(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
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1 (MIRU202) AdrianStoica Jet Propulsion Laboratory 4800 Oak Grove Drive, Pasadena, CA 909, USA uchino@irvs.ait.kyushu-u.ac.jp, {yumi,kurazume}@ait.kyushu-u.ac.jp 2 nearest neighbor [] [2] Gait Energy Image(GEI) [2] [3] [4] [5] [6] [7] [5]
![UFLED30-8BD) ( 7[m] 6[m]) 3 (PointGrey Research](/docs-images/91/105641706/images/2-1.jpg "Inc.Grasshopper2 M/C) 600 200 30Hz 28 5 4 (a)")
2 (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) IR light Walking direction Camera Invisible shadow for the subject 2 Captured image IR light 6m 7m Invisible shadow for the subject
![..., I22 } のうちの 6 個を示 す [9] [0] 図4 (µ20 µ02 µ2 ) µ400 I2 = 0 (µ230 µ203 6µ30 µ2 µ2 µ03 + 4µ30 µ32 + 4µ03 µ32 I = 撮影された画像の例 ついて述べる まず輝度値の差に基づく背景差分法により 撮影画像 から実際の人物領域 及び影領域を抽出する 図 5(a) に 実際の画像 図 5(b)](/docs-images/91/105641706/images/3-1.jpg "に背景差分結果を示す 3µ22 µ22 ) I3 = 7 (µ20 (µ2 µ03 µ22 ) µ (µ30 µ03 µ2 µ2 ) +µ02 (µ30 µ2 µ22 )) I4 = (µ320 µ203 6µ220 µ µ2 µ03 6µ220 µ02 µ2 µ03 +9µ220 µ02 µ22 + 2µ20 µ2 µ2 µ03 +6µ20 µ µ02 µ30 µ03 8µ20 µ µ02")
3 ここで座標 (x, y) は対象人物の領域 O を基準とした座標 であり また I(x, y) は画像の輝度値を示す 式 を離散 化すると xp y q I(x, y) (2) と表される 式 2 より重心の位置は次の式で表される xg = µ0 µ0, yg = (3) と表される 式 2 と式 3 より重心周りのモーメントは次 の式で表される 図 3 構築した実験スタジオ (x xg )p (y yg )q I(x, y) (4) またモーメントは形状の特徴を示しており 0 次モー メントは対象物体領域の面積 2 次モーメントは対象の 重心周りの分散 3 次モーメントは対象物体の歪度を示 す 以下に本研究で用いる 22 個の 2 次元アフィンモー メント不変量 I = {I, I2,...., I22 } のうちの 6 個を示 す [9] [0] 図4 (µ20 µ02 µ2 ) µ400 I2 = 0 (µ230 µ203 6µ30 µ2 µ2 µ03 + 4µ30 µ32 + 4µ03 µ32 I = 撮影された画像の例 ついて述べる まず輝度値の差に基づく背景差分法により 撮影画像 から実際の人物領域 及び影領域を抽出する 図 5(a) に 実際の画像 図 5(b) に背景差分結果を示す 3µ22 µ22 ) I3 = 7 (µ20 (µ2 µ03 µ22 ) µ (µ30 µ03 µ2 µ2 ) +µ02 (µ30 µ2 µ22 )) I4 = (µ320 µ203 6µ220 µ µ2 µ03 6µ220 µ02 µ2 µ03 +9µ220 µ02 µ22 + 2µ20 µ2 µ2 µ03 +6µ20 µ µ02 µ30 µ03 8µ20 µ µ02 µ2 µ2 8µ3 µ30 µ03 6µ20 µ202 µ30 µ2 + 9µ20 µ202 µ22 +2µ2 µ02 µ30 µ2 6µ µ202 µ30 µ2 + µ302 µ230 ) I5 = 6 (µ40 µ04 4µ3 µ3 + 3µ222 ) I6 = 9 (µ40 µ04 µ22 + 2µ3 µ22 µ3 µ40 µ23 µ04 µ23 µ322 ) (5) 図 5 (a) データベースにおける歩行画像の例, (b) 抽出された 対象人物領域 3. 2 歩行周期の切り出し 次に抽出した影領域より 歩行特徴として 2 次元ア 前節で求めた 2 次元アフィンモーメント不変量の第 フィンモーメント不変量を抽出する アフィンモーメン 次モーメントである I に注目すると 図 6 に示すよう ト不変量はモーメントベースの記述子であり 一般的な に周期性があり また極大値は両脚支持期であることが アフィン変換に対して不変である 二次元平面上におけ わかる そこで 最初に極大値を示すフレームから 3 つ る (p + q) 次のモーメントは次の式で表される xp y q I(x, y)dxdy. 目に極大値を示すフレームまでを 一歩行周期と定義し て切り出す ()
![9 (4 ) (a) (b) [5] 2 [5] [] 2 28 5 Leave-one-out](/docs-images/91/105641706/images/4-3.jpg "cross validation 9, 2, 4, 8 4 (CCR) [%] The")
4 6 I k-nearest neighbour (knn) 2 knn (4 ) (a) (b) [5] 2 [5] [] Leave-one-out cross validation 9, 2, 4, 8 4 (CCR) [%] The division number CCR[%] [5]
5 2 [5] [%] The division number CCR[%] [] 95 no., pp.67 74, 993. [0] J. Flusser, T. Suk, and B. Zitova, Moments and Moment Invariants in Pattern Recognition, Wiley & Sons Ltd., [],,, Adrian Stoica, vol., no. 430, PRMU20-98, pp. 7-75, (B) [] S. Lee, Y. Liu & R. Collins: Shape Variation-based Frieze Pattern for Robust Gait Recognition, Computer Vision and Pattern Recognition, pp. 8, [2] J. Han & B. Bhanu: Individual Recognition Using Gait Energy Image, IEEE Trans. PAMI, vol. 28, no. 2, pp , [3] J. Acquah, M. Nixon & J. Carter: Automatic gait recognition by symmetry analysis, Pattern Recognition Letters, 24, pp , [4] K. Sugiura, Y. Makihara & Y. Yagi: Gait Identification based on Multi-view Observations using Omnidirectional Camera, Asian Conf. on Computer Vision, Vol., pp , [5] Y. Iwashita & R. Kurazume: Person identification from human walking sequences using affine moment invariants, Proc. IEEE Int. Conf. Robotics and Automation, pp , [6] A. Stoica: Towards Recognition of Humans and their behaviors from Space and Airborne Platforms: Extracting the Information in the Dynamics of Human Shadows, Bio-inspired, Learning and Intelligent Systems for Security, [7] Y. Iwashita, A. Stoica & R. Kurazume: Person Identification using Shadow Analysis, British Machine Vision Conference, pp.35. 0, 200. [8] R. Green, Spherical Harmonic Lighting: The Gritty Details, SCEA Research and Development, [9] J. Flusser, and T. Suk, Pattern recognition by affine moment invariants, Pattern Recognition, vol.26,
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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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
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光学
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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
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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
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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
3 Abstract CAD 3-D ( ) 4 Spin Image Correspondence Grouping 46.1% 17.4% 97.6% ICP [0.6mm/point] 1 CAD [1][2]
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3 E-mail: {akizuki}@isl.sist.chukyo-u.ac.jp Abstract CAD 3-D ( ) 4 Spin Image Correspondence Grouping 46.1% 17.4% 97.6% ICP [0.6mm/point] 1 CAD [1][2] Shape Index [3] [4][5] 3 SHOT [6] [7] Point Pair Feature
本文6(599) (Page 601)
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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]
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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 +](/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
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,,.,.,,.,.,.,.,,.,..,,,, 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
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