IPSJ SIG Technical Report Vol.2016-CVIM-201 No /3/3 Brick Partitioning 1,a) Winner Update Algorithm(WUA) Multilevel Successive Elimination
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1 Brick Partitioning 1,a) Winner Update Algorithm(WUA) Multilevel Successive Elimination Algorithm(MSEA) ( ) WUA MSEA Brick Partitioning MSEA(ABPMSE+ITE) 1. [1] ( ) ( ) ( ) ( 1 Graduate School of Engineering, Utsunomiya University a) mt146530@cc.utsunomiya-u.ac.jp ) (Sequential Similarity Detection Algorithm SSDA)[2] Coarse-to-Fine [3] [4] SSDA[2] θ θ θ Coarse-to-Fine [4] 1
2 (Adaprive Window Skiping Method, AWS)[5] AWS [1] θ AWS SSDA Successive Elimination Algorithm(SEA)[6] θ Multilevel SEA(MSEA)[7] SEA MSEA [8] Winner Update Algorithm(WUA)[9] θ WUA Brick Partitioning Full Search(FS) T M N, I. FS, ( ),,.,,, d(x, y). (x, y) ( x, y ).,, (1) (Sum of Absolute Difference:SAD). SAD : d(x, y) = T (i, j) I(x+i, y+j) (1) MN i=0 j=0, T (i, j) I(i, j), (i, j)., X Y, x, y 0 x X M, 0 y Y N., (X M + 1) (Y N + 1), d(x, y), d(x, y). 2.2 MSEA FS d(x, y) MSEA[7] d(x, y) d(x, y) d(x, y) l ( ) l = 1 M/2 N/2 M/4 N/ Fig. 1 Multilevel partitioning process. 2
3 1 UB l l l L = log 2 N(N < M) 0 l L 1 K l = 4 l UB l θ θ d(x, y) UB l θ d(x, y) d(x, y) θ θ UB l θ d(x, y) d(x, y) [10] l K l = 4 l k T sub(k) W sub(k) l k UBsub l,k (x, y) (2) T p sub(k) W p sub(k) T sub(k) W sub(k) p P UBsub l,k (x, y) P P = T p sub(k) W p sub(k) (2) UB l (x, y) (3) UB l (x, y) = K l k= UBsub l,k (x, y) (3) MSEA a + b a + b (a, b R) (4) P K l k=0 P T p sub(k) P P K l k=0 W p sub(k) T p sub(k) W p sub(k) P T p sub(k) P W p sub(k) T p sub(k) W p sub(k) UB l (x, y) d(x, y) (5) (5) (6) UB 0 (x, y)... UB l (x.y) UB l+1 (x, y)... d(x, y) (6) MSEA 0 L 1 θ L 1 d(x, y) θ [8] MSEA (ABPMSE+ITE) MSEA MSEA [8] 2.3 WUA WUA[9] MSEA 3
4 MSEA ( ) WUA WUA WUA ( 1 ). ( 2 ) l = 0 l,. ( 3 ). ( 4 ), l = l ( 5 ).,,. 3.. ( 6 ), WUA Brick Partitioning WUA MSEA Brick Partitioning [8] Brick Partitioning WUA WUA 3 MSEA 1 1 (x, y) w(x, y) U (7) U = {w(x, y) 0 x X M, 0 y Y N} (7) 1 V 1 V 1 = {w(x, y) x = Sn, y = Sn, n = 1, 2, 3...} (8) S w(0, 0) x y S ( 2(a)) WUA 2 2 2(b) 1 1 w(x 1 min, y1 min ) 3.2 WUA MSEA 2 Fig. 2 Target window at each stage. 4
5 2 V 2 = {w(x, y) x 1 min S x x 1 min + S, ymin 1 S y ymin 1 + S} (9) 1 x S y S 1 WUA w(x 2 min, y2 min ) ( 2(c)) (10) V 3 V 3 = {w(x, y) w(x, y) / V 1, w(x, y) / V 2 } (10) 3 2 w(x 2 min, y2 min ) MSEA 0 3 WUA 2 1 MSEA 2 3 WUA 3 WUA MSEA 3 1 (θ = ) V 1 WUA 2 1 (θ = d(x 1 min, y1 min )) V 2 WUA 3 MSEA (θ = d(x 2 min, y2 min )) V 3 U (x min, y min ) Fig Brick Partitioning WUA Intial Threshold Estimation by the WUA.,,.,,.,.,,.,,,. (Adaptive Block Partitioning ABP) [8] Brick Partitioning,. Gradient Magnitude, (11). G[T (i, j)] = T (i, j) = G x (i, j) 2 + G y (i, j) 2 G x (i, j) + G y (i, j) (11), G[T (i, j)] T (i, j) Gradient. G x (i, j) G y (i, j), (i, j) x y Gradient. G x (i, j) G y (i, j), (12), (13). G x (i, j) = T (i + 1, j) T (i, j) (12) G y (i, j) = T (i, j + 1) T (i, j) (13) y Gradient Magnitude x H y (j) H y (j) = M i=0 G[T (i, j)] (14) H y (j) SH SH = N j=0 H y (j) (15) 5
6 情報処理学会研究報告 y 軸方向に関して 分割レベル l における部分領域を設 定するための u 番目の分割座標値 sply (u) は以下の式 (16) を満す値として求られる sply (u) Hy (j) = j=sply (u) SH 2l (16) ここで sply (0) = 0 sply (2l ) = N 及び 0 u 2l と する 具体的には j = 0(sply (0)) から射影値を積算して いき 積算値が SH/2l を満した座標値を sply (1) として設 定する 続いて j = sply (1) から改めて射影値を積算してい き 再び積算値が SH/2l を満した座標値を sply (2) とする 図 4 これを u = 2l まで繰り返し 分割レベル l に対して総数 2l Fig. 4 Example of template partitioning results 各領域分割法によるテンプレート分割例 by each method. の座標値を獲得する 次に x 軸方向に関する分割座標値を求める なお 適 応的領域分割は 上述した y 軸方向に関する操作を x 軸方 向においても同様に行うことで実現される 一方で Brick Parititioning では x 軸方向の分割座標値は y 軸方向に よりも 画像が複雑な箇所は小さい領域が 一様な箇所は 大きい領域が割り当られていることがわかる 4. 実験 関して求めた分割座標値により設定される各部分領域ごと に計算する すなわち sply (u 1) 及び sply (u) により得ら れる部分領域内における Gradient Magnitude を y 軸方向 については 距離下限値の計算における領域分割は 等 sply (u) Hxu (i) = G[T (i, j)] (17) 得られた射影値 Hxu (i) の総計 SH u は以下の通りである SH u = 分割 (Proposed WUA) 適応的領域分割 (ABPProposed ABPWUA) 及び Brick Partitioning(BrickProposed Brick- j=sply (u) M 較実験を行った 本論文で比較する従来手法は FS[1] ABPMSE+ITE[8] WUA[9] とした 提案手法及び WUA に射影し 射影値 Hxu (i) を得る 提案手法の有効性を確認するため 従来手法との比 Hxu (i) (18) WUA) を適用した なお 距離下限値の算出にはインテ グラルイメージ [10] を用いる 提案手法におけるウィ ンドウ抽出パラメータ S は すべてのテンプレートに お い て S = 8 と し た 実 験 で 用 い た シ ス テ ム 及 び 開 i=0 x 軸方向に関して 分割レベル l sply (u 1) 及び sply (u) 発環境は CPU:Intel(R)Xeon(R)X GHz Mem- により設定される部分領域内における分割座標値 splx (u, v) は以下の式 (19) を満す値として得られる splx (u,v) i=splx (u,v) Hxu (i) = 表 1 実験データ詳細 Table 1 Description of experimental data. SH u 2l (19) ここで splx (u, 0) = 0 splx (u, 2l ) = M 及び 0 v 2l Template Reference Template size size Correct point (x,y) Lenna (231, 244) (96, 128) Lenna とする y 軸方向の分割座標値と同様に 射影値を積算し Object (240, 60) ていき 積算値が SH u /2l を満すごとに座標値を獲得する Object (440, 405) ある分割座標値 sply (u 1) 及び sply (u) により設定されて Bread (100, 150) いる部分領域につき x 軸方向の分割座標値は 2 だけ得ら Bread (320, 360) れるため 分割レベル l においては その総数は 22l とな Face (118, 305) る splx (u, v) 及び sply (u) によって分割された部分領域間 Face (150, 515) Cabriolet (303, 296) l は同程度の Gradient Magnitude を有しており 等分割さ Cabriolet (672, 148) れた領域や 適応的領域分割による領域から計算される距 SUV (516, 385) 離下限値よりも大きな値が期待でき より効率的なウィン SUV (448, 148) ドウの評価が可能となる 分割レベル 1 から 3 それぞれに Road (775, 980) ついて 等分割 適応的領域分割及び Brick Partitioning Road (280, 575) により領域分割を行った例を図 4 に示す 図 4 より Brick Yard (1065, 900) Partitioning による領域分割は 等分割や適応的領域分割 Yard (150, 515) 2016 Information Processing Society of Japan 6
![2 [ms] Table 2 Computation time [ms] and rate of computation time(set FS to 100%).](/docs-images/94/121434614/images/7-0.jpg "Template FS ABPMSE+ITE WUA ABPWUA BrickWUA Proposed ABPProposed BrickProposed Time % Time % Time % Time % Time % Time % Time % Time % Lenna1 4551 100.00 37 0.81 93 2.04 89 1.95 87 1.91 34 0.74 32 0.")
7 2 [ms] Table 2 Computation time [ms] and rate of computation time(set FS to 100%). Template FS ABPMSE+ITE WUA ABPWUA BrickWUA Proposed ABPProposed BrickProposed Time % Time % Time % Time % Time % Time % Time % Time % Lenna Lenna Object Object Bread Bread Face Face Cabriolet Cabriolet SUV SUV Road Road Yard Yard Average Fig. 5 Reference image and corresponding templates. ory:64gb Compiler:g (-O3 ) Lenna Caltech [11] RGB -30dB θ > FS ABPMSE+ITE WUA FS 2 ms 2 Proposed ABPProposed BrickProposed Average BrickProposed FS 0.16% ABPProposed 0.18% ABPMSE+ITE BrickProposed ABPMSE+ITE 57.4% Road2 Yard2 FS 0.04% 0.06% BrickProposed Proposed 89.0% ABPProposed 94.1% WUA BrickWUA WUA 93.9% ABPWUA 97.6% 6 Face2 WUA ms (Calculation) (Sorting) (Others) WUA 6 WUA WUA 1 7
8 S = 2 S = 64 S Fig. 6 The details of the Computation time. 2 WUA S 7 pixel ms Lenna (96, 128) S = S = S = 32 FS WUA Brick Partitioning WUA Brick Partitioning FS 0.05% [1] (1987). [2] D.I.Barnea and H.F.Silverman : A class of algorithm for fast digital image registration, IEEE Trans.Comput.,Vol.C-21, No.2, pp (1972). [3] : Vol.J86, No.9, pp (2003) [4] V.V.Vinod : - - Vol.J81 No.9,pp (1998). [5] : Vol.J88, No.8, pp (2005). [6] W.Li, E.Salari : Successive elimination algorithm for motion estimation, IEEE Trans.ImageProcess.,Vol.4, No.1, pp (1995). [7] X.Q.Gao, C.J.Duanmu, C.R.Zou : A multilevel successive elimination algorithm based on the block sum pyramid, IEEE Trans.ImageProcess.,Vol.9, No.3, pp (2000). [8] :,, Vol.J94, No.5, pp (2011). [9] Yong-Sheng, et al : Fast Block Matching Algorithm Based on the Winner-Update Strategy, IEEE Trans.ImageProcess, Vol.10, No.8, pp (2001). [10] Jik-Han Jung, et al : A Novel Template Matching Scheme for Fast Full-Search Boosted by an Integral Image, IEEE single processing letters, Vol.17, No.1,pp (2010). [11] 7 Fig. 7 Relationship between the window skipping parameters and template size. 8
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1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Speech Visualization System Based on Augmented Reality Yuichiro Nagano 1 and Takashi Yoshino 2 As the spread of the Augmented Reality(AR) technology and service,
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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
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1 1 1 9 36 3 Stereo b the horiontal rotar oveent of the upswing fishee caera Satoru Yoshioto, 1 Kubo Maoru 1 an Muraoto Kenichiro 1 In this paper, the upswing fishee caera that can shoot up once the enith
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(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
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SNS 1 2 2 2 2 2 2 SNS Detection of posted convergences with a photo post type SNS as a target and application to the tourism potential map Shusaku Yano 1 Masahiro Migita 2 Masashi Toda 2 Takayuki Nagai
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