のバンド画像の比較 Color では 490 [nm] に対応するバンド画像 Wool では 620 [nm] に対応するバンド画像である Butterfly SGchart 図 11 srgb 画像の比較 カメラでの分光反射率推定および srgb 画像取得におい て 有効に働くことを確認した [3] 参考文献 [1] [2] S. Han, I. Sato, T. Okabe and Y. Sato: Fast spectral refectance recovery using DLP projector, Proc. of Asian Conf. on Computer Vision (ACCV), vol. 1, pp (2010). C. Cui, H. Yoo and M. Ben-Ezra: Multi-spectral imaging by optimized wide band illumination, Int. Journal of c 2012 Information Processing Society of Japan [4] [5] Computer Vision, vol. 86, no. 2, pp (2010). H. Fukuda, T. Uchiyama, H. Haneishi, M. Yamaguchi and N. Ohyama: Development of a 16-band multispectral image archiving system, Proc. of SPIE, vol. 5667, pp (2005). J. Park, M. Lee, M. D. Grossberg, and S. K. Nayar: Multispectral imaging using multiplexed illumination, Proc. of IEEE Int. Conf. on Computer Vision (ICCV), pp. 1-8 (2007). K. Ohsawa, T. Ajito, H. Fukuda, Y. Komiya, H. Haneishi, M. Yamaguchi and N. Ohyama: Feature article six band HDTV camera system for spectrum-based 5
6 color reproduction, Journal of Imaging Science and Technology, vol. 48, pp (2004). [6] L. Miao and H. Qi: The design and evaluation of a generic method for generating mosaicked multispectral filter arrays, IEEE Trans. on Image Processing, vol. 15, no. 5, pp (2006). [7] L. Miao, H. Qi, R. Ramanath, and W. E. Snyder: Binary treebased generic demosaicking algorithm for multispectral filter arrays, IEEE Trans. on Image Processing, vol. 15, no. 11, pp (2006). [8] Y. Monno, M. Tanaka, and M. Okutomi: Multispectral demosaicking using adaptive kernel upsampling, Proc. of IEEE Int. Conf. on Image Processing (ICIP), pp (2011). [9] Y. Monno, M. Tanaka, and M. Okutomi: Multispectral demosaicking using guided filter, Proc. of SPIE, vol. 8299, (2012). [10] M. J. Vrhel and H. J. Trussell: Optimal color filters in the presence of noise, IEEE Trans. on Image Processing, vol. 4, no. 6, pp (1995). [11] O. Sharma, H. J. Trussell and M. J. Vrhel: Optimal nonnegative color scanning filters, IEEE Trans. on Image Processing, vol. 7, no. 1, pp (1998). [12] H. Haneishi, T. Hasegawa, A. Hosoi, Y. Yokoyama, N. Tsumura and Y. Miyake: System design for accurately estimating the spectral reflectance of art paintings, Applied Optics, vol. 39, no. 35, pp (2000). [13] N. Shimano: Optimization of spectral sensitivities with gaussian distribution functions for a color image acquisition device in the presence of noise, Optical Engineering, vol. 45, no. 1, pp (2006). [14] D. Y. Ng and J. P. Allebach: A subspace matching color filter design methodology for a multispectral imaging system, IEEE Trans. on Image Processing, vol. 15, no. 9, pp (2006). [15] D.Alleysson, and S. Süsstrunk and J. Marguier : Influence of spectral sensitivity functions on color demosaicing, Proc. 11th Color Imag. Conf., pp (2003). [16] M. Parmar and S. J. Reeves: Selection of optimal spectral sensitivity functions for color filter arrays, IEEE Trans. on Image Processing, vol. 19, no. 12, pp (2010). [17] Varispec, c 2012 Information Processing Society of Japan 6
単板撮像素子を用いたマルチバンド画像撮影システム
単板撮像素子を用いた マルチバンド画像撮影システム 紋野雄介 田中正行 奥富正敏 ( 東京工業大学 ) 菊地直 吉崎和徳 福西宗憲 小宮康宏 ( オリンパス技術開発部門 ) 招待ショートサーベイ講演 2016 年 9 月 5 日 ( 月 ) 電子情報通信学会パターン認識 メディア理解 (PRMU) 研究会 Reflectance Spectral power Spectral power マルチバンド画像撮影
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
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,
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
(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
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
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,
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
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
(a) (b) 2 2 (Bosch, IR Illuminator 850 nm, UFLED30-8BD) ( 7[m] 6[m]) 3 (PointGrey Research Inc.Grasshopper2 M/C) Hz (a) (b
![(a) (b) 2 2 (Bosch, IR Illuminator 850 nm, UFLED30-8BD) ( 7[m] 6[m]) 3 (PointGrey Research Inc.Grasshopper2 M/C) Hz (a) (b](/thumbs/91/105641706.jpg "(a) (b) 2 2 (Bosch, IR Illuminator 850 nm, UFLED30-8BD) ( 7[m] 6[m]) 3 (PointGrey Research Inc.Grasshopper2 M/C) Hz (a) (b")
(MIRU202) 202 8 AdrianStoica 89 0395 744 89 0395 744 Jet Propulsion Laboratory 4800 Oak Grove Drive, Pasadena, CA 909, USA E-mail: uchino@irvs.ait.kyushu-u.ac.jp, {yumi,kurazume}@ait.kyushu-u.ac.jp 2 nearest
2.2 6).,.,.,. Yang, 7).,,.,,. 2.3 SIFT SIFT (Scale-Invariant Feature Transform) 8).,. SIFT,,. SIFT, Mean-Shift 9)., SIFT,., SIFT,. 3.,.,,,,,.,,,., 1,
1 1 2,,.,.,,, SIFT.,,. Pitching Motion Analysis Using Image Processing Shinya Kasahara, 1 Issei Fujishiro 1 and Yoshio Ohno 2 At present, analysis of pitching motion from baseball videos is timeconsuming
情報処理学会研究報告 IPSJ SIG Technical Report Vol.2013-CVIM-188 No /9/3 BRDF i
BRDF E-mail: {yoshie,ki}@cvl.iis.u-tokyo.ac.jp, tetsuro.morimoto@toppan.co.jp, imarik@nii.ac.jp, mukaigaw@am.sanken.osaka-u.ac.jp 2 CG 1 BRDF 1. (BRDF) BRDF Lambert Oren-Nayar [1] Phong [2] Blinn [3] Ward
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
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
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
情報処理学会研究報告 方向の組み合わせを変えながら反射光の強度を計測する この方法は単純であるが入射方向と反射方向の組み合わせ を同時に 1 つしか計測できないため すべての方向を計 測するには膨大な計測回数が必要となる問題がある その ため 計測を効率的に行う方法がこれまでに提案されてい る [3
1,a) 1,b) 1,c) 1,d) 1. CG 1 1 630 0192 8916 5 a) kushida.takahiro.kh3@is.naist.jp b) funatomi@is.naist.jp c) hkubo@is.naist.jp d) mukaigawa@is.naist.jp 散乱なし 散乱あり 1 2. c 2017 Information Processing Society
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
& 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
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
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
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,
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
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
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
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
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
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
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
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,
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
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
(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
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) (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
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
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.
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
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
ï\éÜA4*
Feature Article Imaging of minuscule amounts of chemicals, Scannimg Chemical Microscope --- Increasing analysis information through imaging --- Abstract We have developed a Scanning Chemical Microscope
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
IPSJ SIG Technical Report Vol.2013-GN-87 No /3/ Research of a surround-sound field adjustmen system based on loudspeakers arrangement Ak
1 1 3 Research of a surround-sound field adjustmen system based on loudspeakers arrangement Akiyama Daichi 1 Kanai Hideaki 1 Abstract: In this paper, we propose a presentation method that does not depend
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
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
, ( ξ/) ξ(x), ( ξ/) x = x 1,. ξ ξ ( ξ, u) = 0. M LS ξ ξ (6) u,, u M LS 3).,.. ξ x ξ = ξ(x),, 1. J = (ξ ξ, V [ξ ] 1 (ξ ξ )) (7) ( ξ, u) = 0, = 1,..., N
![, ( ξ/) ξ(x), ( ξ/) x = x 1,. ξ ξ ( ξ, u) = 0. M LS ξ ξ (6) u,, u M LS 3).,.. ξ x ξ = ξ(x),, 1. J = (ξ ξ, V [ξ ] 1 (ξ ξ )) (7) ( ξ, u) = 0, = 1,..., N](/thumbs/80/81723780.jpg ", ( ξ/) ξ(x), ( ξ/) x = x 1,. ξ ξ ( ξ, u) = 0. M LS ξ ξ (6) u,, u M LS 3).,.. ξ x ξ = ξ(x),, 1. J = (ξ ξ, V [ξ ] 1 (ξ ξ )) (7) ( ξ, u) = 0, = 1,..., N")
1,,.,.. Maximum Likelihood Estimation for Geometric Fitting Yasuyuki Sugaya 1 Geometric fitting, the problem which estimates a geometric model of a scene from extracted image data, is one of the most fundamental
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
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
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
03-01-”O‰´‘rŁ¶-4.02
VISION Vol. 21, No. 2, 115 128, 2009 174 8580 75 1 1 LMS 2 115 1 n 0 n 1 v 0 v 1 s 0 s 1 2 2.1 1 n 0 sin(q 0 ) n 1 sin(q 1 ) 1.5 2/3 2 2.2 116 2 3 ) ) 8 bit 16 bit 1 n 0 q 0 n 1 q 1 2 2.3 117 3 Sanz &
第６２巻 第１号 平成２４年４月／石こうを用いた木材ペレット
Bulletin of Japan Association for Fire Science and Engineering Vol. 62. No. 1 (2012) Development of Two-Dimensional Simple Simulation Model and Evaluation of Discharge Ability for Water Discharge of Firefighting
CD納品用.indd
1. 1.1 1.2 1.3 1.4 2. 3. 3.1 3.2 3.3 4. 4.1 4.2 6. Journal of the Institute of Nuclear Safety System Journal of the Institute of Nuclear Safety System Journal of the Institute of Nuclear Safety System
本文6(599) (Page 601)
(MIRU2008) 2008 7 525 8577 1 1 1 E-mail: matsuzaki@i.ci.ritsumei.ac.jp, shimada@ci.ritsumei.ac.jp Object Recognition by Observing Grasping Scene from Image Sequence Hironori KASAHARA, Jun MATSUZAKI, Nobutaka
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
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
Web Web [4] Web Web [5] Web 2 Web 3 4 Web Web 2.1 Web Web Web Web Web 2.2 Web Web Web *1 Web * 2*3 Web 3. [6] [7] [8] 4. Web 4.1 Web Web *1 Ama
![Web Web [4] Web Web [5] Web 2 Web 3 4 Web Web 2.1 Web Web Web Web Web 2.2 Web Web Web *1 Web * 2*3 Web 3. [6] [7] [8] 4. Web 4.1 Web Web *1 Ama](/thumbs/84/90061492.jpg "Web Web [4] Web Web [5] Web 2 Web 3 4 Web Web 2.1 Web Web Web Web Web 2.2 Web Web Web *1 Web * 2*3 Web 3. [6] [7] [8] 4. Web 4.1 Web Web *1 Ama")
1 2 2 3 Web Web A product recommender system based on knowledge on situations, functions, and series of products: Implementation and evaluation of the prototype system Abstract: The aim of this study is
2003 : ( ) :80226561 1 1 1.1............................ 1 1.2......................... 1 1.3........................ 1 1.4......................... 4 2 5 2.1......................... 5 2.2........................
動くレントゲン
FPD 1985 1998 2000 2002 FPD X X X FPD X CAD X FPD X CAD X X 1970 Autofluoroscope Toffolo RR, et al. The autofluoroscope and 133-Xe in dynamic studies of pulmonary perfusion and ventilation. Radiology,
自動車ボディ寸法検査
Dimensional Inspection of an Automotive Body Kazunori Higuchi, Osamu Ozeki, Shin Yamamoto Abstract With recent increase of a high class and high quality cars, there is a great demand for more sophisticated
特集_03-07.Q3C
3-7 Error Detection and Authentication in Quantum Key Distribution YAMAMURA Akihiro and ISHIZUKA Hirokazu Detecting errors in a raw key and authenticating a private key are crucial for quantum key distribution
Symbiotic System ト を 動物 る 人間 情報システム 応 ( ) 人間 共生 情報システム と 体の 長 体の と 体 覚 動 体 と とダイナミクスを持つ 人間と情報システムのインタ ース. コン ータに の動作をさ るために, のつ, するコ ン を, される.. イコン イン
7 人間と共生する情報システムの実現を目指して シンビオティック システムの実現に向けて 人, 社会, 環境, 情報システムの協調系 7 人間と共生する情報システムの実現を目指して 松山隆司 * tm@i.kyoto-u.ac.jp 川嶋宏彰 * kawashima@i.kyoto-u.ac.jp 鷲見和彦 *, ** Sumi.Kazuhiko@ds.MitsubishiElectric.co.jp
JAPAN MARKETING JOURNAL 111 Vol.28 No.32008
Vol.28 No.32008 JAPAN MARKETING JOURNAL 111 Vol.28 No.32008 JAPAN MARKETING JOURNAL 111 Vol.28 No.32008 JAPAN MARKETING JOURNAL 111 Vol.28 No.32008 JAPAN MARKETING JOURNAL 111 Vol.28 No.32008 JAPAN MARKETING
JAPAN MARKETING JOURNAL 113 Vol.29 No.12009
JAPAN MARKETING JOURNAL 113 Vol.29 No.12009 JAPAN MARKETING JOURNAL 113 Vol.29 No.12009 JAPAN MARKETING JOURNAL 113 Vol.29 No.12009 JAPAN MARKETING JOURNAL 113 Vol.29 No.12009 Vol.29 No.12009 JAPAN MARKETING
JAPAN MARKETING JOURNAL 110 Vol.28 No.22008
Vol.28 No.22008 JAPAN MARKETING JOURNAL 110 Vol.28 No.22008 JAPAN MARKETING JOURNAL 110 Vol.28 No.22008 JAPAN MARKETING JOURNAL 110 Vol.28 No.22008 JAPAN MARKETING JOURNAL 110 Vol.28 No.22008 JAPAN MARKETING
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)
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 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
1 4 4 [3] SNS 5 SNS , ,000 [2] c 2013 Information Processing Society of Japan
![1 4 4 [3] SNS 5 SNS , ,000 [2] c 2013 Information Processing Society of Japan](/thumbs/93/112672153.jpg "1 4 4 [3] SNS 5 SNS , ,000 [2] c 2013 Information Processing Society of Japan")
SNS 1,a) 2 3 3 2012 3 30, 2012 10 10 SNS SNS Development of Firefighting Knowledge Succession Support SNS in Tokyo Fire Department Koutarou Ohno 1,a) Yuki Ogawa 2 Hirohiko Suwa 3 Toshizumi Ohta 3 Received:
知能と情報, Vol.30, No.5, pp
1, Adobe Illustrator Photoshop [1] [2] [3] Initital Values Assignment of Parameters Using Onomatopoieas for Interactive Design Tool Tsuyoshi NAKAMURA, Yuki SAWAMURA, Masayoshi KANOH, and Koji YAMADA Graduate
HPC pdf
GPU 1 1 2 2 1 1024 3 GPUGraphics Unit1024 3 GPU GPU GPU GPU 1024 3 Tesla S1070-400 1 GPU 2.6 Accelerating Out-of-core Cone Beam Reconstruction Using GPU Yusuke Okitsu, 1 Fumihiko Ino, 1 Taketo Kishi, 2
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
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
36 581/2 2012
4 Development of Optical Ground Station System 4-1 Overview of Optical Ground Station with 1.5 m Diameter KUNIMORI Hiroo, TOYOSHMA Morio, and TAKAYAMA Yoshihisa The OICETS experiment, LEO Satellite-Ground
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
1 UD Fig. 1 Concept of UD tourist information system. 1 ()KDDI UD 7) ) UD c 2010 Information Processing S
UD 1 2 3 4 1 UD UD UD 2008 2009 Development and Evaluation of UD Tourist Information System Using Mobile Phone to Heritage Park HISASHI ICHIKAWA, 1 HIROYUKI FUKUOKA, 2 YASUNORI OSHIDA, 3 TORU KANO 4 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
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
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
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
( ), ( ) Patrol Mobile Robot To Greet Passing People Takemi KIMURA(Univ. of Tsukuba), and Akihisa OHYA(Univ. of Tsukuba) Abstract This research aims a
( ), ( ) Patrol Mobile Robot To Greet Passing People Takemi KIMURA(Univ. of Tsukuba), and Akihisa OHYA(Univ. of Tsukuba) Abstract This research aims at the development of a mobile robot to perform greetings
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)]
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
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:
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
2. Eades 1) Kamada-Kawai 7) Fruchterman 2) 6) ACE 8) HDE 9) Kruskal MDS 13) 11) Kruskal AGI Active Graph Interface 3) Kruskal 5) Kruskal 4) 3. Kruskal
1 2 3 A projection-based method for interactive 3D visualization of complex graphs Masanori Takami, 1 Hiroshi Hosobe 2 and Ken Wakita 3 Proposed is a new interaction technique to manipulate graph layouts
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.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
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. UWB UWB
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. UWB -1 E-mail: seki@aso.cce.i.koto-u.ac.jp UWB SEABED SEABED SEABED,,, SEABED Application of fast imaging
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,
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
[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
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
IPSJ SIG Technical Report Vol.2015-CVIM-195 No /1/23 RGB-D RGB 3 1,a) RGB-D RGB-D 3. RGB CG RGB DTAM[1] MonoFusi
![IPSJ SIG Technical Report Vol.2015-CVIM-195 No /1/23 RGB-D RGB 3 1,a) RGB-D RGB-D 3. RGB CG RGB DTAM[1] MonoFusi](/thumbs/104/162872164.jpg "IPSJ SIG Technical Report Vol.2015-CVIM-195 No /1/23 RGB-D RGB 3 1,a) RGB-D RGB-D 3. RGB CG RGB DTAM[1] MonoFusi")
RGB-D RGB 3 1,a) 1 1 1 1 RGB-D 3. 3. RGB-D 3. RGB 3 3 1. 3 CG 3 3 3 3 RGB DTAM[1] MonoFusion[2] KinectFusion[3] 3 3 3 1 NAIST, Ikoma, Nara 630 0192, Japan a) takehara.hikari.tz9@is.naist.jp 3 3 3 3 [4],
16 June 2004 2 RAW Fig. 2 High resolution color image reconstruction from RAW data. u(i 1,i 2 )= p(i 1 x, i 2 y)i(x, y)dxdy (1) 1 demosaicking Fig. 1
Vol. 45 No. SIG 8(CVIM 9) June 2004 RAW, RAW demosaicking High Resolution Color Image Reconstruction Using Raw Data of a Single Imaging Chip Tomomasa Gotoh, and Masatoshi Okutomi The limited resolution
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.
untitled
(SPLE) 2009/10/23 SRA yosikazu@sra.co.jp First, a Message from My Employers 2 SRA CMMI ] SPICE 3 And Now, Today s Feature Presentation 4 Engineering SPLE SPLE SPLE SPLE SPLE 5 6 SPL Engineering Engineeringi
Vol.-ICS-6 No.3 /3/8 Input.8.6 y.4 Fig....5 receptive field x 3 w x y Machband w(x =
DOG(Difference of two Gaussians 8 A feedback model for the brightness illusion Shoji Nodasaka and Asaki Saito We consider mechanism of the Hermann grid. The mechanism is usually explained by effects of
IPSJ SIG Technical Report Vol.2011-EC-19 No /3/ ,.,., Peg-Scope Viewer,,.,,,,. Utilization of Watching Logs for Support of Multi-
1 3 5 4 1 2 1,.,., Peg-Scope Viewer,,.,,,,. Utilization of Watching Logs for Support of Multi-View Video Contents Kosuke Niwa, 1 Shogo Tokai, 3 Tetsuya Kawamoto, 5 Toshiaki Fujii, 4 Marutani Takafumi,