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

Size: px
Start display at page:

Download "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"

Transcription

1 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, National Institute of Advanced Industrial Science and Technology (AIST), AIST Tsukuba Central 2, Umezono, Tsukuba-shi, Japan a) [email protected] [9] [11] 2 [12] 2 [6] [9] 1 D Vol. J90 D No. 8 pp c

2 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 FMS [14], [15] (e) (f) (g) (h) 1 Fig. 1 Relation between subtraction processes and combinatorial intensity levels I 1(x) ai 2(x) a =( n I1(x)/n)/( n I2(x)/n) i=1 i=1 I k x n k (0 255)

3 30 2 1(e) 1 1(f) I 1I 2 2 I 1(x) (ai 2(x) 30) (ai 2(x) + 30) I 1I 2 1(g) (I 1,I 2) (I 1,I 2) 1(h) 1(e) 2 I 2 = I 1 C I 2 = CI 1 C C m I 1 I 2 N(C m,σ) I 1 I 2 = C mi 1 N(a, b) a b 2 1 I 2/I 1 I 2/I 1 =0.96 I 2 =0.96I 1 (I 1,I 2) 2 (I 1,I 2) 2 2 Fig. 2 Example of changed area based on combinatorial intensity levels I 1 I 2 N(C m,σ) I 1 1 I 2 =0.6I 1 C 3 I 2 = CI 1 (C >0)

4 2007/8 Vol. J90 D No. 8 3 Fig. 3 Diagram of distribution of various changes I II III (I 1,I 2) i 4 σ =3.0 1 I 1I 2 3 I 2 = CI 1 I 1I 2 (r, θ) r θ r = Fig. 4 Changed Pixels obtained based inference of background clusters. 3. r = I 1I 2 4 I 1I 2 ii L1 D (0, 0) (255, 255) iii (I 1,I 2) Sig(I 1,I 2) Sig(I 1,I 2)=0 if(i 1,I 2) is included in selected clusters. Sig(I 1,I 2)=1 else 1960

5 (I 1(x),I 2(x)) σ L1 D1 σ L1 D1 I 2 = C mi 1 4 Sig(I 1,I 2)= N1 N1 N1 = 300 x y S e = min(e x,e y) n i=1 E x = (e1,x(x) µe 1,x)(e 2,x(x) µ e2,x ) n(max(σ e1,x,σ e2,x )) 2 n i=1 E y = (e1,y(x) µe 1,y )(e 2,y(x) µ e2,y ) n(max(σ e1,y,σ e2,y )) 2 e i,k µ ei,k σ ei,k i k S e 0.0 S e Fig. 5 Discrimination using gradient correlation. 0.1 < S e < S e S e T 1 T FMS σ L1 D (pixels) 30 (deg)

6 2007/8 Vol. J90 D No. 8 6 Fig. 6 Results examples under various conditions I 2/I <I 2/I 1 < 1.4 SIG(I 1,I 2)

7 2 I 2/I Sig(I 1,I 2)=0 1.0 ±3σ 7(e) ±3σ N (e) (f) 7 Fig. 7 Example of failure. (e) (f) 8 1 Fig. 8 Experimental results

8 2007/8 Vol. J90 D No Fig. 9 Experimental results (e) Sig(I 1,I 2) T 1=0 9 9 Intel/Xeon 2.4GHz 100 ms ms Bromiley [16] (I 1,I 2) (I 1,I 2) / 3 3 σ L1 D1 2 N1 S e

9 [1] R.J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, Image change detection algorithms: A systematic survey, IEEE Trans. Image Process., vol.14, no.3, pp , [2] B. Zitová and J. Flusser, Image registration methods: A survey, Image Vis. Comput., vol.21, no.11, pp , [3] K. Toyama, J. Krumm, B. Brumit, and B. Meyers, Wallflower: Principles and practice of background maintenance, Proc. International Conference on Computer Vision, pp , [4] D-II vol.j86-d-ii, no.6, pp , June [5] M. Pic, L. Berthouze, and T. KuritaB, Adaptive background estimation: Computing a pixel-wise learning rate from local confidence and global correlation values, IEICE Trans. Inf. & Syst., vol.e87-d, no.1, pp.50 57, Jan [6] D-II vol.j79-d-ii, no.4, pp , April [7] C. Stauffer and E. Grimson, Adaptive background mixture models for real-time tracking, Proc. of Compurt Vision and Pattern Recognition 99, pp , [8] watershed D-II vol.j84-d-ii, no.12, pp , Dec [9] D-II vol.j84-d-ii, no.10, pp , Oct [10] vol.44, no.sig 5 (CVIMn 6), pp.54 63, [11] D-II vol.j87-d-ii, no.5, pp , May [12] Radial Reach Filter RRF D- II vol.j86-d-ii, no.5, pp , May [13] F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, Multimodality image registration by maximization of mutual information, IEEE Trans. Med. Imaging, vol.16, no.2, pp , [14] FieldServerEn/default.htm [15] T. Fukatsu and M. Hirafuji, Field monitoring using sensor-nodes with a Web server, J. Robotics and Mechatronics, vol.17, no.2, pp , [16] P.A. Bromiley, N.A. Thacker, and P. Courtney, Nonparametric image subtraction using grey level scattergrams, Image Vis. Comput., vol.20, pp , Oxford 2005 IEEE CS 1965

(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,

(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]

More information

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 + 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

More information

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

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.

More information

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

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

More information

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

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

More information

& Vol.5 No (Oct. 2015) TV 1,2,a) , Augmented TV TV AR Augmented Reality 3DCG TV Estimation of TV Screen Position and Ro

& 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

More information

Japan Academy of Health Science J Jpn Health Sci Vol.14 No

Japan Academy of Health Science J Jpn Health Sci Vol.14 No .The Journal of Japan Academy of HealthSciences,47:=Wt! p.32-39 Development of display program that visualizes the process of medical image registration Takeshi Itou1, Hiroyuki Shinoharal, Takeyuki Hashimoto

More information

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

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]

More information

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

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

More information

プラズマ核融合学会誌11月【81‐11】/小特集5

プラズマ核融合学会誌11月【81‐11】/小特集5 Japan Atomic Energy Agency, Ibaraki 311-0193, Japan 1) Kyoto University, Uji 611-0011, Japan 2) National Institute of Advanced Industrial Science and Technology, Tsukuba 305-8569, Japan 3) Central Research

More information

[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 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

More information

(MIRU2008) HOG Histograms of Oriented Gradients (HOG)

(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

More information

Fig. 2 Signal plane divided into cell of DWT Fig. 1 Schematic diagram for the monitoring system

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

More information

,,.,.,,.,.,.,.,,.,..,,,, i

,,.,.,,.,.,.,.,,.,..,,,, 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

More information

main.dvi

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

More information

THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE {s-kasihr, wakamiya,

THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE {s-kasihr, wakamiya, THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. 565-0871 1 5 E-mail: {s-kasihr, wakamiya, murata}@ist.osaka-u.ac.jp PC 70% Design, implementation, and evaluation

More information

Fig. 3 Flow diagram of image processing. Black rectangle in the photo indicates the processing area (128 x 32 pixels).

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

More information

光学

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

More information

Microsoft Excelを用いた分子軌道の描画の実習

Microsoft Excelを用いた分子軌道の描画の実習 J. Comput. Chem. Jpn.,Vol.9, No.4, pp.177 182 (2010) 2010 Society of Computer Chemistry, Japan Microsoft Excel a*, b, c a, 790-8577 2-5 b, 350-0295 1-1 c, 305-8568 1-1-1 *e-mail: [email protected]

More information

Convolutional Neural Network A Graduation Thesis of College of Engineering, Chubu University Investigation of feature extraction by Convolution

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

More information

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 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

More information

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)

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

More information

(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 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

More information

THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.

THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. E-mail: {ytamura,takai,tkato,tm}@vision.kuee.kyoto-u.ac.jp Abstract Current Wave Pattern Analysis for Anomaly

More information

Run-Based Trieから構成される 決定木の枝刈り法

Run-Based Trieから構成される  決定木の枝刈り法 Run-Based Trie 2 2 25 6 Run-Based Trie Simple Search Run-Based Trie Network A Network B Packet Router Packet Filtering Policy Rule Network A, K Network B Network C, D Action Permit Deny Permit Network

More information

Gaze Head Eye (a) deg (b) 45 deg (c) 9 deg 1: - 1(b) - [5], [6] [7] Stahl [8], [9] Fang [1], [11] Itti [12] Itti [13] [7] Fang [1],

Gaze Head Eye (a) deg (b) 45 deg (c) 9 deg 1: - 1(b) - [5], [6] [7] Stahl [8], [9] Fang [1], [11] Itti [12] Itti [13] [7] Fang [1], 1 1 1 Structure from Motion - 1 Ville [1] NAC EMR-9 [2] 1 Osaka University [3], [4] 1 1(a) 1(c) 9 9 9 c 216 Information Processing Society of Japan 1 Gaze Head Eye (a) deg (b) 45 deg (c) 9 deg 1: - 1(b)

More information

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) (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

More information

& 3 3 ' ' (., (Pixel), (Light Intensity) (Random Variable). (Joint Probability). V., V = {,,, V }. i x i x = (x, x,, x V ) T. x i i (State Variable),

& 3 3 ' ' (., (Pixel), (Light Intensity) (Random Variable). (Joint Probability). V., V = {,,, V }. i x i x = (x, x,, x V ) T. x i i (State Variable), .... Deeping and Expansion of Large-Scale Random Fields and Probabilistic Image Processing Kazuyuki Tanaka The mathematical frameworks of probabilistic image processing are formulated by means of Markov

More information

[1] SBS [2] SBS Random Forests[3] Random Forests ii

[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

More information

IPSJ SIG Technical Report Vol.2015-MUS-107 No /5/23 HARK-Binaural Raspberry Pi 2 1,a) ( ) HARK 2 HARK-Binaural A/D Raspberry Pi 2 1.

IPSJ SIG Technical Report Vol.2015-MUS-107 No /5/23 HARK-Binaural Raspberry Pi 2 1,a) ( ) HARK 2 HARK-Binaural A/D Raspberry Pi 2 1. HARK-Binaural Raspberry Pi 2 1,a) 1 1 1 2 3 () HARK 2 HARK-Binaural A/D Raspberry Pi 2 1. [1,2] [2 5] () HARK (Honda Research Institute Japan audition for robots with Kyoto University) *1 GUI ( 1) Python

More information

5 I The Current Situation and Future Prospects of the North Korean Economy presented at the 2014 Korea Dialogue Conference on Strengthenin

5 I The Current Situation and Future Prospects of the North Korean Economy presented at the 2014 Korea Dialogue Conference on Strengthenin 5 I. 3 1 1990 2 The Current Situation and Future Prospects of the North Korean Economy presented at the 2014 Korea Dialogue Conference on Strengthening North Pacific Cooperation organized by the East-West

More information

(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 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

More information

Vol.55 No (Jan. 2014) saccess 6 saccess 7 saccess 2. [3] p.33 * B (A) (B) (C) (D) (E) (F) *1 [3], [4] Web PDF a m

Vol.55 No (Jan. 2014) saccess 6 saccess 7 saccess 2. [3] p.33 * B (A) (B) (C) (D) (E) (F) *1 [3], [4] Web PDF   a m Vol.55 No.1 2 15 (Jan. 2014) 1,a) 2,3,b) 4,3,c) 3,d) 2013 3 18, 2013 10 9 saccess 1 1 saccess saccess Design and Implementation of an Online Tool for Database Education Hiroyuki Nagataki 1,a) Yoshiaki

More information

1(a) (b),(c) - [5], [6] Itti [12] [13] gaze eyeball head 2: [time] [7] Stahl [8], [9] Fang [1], [11] 3 -

1(a) (b),(c) - [5], [6] Itti [12] [13] gaze eyeball head 2: [time] [7] Stahl [8], [9] Fang [1], [11] 3 - Vol216-CVIM-22 No18 216/5/12 1 1 1 Structure from Motion - 1 8% Tobii Pro TX3 NAC EMR ACTUS Eye Tribe Tobii Pro Glass NAC EMR-9 Pupil Headset Ville [1] EMR-9 [2] 1 Osaka University Gaze Head Eye (a) deg

More information

Studies of Foot Form for Footwear Design (Part 9) : Characteristics of the Foot Form of Young and Elder Women Based on their Sizes of Ball Joint Girth

Studies of Foot Form for Footwear Design (Part 9) : Characteristics of the Foot Form of Young and Elder Women Based on their Sizes of Ball Joint Girth Studies of Foot Form for Footwear Design (Part 9) : Characteristics of the Foot Form of Young and Elder Women Based on their Sizes of Ball Joint Girth and Foot Breadth Akiko Yamamoto Fukuoka Women's University,

More information

1., 1 COOKPAD 2, Web.,,,,,,.,, [1]., 5.,, [2].,,.,.,, 5, [3].,,,.,, [4], 33,.,,.,,.. 2.,, 3.., 4., 5., ,. 1.,,., 2.,. 1,,

1., 1 COOKPAD 2, Web.,,,,,,.,, [1]., 5.,, [2].,,.,.,, 5, [3].,,,.,, [4], 33,.,,.,,.. 2.,, 3.., 4., 5., ,. 1.,,., 2.,. 1,, THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.,, 464 8601 470 0393 101 464 8601 E-mail: [email protected], {ide,murase,hirayama}@is.nagoya-u.ac.jp,

More information

10_08.dvi

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

More information

28 Horizontal angle correction using straight line detection in an equirectangular image

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

More information

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 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)]

More information

2 4 2 3 4 3 [12] 2 3 4 5 1 1 [5, 6, 7] [5, 6] [7] 1 [8] 1 1 [9] 1 [10, 11] [10] [11] 1 [13, 14] [13] [14] [13, 14] [10, 11, 13, 14] 1 [12]

2 4 2 3 4 3 [12] 2 3 4 5 1 1 [5, 6, 7] [5, 6] [7] 1 [8] 1 1 [9] 1 [10, 11] [10] [11] 1 [13, 14] [13] [14] [13, 14] [10, 11, 13, 14] 1 [12] Walking Person Recognition by Matching Video Fragments Masashi Nishiyama, Mayumi Yuasa, Tomokazu Wakasugi, Tomoyuki Shibata, Osamu Yamaguchi ( ), Corporate Research and Development Center, TOSHIBA Corporation

More information

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 ( ) 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

More information

untitled

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

More information

75 unit: mm Fig. Structure of model three-phase stacked transformer cores (a) Alternate-lap joint (b) Step-lap joint 3 4)

75 unit: mm Fig. Structure of model three-phase stacked transformer cores (a) Alternate-lap joint (b) Step-lap joint 3 4) 3 * 35 (3), 7 Analysis of Local Magnetic Properties and Acoustic Noise in Three-Phase Stacked Transformer Core Model Masayoshi Ishida Kenichi Sadahiro Seiji Okabe 3.7 T 5 Hz..4 3 Synopsis: Methods of local

More information

IHI Robust Path Planning against Position Error for UGVs in Rough Terrain Yuki DOI, Yonghoon JI, Yusuke TAMURA(University of Tokyo), Yuki IKEDA, Atsus

IHI Robust Path Planning against Position Error for UGVs in Rough Terrain Yuki DOI, Yonghoon JI, Yusuke TAMURA(University of Tokyo), Yuki IKEDA, Atsus IHI Robust Path Planning against Position Error for UGVs in Rough Terrain Yuki DOI, Yonghoon JI, Yusuke TAMURA(University of Tokyo), Yuki IKEDA, Atsushi UMEMURA, Yoshiharu KANESHIMA, Hiroki MURAKAMI(IHI

More information

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 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

More information

2003/9 Vol. J86 D I No. 9 GA GA [8] [10] GA GA GA SGA GA SGA2 SA TS GA C1: C2: C3: 1 C4: C5: 692

2003/9 Vol. J86 D I No. 9 GA GA [8] [10] GA GA GA SGA GA SGA2 SA TS GA C1: C2: C3: 1 C4: C5: 692 Comparisons of Genetic Algorithms for Timetabling Problems Hiroaki UEDA, Daisuke OUCHI, Kenichi TAKAHASHI, and Tetsuhiro MIYAHARA GA GA GA GA GA SGA GA SGA2SA TS 6 SGA2 GA GA SA 1. GA [1] [12] GA Faculty

More information

28 TCG SURF Card recognition using SURF in TCG play video

28 TCG SURF Card recognition using SURF in TCG play video 28 TCG SURF Card recognition using SURF in TCG play video 1170374 2017 3 2 TCG SURF TCG TCG OCG SURF Bof 20 20 30 10 1 SURF Bag of features i Abstract Card recognition using SURF in TCG play video Haruka

More information

IPSJ SIG Technical Report Vol.2016-CE-137 No /12/ e β /α α β β / α A judgment method of difficulty of task for a learner using simple

IPSJ SIG Technical Report Vol.2016-CE-137 No /12/ e β /α α β β / α A judgment method of difficulty of task for a learner using simple 1 2 3 4 5 e β /α α β β / α A judgment method of difficulty of task for a learner using simple electroencephalograph Katsuyuki Umezawa 1 Takashi Ishida 2 Tomohiko Saito 3 Makoto Nakazawa 4 Shigeichi Hirasawa

More information

proc.dvi

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.

More information

untitled

untitled 2010 58 1 39 59 c 2010 20 2009 11 30 2010 6 24 6 25 1 1953 12 2008 III 1. 5, 1961, 1970, 1975, 1982, 1992 12 2008 2008 226 0015 32 40 58 1 2010 III 2., 2009 3 #3.xx #3.1 #3.2 1 1953 2 1958 12 2008 1 2

More information

Study on Throw Accuracy for Baseball Pitching Machine with Roller (Study of Seam of Ball and Roller) Shinobu SAKAI*5, Juhachi ODA, Kengo KAWATA and Yu

Study on Throw Accuracy for Baseball Pitching Machine with Roller (Study of Seam of Ball and Roller) Shinobu SAKAI*5, Juhachi ODA, Kengo KAWATA and Yu Study on Throw Accuracy for Baseball Pitching Machine with Roller (Study of Seam of Ball and Roller) Shinobu SAKAI*5, Juhachi ODA, Kengo KAWATA and Yuichiro KITAGAWA Department of Human and Mechanical

More information