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1 23 Study on character extraction from a picture using a gradient-based feature

2 Google Street View Google Street View SIFT 3 SIFT 3 y SIFT i

3 Abstract Study on character extraction from a picture using a gradient-based feature Kenji OGAWA In image retrieval system, a user has to use keywords to retrieve image from a image database. Every image in the database should be tagged by meta-data such as keywords. If images are not tagged by meta-data, we will not able to retrieve images. For example, images in Google Street View does not have meta-data, and it is difficult to search a special image from Google Street View. In this thesis, we study on character extraction from the image. Characters in Google Street View are usually not taken from directly right in front of them. They are usually taken obliquely. We use SIFT feature, which is robust for rotation of the image, invariant to changes in scale and brightness change, and study for possibility of robustness for three-dimensional rotation. The experiments are performed to extract characters from the image using SIFT, and change the distance to determine similarity among keypoints under the threedimensional image rotation. The images are rotated 80 from -80 around the y axis. The result result shows that characters can be extracted under the spacial rotation between -50 to 30. key words gradient-based feature SIFT character extraction ii

4 Histograms of Oriented Gradients(HOG) Haar-like Scale-Invariant Feature Transform(SIFT) SIFT Difference-of-Gaussian(DoG) SIFT iii

5 27 29 A d= B d= iv

6 3.1 DoG d= d= d= d= d= d= d= d= A.1 d= A.2 d= A.3 d= v

7 A.4 d= A.5 d= A.6 d= A.7 d= A.8 d= A.9 d= A.10 d= A.11 d= A.12 d= A.13 d= A.14 d= A.15 d= A.16 d= A.17 d= A.18 d= A.19 d= A.20 d= A.21 d= A.22 d= A.23 d= A.24 d= A.25 d= A.26 d= A.27 d= A.28 d= A.29 d= vi

8 A.30 d= A.31 d= A.32 d= A.33 d= B.1 d= B.2 d= B.3 d= B.4 d= B.5 d= B.6 d= B.7 d= B.8 d= B.9 d= B.10 d= B.11 d= B.12 d= B.13 d= B.14 d= B.15 d= B.16 d= B.17 d= B.18 d= B.19 d= B.20 d= B.21 d= vii

9 B.22 d= B.23 d= B.24 d= B.25 d= B.26 d= B.27 d= B.28 d= B.29 d= B.30 d= B.31 d= B.32 d= B.33 d= viii

10 d= d= ix

11 1 ( ) Google Street View Panoramio [1] [2] [1] SIFT(Scale- Invariant Feature Transform) y

12 3 Google Street View Google Street View Google Street View 2 3 SIFT 4 SITF 5 2

13 2 HOG Haar-like SIFT 2.1 Histograms of Oriented Gradients(HOG) HOG [3] HOG 2.2 Haar-like Haar-like 2 [5] Haar-like 3

14 2.3 Scale-Invariant Feature Transform(SIFT) 2.3 Scale-Invariant Feature Transform(SIFT) SIFT [2] [4] SIFT y HOG Haar-like SIFT 4

15 3 SIFT SIFT Difference-of-Gaussian(DoG) DoG Difference-of-Gaussian G(x,y,σ) (3.1) I(a,b) L(a,b,σ) (3.2) DoG G(x,y,σ) = 1 2πσ 2 exp ( x2 +y 2 ) 2σ 2 (3.1) 5

16 3.1 L(a,b,σ) = G(x,y,σ) I(a,b) (3.2) DoG D(a,b,σ) DoG D(a,b,σ) = (G(x,y,kσ) G(x,y,σ)) I(a,b) = L(a,b,kσ) L(a,b,σ) (3.3) σ 0 k 3.1 DoG 3.1 DoG 6

17 DoG DoG [3] σ DoG DoG 26 1 [3]

18 DoG DoG DoG H H = [ Dxx D xy D xy D yy ] (3.4) DoG 2 1 D xx =α 2 D yy =β(α > β) Tr(H) Det(H) Tr(H) = D xx +D yy = α+β (3.5) Det(H) = D xx D yy (D xy ) 2 = αβ (3.6) 1 α 2 β γ α = γβ Tr(H) 2 Det(H) = (α+β)2 αβ = (γβ +β)2 γβ 2 = (γ +1)2 γ (3.7) 8

19 3.2 1 α 2 β Tr(H) 2 Det(H) < (γ th +1) 2 (3.8) γ th γ th Tr(H) 2 /Det(H) [6] [6] a=(x,y,σ) T DoG D(a) [3] D(a) = D + DT a a+ 1 2 at 2 D a 2 a (3.9) a 0 [3] D a + 2 D a 2 â = 0 (3.10) â (x,y,σ) T 2 D a 2 â = D a (3.11) 9

20 3.3 â â = x y σ = 2 D x 2 2 D xy 2 D xσ 2 D xy 2 D y 2 2 D yσ 2 D xσ 2 D yσ 2 D σ 2 1 D x D y D σ (3.12) â=(x,y,σ) DoG D(â) = D D T a â (3.13) D DoG â D(â) D(â) 3.3 L(x,y) m(x,y) θ(x,y) 10

21 3.3 m(x,y) = f x (x,y) 2 +f y (x,y) 2 (3.14) θ(x,y) = tan 1 f x(x,y) f y (x,y) (3.15) { fx (x,y) = L(x+1,y) L(x 1,y) f y (x,y) = L(x,y +1) L(x,y 1) (3.16) m(x,y) θ(x,y) h θ = x ω(x,y)δ[θ,θ(x,y)] (3.17) y ω(x,y) = G(x,y,σ)m(x,y) (3.18) h θ 36 ω(x,y) δ θ(x,y) θ 1 [3] ω(x,y) G(x,y,σ) m(x,y) 36 h θ 80%

22

23 = =

24 DoG [4]

25 4 SIFT SIFT SIFT SIFT 0.3 SIFT SIFT 2. SIFT SIFT 15

26 4.1 y 5 2 y

27

28

29 y r r = n correct n all (4.1) n all 4.1 SIFT n correct n all 19

30 5 5.1 (d) d=

31 d= d= n all SIFT A 5.1 (d) 0.3 n all n correct r 21

32 d= n all n correct r(%) (d) d=0.3 (d) d=

33 d= d= n all SIFT B 5.2 (d) 0.2 n all n correct r n all 23

34 d= n all n correct r(%) (d) d= n all n correct SIFT 50 SIFT 0.3 SIFT SIFT 24

35 % n all % SIFT SIFT 0.3 n all 25

36 6 SIFT y SIFT 70 SIFT 3 Google Street View Google Street View 26

37 Free BSD PC LATEX

38 28

39 [1] Y. Kusachi, A. Suzuki, N. Ito, and K. Arakawa, Kanji Recognition in scene images without detection of textelds robust against variation of viewpoint, contrast, andbackground texture, Proc. ICPR2004, [2],,2011. [3] Gradient -SIFT HOG- [4],, SIFT Mean-Shift [5], [6] SIFT, 82%B4%E3%83%AA%E3%82%BA%E3%83%A0 29

40 A d=0.3 A.1 d= A.2 d=

41 A.3 d= A.4 d= A.5 d=

42 A.6 d= A.7 d= A.8 d=

43 A.9 d= A.10 d= A.11 d=

44 A.12 d= A.13 d= A.14 d=

45 A.15 d= A.16 d=0.3-5 A.17 d=

46 A.18 d=0.3 5 A.19 d= A.20 d=

47 A.21 d= A.22 d= A.23 d=

48 A.24 d= A.25 d= A.26 d=

49 A.27 d= A.28 d= A.29 d=

50 A.30 d= A.31 d= A.32 d=

51 A.33 d=

52 B d=0.2 B.1 d= B.2 d=

53 B.3 d= B.4 d= B.5 d=

54 B.6 d= B.7 d= B.8 d=

55 B.9 d= B.10 d= B.11 d=

56 B.12 d= B.13 d= B.14 d=

57 B.15 d= B.16 d=0.2-5 B.17 d=

58 B.18 d=0.2 5 B.19 d= B.20 d=

59 B.21 d= B.22 d= B.23 d=

60 B.24 d= B.25 d= B.26 d=

61 B.27 d= B.28 d= B.29 d=

62 B.30 d= B.31 d= B.32 d=

63 B.33 d=

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