28 TCG SURF Card recognition using SURF in TCG play video

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1 28 TCG SURF Card recognition using SURF in TCG play video

2 TCG SURF TCG TCG OCG SURF Bof SURF Bag of features i

3 Abstract Card recognition using SURF in TCG play video Haruka MOTOZONO Today, TCG s play video has been distributed with the spread of the Internet and video distribution services, and many people have enjoyed the game situation. However, there are a lot of kinds of TCG cards. In addition, texts on tha cad for card names and explanation are small. Therefore it is difficult to understand which cards are used in TCG play video. In this research, we propose card recognition using SURF in video presenting cards for Yu-Gi-Oh! OCG The processing is as follows, 1)extract the card from each frame of the video using contour extraction, 2)identify the card type, 3)create a histogram by Bof, 4)compare the histogram of the registered templates for the specfied card type, 5)use the top 20 template images with high similarity as candidates, 6)compare the distance between the corresponding feature points, 7)choose smallest distance one as a result of card recognition. In the experiments, we selected 10 cards from each card types randomly, and examined whether it is recognized correctly by using this proposed method. In addtion, we evaluated average processing time per frame. key words Trading Card Game SURF Bag of features ii

4 (TCG) SURF Bag-of-features SURF iii

5 iv

6 2.1 Bag-of-features visualwords ( ) ( ) ( ) ( ) ( ) 180 ( ) ( ) ( ) v

7 [sec] vi

8 [1] TCG WIXOSS( ) TCG TCG ( OCG)[2] SURF [3] 1

9 SURF Bag of features TCG SURF 2

10 2 SURF Bag of features 2.1 (TCG) ( TCG) (CCG) (CCG) 2 TCG TCG TCG 3

11 2.2 SURF 2.2 SURF SURF(Speeded Up Robust features) SIFT ( ) Hessian-Laplace Box Hessian-Laplace (2.1) [ Lxx (x, σ) L xy (x, σ) H(x, σ) = L xy (x, σ) L yy (x, σ) ] (2.1) (2.1) x, y, σ 3 SURF (2.2) Box (2.3) det(h) = L xx (X, σ) L yy (x, σ) (L xy (x, σ)) 2 (2.2) det(h) = D xx D yy (D xy 0.9) 2 (2.3) Haar-like Haar-like( 2 2) x, y d x, d y 4 v = ( d x, d y, d x, d y ) [3][4] 4

12 2.3 Bag-of-features 2.3 Bag-of-features Bag-of-features( Bof) 1 Bof 2.1 DB visual words DB visual words DB [5] 2.1 Bag-of-features visual words 2.2 DB 2.2 kmeans 2.3 DB visual words 5

13 2.3 Bag-of-features 2.2 visualwords 2.3 6

14 3, TCG SURF, ( pixel) OCG 1/ pixel 30fps OCG mm 510mm 82cm OCG

15

16 SURF SURF visual words Bof Bof

17 ( ) 3.3( ) 3.3( ) HSV 1/6 1/25 1/3 2/3 3 3pixel HSV 20 HSV HSV ( ) ( ) ( ) 10

18

19 ( ) SURF kmeans visual words 256 visual words visual words 12

20 3.5 SURF 3.5 SURF 3.8 SURF 3.6( ) 3.6( ) 1/7 2/ ( ) ( ) 180 ( ) 13

21 ( ) 3.7( ) 3.7( ) 4 3.7( ) 3.7( ) SURF

22 Ha Hb X i Min(Ha[i], Hb[i]) X = i Ha[i] (3.1) SURF SURF ( ) ( ) ( ) 15

23 4,, python. OpenCV scikit-learn numpy A B C D 1 16

24

25

26 (%)

27 (%)

28 (%) A B C D [sec] A B C D [sec]

29 HSV SURF Bof (4.1) (4.5) (4.1) A (4.2) B (4.3) C (4.4) D (4.5) X(t) + nh(t) + 20K(t) (4.1) X(t) + nk(t) (4.2) X(t) + nt (t) (4.3) Y (t) + mk(t) (4.4) Y (t) + mt (t) (4.5) 22

30 4.4 X(t) Y (t) H(t) 1 K(t) 1 T (t) 1 n m

31 5 TCG SURF 1 24

32 OCG

33 [1] TCG vol. 115 no. 405 pp [2] / /KONAMI [3] H.Bay A.Ess T.Tuytelaars and L.Gool Supeededup robust features(surf) Computer Vision and Image Understanding 110 pp [4] 2 pp [5] 3 pp

SURF,,., 55%,.,., SURF(Speeded Up Robust Features), 4 (,,, ), SURF.,, 84%, 96%, 28%, 32%.,,,. SURF, i

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